,
Russell Lenth [ctb]| Title: | Fast Fixed-Effects Estimations |
|---|---|
| Description: | Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors. |
| Authors: | Laurent Berge [aut, cre],
Sebastian Krantz [ctb],
Grant McDermott [ctb] ,
Russell Lenth [ctb] |
| Maintainer: | Laurent Berge <[email protected]> |
| License: | GPL-3 |
| Version: | 0.12.1 |
| Built: | 2024-06-21 13:28:08 UTC |
| Source: | https://github.com/lrberge/fixest |
Subsets a fixest_multi object using different keys.
## S3 method for class 'fixest_multi' x[i, sample, lhs, rhs, fixef, iv, I, reorder = TRUE, drop = FALSE]## S3 method for class 'fixest_multi' x[i, sample, lhs, rhs, fixef, iv, I, reorder = TRUE, drop = FALSE]
x |
A |
i |
An integer vector. Represents the estimations to extract. |
sample |
An integer vector, a logical scalar, or a character vector. It represents
the |
lhs |
An integer vector, a logical scalar, or a character vector. It represents
the left-hand-sides identifiers for which the results should be extracted. Only valid when
the |
rhs |
An integer vector or a logical scalar. It represents the right-hand-sides
identifiers for which the results should be extracted. Only valid when the |
fixef |
An integer vector or a logical scalar. It represents the fixed-effects
identifiers for which the results should be extracted. Only valid when the |
iv |
An integer vector or a logical scalar. It represent the stages of the IV. Note
that the length can be greater than 2 when there are multiple endogenous regressors (the
first stage corresponding to multiple estimations). Note that the order of the stages depends
on the |
I |
An integer vector. Represents the root element to extract. |
reorder |
Logical, default is |
drop |
Logical, default is |
The order with we we use the keys matter. Every time a key sample, lhs, rhs,
fixef or iv is used, a reordering is performed to consider the leftmost-side key
to be the new root.
Use logical keys to easily reorder. For example, say the object res contains a
multiple estimation with multiple left-hand-sides, right-hand-sides and fixed-effects.
By default the results are ordered as follows: lhs, fixef, rhs.
If you use res[lhs = FALSE], then the new order is: fixef, rhs, lhs.
With res[rhs = TRUE, lhs = FALSE] it becomes: rhs, fixef, lhs. In both cases
you keep all estimations.
It returns a fixest_multi object. If there is only one estimation left in the object, then
the result is simplified into a fixest object only with drop = TRUE.
The main fixest estimation functions: feols, fepois,
fenegbin, feglm, feNmlm. Tools for mutliple fixest
estimations: summary.fixest_multi, print.fixest_multi, as.list.fixest_multi,
sub-sub-.fixest_multi, sub-.fixest_multi.
# Estimation with multiple samples/LHS/RHS aq = airquality[airquality$Month %in% 5:6, ] est_split = feols(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), aq, split = ~ Month) # By default: sample is the root etable(est_split) # Let's reorder, by considering lhs the root etable(est_split[lhs = 1:.N]) # Selecting only one LHS and RHS etable(est_split[lhs = "Ozone", rhs = 1]) # Taking the first root (here sample = 5) etable(est_split[I = 1]) # The first and last estimations etable(est_split[i = c(1, .N)])# Estimation with multiple samples/LHS/RHS aq = airquality[airquality$Month %in% 5:6, ] est_split = feols(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), aq, split = ~ Month) # By default: sample is the root etable(est_split) # Let's reorder, by considering lhs the root etable(est_split[lhs = 1:.N]) # Selecting only one LHS and RHS etable(est_split[lhs = "Ozone", rhs = 1]) # Taking the first root (here sample = 5) etable(est_split[I = 1]) # The first and last estimations etable(est_split[i = c(1, .N)])
fixest_panel
Subselection from a fixest_panel which has been created with the function panel.
Also allows to create lag/lead variables with functions l/f if
the fixest_panel is also a data.table::data.table.
## S3 method for class 'fixest_panel' x[i, j, ...]## S3 method for class 'fixest_panel' x[i, j, ...]
x |
A |
i |
Row subselection. Allows |
j |
Variable selection. Allows |
... |
Other arguments to be passed to |
If the original data was also a data.table, some calls to [.fixest_panel may dissolve
the fixest_panel object and return a regular data.table. This is the case for
subselections with additional arguments. If so, a note is displayed on the console.
It returns a fixest_panel data base, with the attributes allowing to create
lags/leads properly bookkeeped.
Laurent Berge
Alternatively, the function panel changes a data.frame into a panel from which the
functions l and f (creating leads and lags) can be called. Otherwise you can set the
panel 'live' during the estimation using the argument panel.id (see for example in
the function feols).
data(base_did) # Creating a fixest_panel object pdat = panel(base_did, ~id+period) # Subselections of fixest_panel objects bookkeeps the leads/lags engine pdat_small = pdat[!pdat$period %in% c(2, 4), ] a = feols(y~l(x1, 0:1), pdat_small) # we obtain the same results, had we created the lags "on the fly" base_small = base_did[!base_did$period %in% c(2, 4), ] b = feols(y~l(x1, 0:1), base_small, panel.id = ~id+period) etable(a, b) # Using data.table to create new lead/lag variables if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Variable creation pdat_dt[, x_l1 := l(x1)] pdat_dt[, c("x_l1", "x_f1_2") := .(l(x1), f(x1)**2)] # Estimation on a subset of the data # (the lead/lags work appropriately) feols(y~l(x1, 0:1), pdat_dt[!period %in% c(2, 4)]) }data(base_did) # Creating a fixest_panel object pdat = panel(base_did, ~id+period) # Subselections of fixest_panel objects bookkeeps the leads/lags engine pdat_small = pdat[!pdat$period %in% c(2, 4), ] a = feols(y~l(x1, 0:1), pdat_small) # we obtain the same results, had we created the lags "on the fly" base_small = base_did[!base_did$period %in% c(2, 4), ] b = feols(y~l(x1, 0:1), base_small, panel.id = ~id+period) etable(a, b) # Using data.table to create new lead/lag variables if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Variable creation pdat_dt[, x_l1 := l(x1)] pdat_dt[, c("x_l1", "x_f1_2") := .(l(x1), f(x1)**2)] # Estimation on a subset of the data # (the lead/lags work appropriately) feols(y~l(x1, 0:1), pdat_dt[!period %in% c(2, 4)]) }
fixest_multi objectExtracts single elements from multiple fixest estimations.
## S3 method for class 'fixest_multi' x[[i]]## S3 method for class 'fixest_multi' x[[i]]
x |
A |
i |
An integer scalar. The identifier of the estimation to extract. |
A fixest object is returned.
The main fixest estimation functions: feols, fepois,
fenegbin, feglm, feNmlm. Tools for mutliple fixest
estimations: summary.fixest_multi, print.fixest_multi, as.list.fixest_multi,
sub-sub-.fixest_multi, sub-.fixest_multi.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # The first estimation res[[1]] # The second one, etc res[[2]]base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # The first estimation res[[1]] # The second one, etc res[[2]]
Simple tool that aggregates the value of CATT coefficients in staggered difference-in-difference setups (see details).
## S3 method for class 'fixest' aggregate(x, agg, full = FALSE, use_weights = TRUE, ...)## S3 method for class 'fixest' aggregate(x, agg, full = FALSE, use_weights = TRUE, ...)
x |
A |
agg |
A character scalar describing the variable names to be aggregated,
it is pattern-based. For |
full |
Logical scalar, defaults to |
use_weights |
Logical, default is |
... |
Arguments to be passed to |
This is a function helping to replicate the estimator from Sun and Abraham (2021).
You first need to perform an estimation with cohort and relative periods dummies
(typically using the function i), this leads to estimators of the cohort
average treatment effect on the treated (CATT). Then you can use this function to
retrieve the average treatment effect on each relative period, or for any other way
you wish to aggregate the CATT.
Note that contrary to the SA article, here the cohort share in the sample is considered to be a perfect measure for the cohort share in the population.
It returns a matrix representing a table of coefficients.
Laurent Berge
Liyang Sun and Sarah Abraham, 2021, "Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects". Journal of Econometrics.
# # DiD example # data(base_stagg) # 2 kind of estimations: # - regular TWFE model # - estimation with cohort x time_to_treatment interactions, later aggregated # Note: the never treated have a time_to_treatment equal to -1000 # Now we perform the estimation res_twfe = feols(y ~ x1 + i(time_to_treatment, treated, ref = c(-1, -1000)) | id + year, base_stagg) # we use the "i." prefix to force year_treated to be considered as a factor res_cohort = feols(y ~ x1 + i(time_to_treatment, i.year_treated, ref = c(-1, -1000)) | id + year, base_stagg) # Displaying the results iplot(res_twfe, ylim = c(-6, 8)) att_true = tapply(base_stagg$treatment_effect_true, base_stagg$time_to_treatment, mean)[-1] points(-9:8 + 0.15, att_true, pch = 15, col = 2) # The aggregate effect for each period agg_coef = aggregate(res_cohort, "(ti.*nt)::(-?[[:digit:]]+)") x = c(-9:-2, 0:8) + .35 points(x, agg_coef[, 1], pch = 17, col = 4) ci_low = agg_coef[, 1] - 1.96 * agg_coef[, 2] ci_up = agg_coef[, 1] + 1.96 * agg_coef[, 2] segments(x0 = x, y0 = ci_low, x1 = x, y1 = ci_up, col = 4) legend("topleft", col = c(1, 2, 4), pch = c(20, 15, 17), legend = c("TWFE", "True", "Sun & Abraham")) # The ATT aggregate(res_cohort, c("ATT" = "treatment::[^-]")) with(base_stagg, mean(treatment_effect_true[time_to_treatment >= 0])) # The total effect for each cohort aggregate(res_cohort, c("cohort" = "::[^-].*year_treated::([[:digit:]]+)"))# # DiD example # data(base_stagg) # 2 kind of estimations: # - regular TWFE model # - estimation with cohort x time_to_treatment interactions, later aggregated # Note: the never treated have a time_to_treatment equal to -1000 # Now we perform the estimation res_twfe = feols(y ~ x1 + i(time_to_treatment, treated, ref = c(-1, -1000)) | id + year, base_stagg) # we use the "i." prefix to force year_treated to be considered as a factor res_cohort = feols(y ~ x1 + i(time_to_treatment, i.year_treated, ref = c(-1, -1000)) | id + year, base_stagg) # Displaying the results iplot(res_twfe, ylim = c(-6, 8)) att_true = tapply(base_stagg$treatment_effect_true, base_stagg$time_to_treatment, mean)[-1] points(-9:8 + 0.15, att_true, pch = 15, col = 2) # The aggregate effect for each period agg_coef = aggregate(res_cohort, "(ti.*nt)::(-?[[:digit:]]+)") x = c(-9:-2, 0:8) + .35 points(x, agg_coef[, 1], pch = 17, col = 4) ci_low = agg_coef[, 1] - 1.96 * agg_coef[, 2] ci_up = agg_coef[, 1] + 1.96 * agg_coef[, 2] segments(x0 = x, y0 = ci_low, x1 = x, y1 = ci_up, col = 4) legend("topleft", col = c(1, 2, 4), pch = c(20, 15, 17), legend = c("TWFE", "True", "Sun & Abraham")) # The ATT aggregate(res_cohort, c("ATT" = "treatment::[^-]")) with(base_stagg, mean(treatment_effect_true[time_to_treatment >= 0])) # The total effect for each cohort aggregate(res_cohort, c("cohort" = "::[^-].*year_treated::([[:digit:]]+)"))
This function computes the AIC (Aikake's, an information criterion) from a fixest estimation.
## S3 method for class 'fixest' AIC(object, ..., k = 2)## S3 method for class 'fixest' AIC(object, ..., k = 2)
object |
A |
... |
Optionally, more fitted objects. |
k |
A numeric, the penalty per parameter to be used; the default k = 2 is the
classical AIC (i.e. |
The AIC is computed as:
with k the penalty parameter.
You can have more information on this criterion on AIC.
It return a numeric vector, with length the same as the number of objects taken as arguments.
Laurent Berge
See also the main estimation functions femlm, feols or feglm.
Other statictics methods: BIC.fixest, logLik.fixest, nobs.fixest.
# two fitted models with different expl. variables: res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris) AIC(res1, res2) BIC(res1, res2)# two fitted models with different expl. variables: res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris) AIC(res1, res2) BIC(res1, res2)
Transforms a single character string containing a dictionary in a textual format into a proper dictionary, that is a named character vector
as.dict(x)as.dict(x)
x |
A character scalar of the form |
This function is mostly used in combination with setFixest_dict to set the dictionary to be
used in the function etable.
It returns a named character vector.
Laurent Berge
x = "# Main vars mpg: Miles per gallon hp: Horsepower # Categorical variables cyl: Number of cylinders; vs: Engine" as.dict(x)x = "# Main vars mpg: Miles per gallon hp: Horsepower # Categorical variables cyl: Number of cylinders; vs: Engine" as.dict(x)
Extracts the results from a fixest_multi object and place them into a list.
## S3 method for class 'fixest_multi' as.list(x, ...)## S3 method for class 'fixest_multi' as.list(x, ...)
x |
A |
... |
Not currently used. |
Returns a list containing all the results of the multiple estimations.
The main fixest estimation functions: feols, fepois,
fenegbin, feglm, feNmlm. Tools for mutliple fixest
estimations: summary.fixest_multi, print.fixest_multi, as.list.fixest_multi,
sub-sub-.fixest_multi, sub-.fixest_multi.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # All the results at once as.list(res)base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # All the results at once as.list(res)
This data has been generated to illustrate the use of difference in difference functions in
package fixest. This is a balanced panel of 104 individuals and 10 periods.
About half the individuals are treated, the treatment having a positive effect on
the dependent variable y after the 5th period. The effect of the treatment on y is gradual.
data(base_did)data(base_did)
base_did is a data frame with 1,040 observations and 6 variables named
y, x1, id, period, post and treat.
The dependent variable affected by the treatment.
An explanatory variable.
Identifier of the individual.
From 1 to 10
Indicator taking value 1 if the period is strictly greater than 5, 0 otherwise.
Indicator taking value 1 if the individual is treated, 0 otherwise.
This data has been generated from R.
This data has been generated to illustrate the Sun and Abraham (Journal of Econometrics, 2021) method for staggered difference-in-difference. This is a balanced panel of 95 individuals and 10 periods. Half the individuals are treated. For those treated, the treatment date can vary from the second to the last period. The effect of the treatment depends on the time since the treatment: it is first negative and then increasing.
data(base_stagg)data(base_stagg)
base_stagg is a data frame with 950 observations and 7 variables:
id: panel identifier.
year: from 1 to 10.
year_treated: the period at which the individual is treated.
time_to_treatment: different between the year and the treatment year.
treated: indicator taking value 1 if the individual is treated, 0 otherwise.
treatment_effect_true: true effect of the treatment.
x1: explanatory variable, correlated with the period.
y: the dependent variable affected by the treatment.
This data has been generated from R.
This function computes the BIC (Bayesian information criterion) from a fixest estimation.
## S3 method for class 'fixest' BIC(object, ...)## S3 method for class 'fixest' BIC(object, ...)
object |
A |
... |
Optionally, more fitted objects. |
The BIC is computed as follows:
with k the penalty parameter.
You can have more information on this criterion on AIC.
It return a numeric vector, with length the same as the number of objects taken as arguments.
Laurent Berge
See also the main estimation functions femlm, feols or feglm. Other statistics functions: AIC.fixest, logLik.fixest.
# two fitted models with different expl. variables: res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris) AIC(res1, res2) BIC(res1, res2)# two fitted models with different expl. variables: res1 = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) res2 = femlm(Sepal.Length ~ Petal.Width | Species, iris) AIC(res1, res2) BIC(res1, res2)
Tool to easily group the values of a given variable.
bin(x, bin)bin(x, bin)
x |
A vector whose values have to be grouped. Can be of any type but must be atomic. |
bin |
A list of values to be grouped, a vector, a formula, or the special
values |
It returns a vector of the same length as x.
Numeric vectors can be cut easily into: a) equal parts, b) user-specified bins.
Use "cut::n" to cut the vector into n (roughly) equal parts. Percentiles are
used to partition the data, hence some data distributions can lead to create less
than n parts (for example if P0 is the same as P50).
The user can specify custom bins with the following syntax: "cut::a]b]c]". Here
the numbers a, b, c, etc, are a sequence of increasing numbers, each followed
by an open or closed square bracket. The numbers can be specified as either
plain numbers (e.g. "cut::5]12[32["), quartiles (e.g. "cut::q1]q3["),
or percentiles (e.g. "cut::p10]p15]p90]"). Values of different types can be mixed:
"cut::5]q2[p80[" is valid provided the median (q2) is indeed greater
than 5, otherwise an error is thrown.
The square bracket right of each number tells whether the numbers should be included
or excluded from the current bin. For example, say x ranges from 0 to 100,
then "cut::5]" will create two bins: one from 0 to 5 and a second from 6 to 100.
With "cut::5[" the bins would have been 0-4 and 5-100.
A factor is always returned. The labels always report the min and max values in each bin.
To have user-specified bin labels, just add them in the character vector
following 'cut::values'. You don't need to provide all of them, and NA values
fall back to the default label. For example, bin = c("cut::4", "Q1", NA, "Q3")
will modify only the first and third label that will be displayed as "Q1" and "Q3".
bin vs ref
The functions bin and ref are able to do the same thing, then why use one
instead of the other? Here are the differences:
ref always returns a factor. This is in contrast with bin which returns,
when possible, a vector of the same type as the vector in input.
ref always places the values modified in the first place of the factor levels.
On the other hand, bin tries to not modify the ordering of the levels. It is possible
to make bin mimic the behavior of ref by adding an "@" as the first element of
the list in the argument bin.
when a vector (and not a list) is given in input, ref will place each element of
the vector in the first place of the factor levels. The behavior of bin is
totally different, bin will transform all the values in the vector into a single
value in x (i.e. it's binning).
Laurent Berge
To re-factor variables: ref.
data(airquality) month_num = airquality$Month table(month_num) # Grouping the first two values table(bin(month_num, 5:6)) # ... plus changing the name to '10' table(bin(month_num, list("10" = 5:6))) # ... and grouping 7 to 9 table(bin(month_num, list("g1" = 5:6, "g2" = 7:9))) # Grouping every two months table(bin(month_num, "bin::2")) # ... every 2 consecutive elements table(bin(month_num, "!bin::2")) # ... idem starting from the last one table(bin(month_num, "!!bin::2")) # Using .() for list(): table(bin(month_num, .("g1" = 5:6))) # # with non numeric data # month_lab = c("may", "june", "july", "august", "september") month_fact = factor(month_num, labels = month_lab) # Grouping the first two elements table(bin(month_fact, c("may", "jun"))) # ... using regex table(bin(month_fact, "@may|jun")) # ...changing the name table(bin(month_fact, list("spring" = "@may|jun"))) # Grouping every 2 consecutive months table(bin(month_fact, "!bin::2")) # ...idem but starting from the last table(bin(month_fact, "!!bin::2")) # Relocating the months using "@d" in the name table(bin(month_fact, .("@5" = "may", "@1 summer" = "@aug|jul"))) # Putting "@" as first item means subsequent items will be placed first table(bin(month_fact, .("@", "aug", "july"))) # # "Cutting" numeric data # data(iris) plen = iris$Petal.Length # 3 parts of (roughly) equal size table(bin(plen, "cut::3")) # Three custom bins table(bin(plen, "cut::2]5]")) # .. same, excluding 5 in the 2nd bin table(bin(plen, "cut::2]5[")) # Using quartiles table(bin(plen, "cut::q1]q2]q3]")) # Using percentiles table(bin(plen, "cut::p20]p50]p70]p90]")) # Mixing all table(bin(plen, "cut::2[q2]p90]")) # NOTA: # -> the labels always contain the min/max values in each bin # Custom labels can be provided, just give them in the char. vector # NA values lead to the default label table(bin(plen, c("cut::2[q2]p90]", "<2", "]2; Q2]", NA, ">90%"))) # # With a formula # data(iris) plen = iris$Petal.Length # We need to use "x" table(bin(plen, list("< 2" = ~x < 2, ">= 2" = ~x >= 2)))data(airquality) month_num = airquality$Month table(month_num) # Grouping the first two values table(bin(month_num, 5:6)) # ... plus changing the name to '10' table(bin(month_num, list("10" = 5:6))) # ... and grouping 7 to 9 table(bin(month_num, list("g1" = 5:6, "g2" = 7:9))) # Grouping every two months table(bin(month_num, "bin::2")) # ... every 2 consecutive elements table(bin(month_num, "!bin::2")) # ... idem starting from the last one table(bin(month_num, "!!bin::2")) # Using .() for list(): table(bin(month_num, .("g1" = 5:6))) # # with non numeric data # month_lab = c("may", "june", "july", "august", "september") month_fact = factor(month_num, labels = month_lab) # Grouping the first two elements table(bin(month_fact, c("may", "jun"))) # ... using regex table(bin(month_fact, "@may|jun")) # ...changing the name table(bin(month_fact, list("spring" = "@may|jun"))) # Grouping every 2 consecutive months table(bin(month_fact, "!bin::2")) # ...idem but starting from the last table(bin(month_fact, "!!bin::2")) # Relocating the months using "@d" in the name table(bin(month_fact, .("@5" = "may", "@1 summer" = "@aug|jul"))) # Putting "@" as first item means subsequent items will be placed first table(bin(month_fact, .("@", "aug", "july"))) # # "Cutting" numeric data # data(iris) plen = iris$Petal.Length # 3 parts of (roughly) equal size table(bin(plen, "cut::3")) # Three custom bins table(bin(plen, "cut::2]5]")) # .. same, excluding 5 in the 2nd bin table(bin(plen, "cut::2]5[")) # Using quartiles table(bin(plen, "cut::q1]q2]q3]")) # Using percentiles table(bin(plen, "cut::p20]p50]p70]p90]")) # Mixing all table(bin(plen, "cut::2[q2]p90]")) # NOTA: # -> the labels always contain the min/max values in each bin # Custom labels can be provided, just give them in the char. vector # NA values lead to the default label table(bin(plen, c("cut::2[q2]p90]", "<2", "]2; Q2]", NA, ">90%"))) # # With a formula # data(iris) plen = iris$Petal.Length # We need to use "x" table(bin(plen, list("< 2" = ~x < 2, ">= 2" = ~x >= 2)))
Extracts the bread matrix from fixest objects to be used to compute sandwich variance-covariance matrices.
## S3 method for class 'fixest' bread(x, ...)## S3 method for class 'fixest' bread(x, ...)
x |
A |
... |
Not currently used. |
Returns a matrix of the same dimension as the number of variables used in the estimation.
est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) bread(est)est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) bread(est)
feols estimationChecks the convergence of a feols estimation by computing the first-order conditions of all fixed-effects (all should be close to 0)
check_conv_feols(x) ## S3 method for class 'fixest_check_conv' summary(object, type = "short", ...)check_conv_feols(x) ## S3 method for class 'fixest_check_conv' summary(object, type = "short", ...)
x |
A |
object |
An object returned by |
type |
Either "short" (default) or "detail". If "short", only the maximum absolute FOC are displayed, otherwise the 2 smallest and the 2 largest FOC are reported for each fixed-effect and each variable. |
... |
Not currently used. Note that this function first re-demeans the variables, thus possibly incurring some extra computation time. |
It returns a list of N elements, N being the number of variables in the estimation
(dependent variable + explanatory variables +, if IV, endogenous variables and instruments). For
each variable, all the first-order conditions for each fixed-effect are returned.
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) base$FE = rep(1:30, 5) # one estimation with fixed-effects + varying slopes est = feols(y ~ x1 | species[x2] + FE[x3], base) # Checking the convergence conv = check_conv_feols(est) # We can check that al values are close to 0 summary(conv) summary(conv, "detail")base = setNames(iris, c("y", "x1", "x2", "x3", "species")) base$FE = rep(1:30, 5) # one estimation with fixed-effects + varying slopes est = feols(y ~ x1 | species[x2] + FE[x3], base) # Checking the convergence conv = check_conv_feols(est) # We can check that al values are close to 0 summary(conv) summary(conv, "detail")
fixest estimationThis function extracts the coefficients obtained from a model estimated with
femlm, feols or feglm.
## S3 method for class 'fixest' coef(object, keep, drop, order, collin = FALSE, agg = TRUE, ...) ## S3 method for class 'fixest' coefficients(object, keep, drop, order, collin = FALSE, agg = TRUE, ...)## S3 method for class 'fixest' coef(object, keep, drop, order, collin = FALSE, agg = TRUE, ...) ## S3 method for class 'fixest' coefficients(object, keep, drop, order, collin = FALSE, agg = TRUE, ...)
object |
A |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
collin |
Logical, default is |
agg |
Logical scalar, default is |
... |
Not currently used. |
The coefficients are the ones that have been found to maximize the log-likelihood of the specified model. More information can be found on the models from the estimations help pages: femlm, feols or feglm.
Note that if the model has been estimated with fixed-effects, to obtain the fixed-effect coefficients, you need to use the function fixef.fixest.
This function returns a named numeric vector.
Laurent Berge
See also the main estimation functions femlm, feols or feglm. summary.fixest, confint.fixest, vcov.fixest, etable, fixef.fixest.
# simple estimation on iris data, using "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # the coefficients of the variables: coef(res) # the fixed-effects coefficients: fixef(res)# simple estimation on iris data, using "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # the coefficients of the variables: coef(res) # the fixed-effects coefficients: fixef(res)
Utility to extract the coefficients of multiple estimations and rearrange them into a matrix.
## S3 method for class 'fixest_multi' coef( object, keep, drop, order, collin = FALSE, long = FALSE, na.rm = TRUE, ... ) ## S3 method for class 'fixest_multi' coefficients( object, keep, drop, order, collin = FALSE, long = FALSE, na.rm = TRUE, ... )## S3 method for class 'fixest_multi' coef( object, keep, drop, order, collin = FALSE, long = FALSE, na.rm = TRUE, ... ) ## S3 method for class 'fixest_multi' coefficients( object, keep, drop, order, collin = FALSE, long = FALSE, na.rm = TRUE, ... )
object |
A |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
collin |
Logical, default is |
long |
Logical, default is |
na.rm |
Logical, default is |
... |
Not currently used. |
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # A multiple estimation est = feols(y ~ x1 + csw0(x2, x3), base) # Getting all the coefficients at once, # each row is a model coef(est) # Example of keep/drop/order coef(est, keep = "Int|x1", order = "x1") # To change the order of the model, use fixest_multi # extraction tools: coef(est[rhs = .N:1]) # collin + long + na.rm base$x1_bis = base$x1 # => collinear est = feols(y ~ x1_bis + csw0(x1, x2, x3), base, split = ~species) # does not display x1 since it is always collinear coef(est) # now it does coef(est, collin = TRUE) # long coef(est, long = TRUE) # long but balanced (with NAs then) coef(est, long = TRUE, na.rm = FALSE)base = iris names(base) = c("y", "x1", "x2", "x3", "species") # A multiple estimation est = feols(y ~ x1 + csw0(x2, x3), base) # Getting all the coefficients at once, # each row is a model coef(est) # Example of keep/drop/order coef(est, keep = "Int|x1", order = "x1") # To change the order of the model, use fixest_multi # extraction tools: coef(est[rhs = .N:1]) # collin + long + na.rm base$x1_bis = base$x1 # => collinear est = feols(y ~ x1_bis + csw0(x1, x2, x3), base, split = ~species) # does not display x1 since it is always collinear coef(est) # now it does coef(est, collin = TRUE) # long coef(est, long = TRUE) # long but balanced (with NAs then) coef(est, long = TRUE, na.rm = FALSE)
This function plots the results of estimations (coefficients and confidence intervals).
The function iplot restricts the output to variables created with i, either
interactions with factors or raw factors.
coefplot( object, ..., style = NULL, sd, ci_low, ci_high, df.t = NULL, x, x.shift = 0, horiz = FALSE, dict = getFixest_dict(), keep, drop, order, ci.width = "1%", ci_level = 0.95, add = FALSE, pt.pch = c(20, 17, 15, 21, 24, 22), pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref = "auto", ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, only.params = FALSE, sep, as.multiple = FALSE, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL ) iplot( object, ..., i.select = 1, style = NULL, sd, ci_low, ci_high, df.t = NULL, x, x.shift = 0, horiz = FALSE, dict = getFixest_dict(), keep, drop, order, ci.width = "1%", ci_level = 0.95, add = FALSE, pt.pch = c(20, 17, 15, 21, 24, 22), pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref = "auto", ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, only.params = FALSE, sep, as.multiple = FALSE, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL )coefplot( object, ..., style = NULL, sd, ci_low, ci_high, df.t = NULL, x, x.shift = 0, horiz = FALSE, dict = getFixest_dict(), keep, drop, order, ci.width = "1%", ci_level = 0.95, add = FALSE, pt.pch = c(20, 17, 15, 21, 24, 22), pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref = "auto", ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, only.params = FALSE, sep, as.multiple = FALSE, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL ) iplot( object, ..., i.select = 1, style = NULL, sd, ci_low, ci_high, df.t = NULL, x, x.shift = 0, horiz = FALSE, dict = getFixest_dict(), keep, drop, order, ci.width = "1%", ci_level = 0.95, add = FALSE, pt.pch = c(20, 17, 15, 21, 24, 22), pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref = "auto", ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, only.params = FALSE, sep, as.multiple = FALSE, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL )
object |
Can be either: i) an estimation object (obtained for example from
|
... |
Other arguments to be passed to |
style |
A character scalar giving the style of the plot to be used. You
can set styles with the function |
sd |
The standard errors of the estimates. It may be missing. |
ci_low |
If |
ci_high |
If |
df.t |
Integer scalar or |
x |
The value of the x-axis. If missing, the names of the argument |
x.shift |
Shifts the confidence intervals bars to the left or right, depending
on the value of |
horiz |
A logical scalar, default is |
dict |
A named character vector or a logical scalar. It changes the original variable names
to the ones contained in the |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
ci.width |
The width of the extremities of the confidence intervals. Default is |
ci_level |
Scalar between 0 and 1: the level of the CI. By default it is equal to 0.95. |
add |
Default is |
pt.pch |
The patch of the coefficient estimates. Default is 1 (circle). |
pt.bg |
The background color of the point estimate (when the |
cex |
Numeric, default is 1. Expansion factor for the points |
pt.cex |
The size of the coefficient estimates. Default is the other argument |
col |
The color of the points and the confidence intervals. Default is 1
("black"). Note that you can set the colors separately for each of them with |
pt.col |
The color of the coefficient estimates. Default is equal to the other argument |
ci.col |
The color of the confidence intervals. Default is equal to the other argument |
lwd |
General line with. Default is 1. |
pt.lwd |
The line width of the coefficient estimates. Default is equal to
the other argument |
ci.lwd |
The line width of the confidence intervals. Default is equal to
the other argument |
ci.lty |
The line type of the confidence intervals. Default is 1. |
grid |
Logical, default is |
grid.par |
List. Parameters of the grid. The default values are: |
zero |
Logical, default is |
zero.par |
List. Parameters of the zero-line. The default values are
|
pt.join |
Logical, default is |
pt.join.par |
List. Parameters of the line joining the coefficients. The
default values are: |
ci.join |
Logical default to |
ci.join.par |
A list of parameters to be passed to |
ci.fill |
Logical default to |
ci.fill.par |
A list of parameters to be passed to |
ref |
Used to add points equal to 0 (typically to visualize reference points).
Either: i) "auto" (default), ii) a character vector of length 1, iii) a list
of length 1, iv) a named integer vector of length 1, or v) a numeric vector.
By default, in |
ref.line |
Logical or numeric, default is "auto", whose behavior depends
on the situation. It is |
ref.line.par |
List. Parameters of the vertical line on the reference. The
default values are: |
lab.cex |
The size of the labels of the coefficients. Default is missing.
It is automatically set by an internal algorithm which can go as low as |
lab.min.cex |
The minimum size of the coefficients labels, as set by the internal algorithm. Default is 0.85. |
lab.max.mar |
The maximum size the left margin can take when trying to fit
the coefficient labels into it (only when |
lab.fit |
The method to fit the coefficient labels into the plotting region
(only when |
xlim.add |
A numeric vector of length 1 or 2. It represents an extension
factor of xlim, in percentage. Eg: |
ylim.add |
A numeric vector of length 1 or 2. It represents an extension
factor of ylim, in percentage. Eg: |
only.params |
Logical, default is |
sep |
The distance between two estimates – only when argument |
as.multiple |
Logical: default is |
bg |
Background color for the plot. By default it is white. |
group |
A list, default is missing. Each element of the list reports the
coefficients to be grouped while the name of the element is the group name. Each
element of the list can be either: i) a character vector of length 1, ii) of
length 2, or ii) a numeric vector. If equal to: i) then it is interpreted as
a pattern: all element fitting the regular expression will be grouped (note that
you can use the special character "^^" to clean the beginning of the names, see
example), if ii) it corresponds to the first and last elements to be grouped,
if iii) it corresponds to the coefficients numbers to be grouped. If equal to
a character vector, you can use a percentage to tell the algorithm to look at
the coefficients before aliasing (e.g. |
group.par |
A list of parameters controlling the display of the group. The
parameters controlling the line are: |
main |
The title of the plot. Default is |
value.lab |
The label to appear on the side of the coefficient values. If
|
ylab |
The label of the y-axis, default is |
xlab |
The label of the x-axis, default is |
sub |
A subtitle, default is |
i.select |
Integer scalar, default is 1. In |
iplot(): Plots the coefficients generated with i()
The function coefplot dispose of many arguments to parametrize the plots. Most
of these arguments can be set once an for all using the function setFixest_coefplot.
See Example 3 below for a demonstration.
The function iplot restricts coefplot to interactions or factors created
with the function i. Only one of the i-variables will be plotted at a time.
If you have several i-variables, you can navigate through them with the i.select argument.
The argument i.select is an index that will go through all the i-variables.
It will work well if the variables are pure, meaning not interacted with other
variables. If the i-variables are interacted, the index may have an odd behavior
but will (in most cases) work all the same, just try some numbers up until you
(hopefully) obtain the graph you want.
Note, importantly, that interactions of two factor variables are (in general) disregarded since they would require a 3-D plot to be properly represented.
The arguments keep, drop and order use regular expressions. If you are not aware
of regular expressions, I urge you to learn it, since it is an extremely powerful way
to manipulate character strings (and it exists across most programming languages).
For example drop = "Wind" would drop any variable whose name contains "Wind". Note that
variables such as "Temp:Wind" or "StrongWind" do contain "Wind", so would be dropped.
To drop only the variable named "Wind", you need to use drop = "^Wind$" (with "^" meaning
beginning, resp. "$" meaning end, of the string => this is the language of regular expressions).
Although you can combine several regular expressions in a single character string using pipes,
drop also accepts a vector of regular expressions.
You can use the special character "!" (exclamation mark) to reverse the effect of the regular
expression (this feature is specific to this function). For example drop = "!Wind" would drop
any variable that does not contain "Wind".
You can use the special character "%" (percentage) to make reference to the original variable
name instead of the aliased name. For example, you have a variable named "Month6", and use a
dictionary dict = c(Month6="June"). Thus the variable will be displayed as "June". If you
want to delete that variable, you can use either drop="June", or drop="%Month6" (which makes
reference to its original name).
The argument order takes in a vector of regular expressions, the order will follow the
elements of this vector. The vector gives a list of priorities, on the left the elements with
highest priority. For example, order = c("Wind", "!Inter", "!Temp") would give highest
priorities to the variables containing "Wind" (which would then appear first), second highest
priority is the variables not containing "Inter", last, with lowest priority, the variables not
containing "Temp". If you had the following variables: (Intercept), Temp:Wind, Wind, Temp you
would end up with the following order: Wind, Temp:Wind, Temp, (Intercept).
Laurent Berge
See setFixest_coefplot to set the default values of coefplot, and the estimation
functions: e.g. feols, fepois, feglm, fenegbin.
# # Example 1: Stacking two sets of results on the same graph # # Estimation on Iris data with one fixed-effect (Species) est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width | Species, iris) # Estimation results with clustered standard-errors # (the default when fixed-effects are present) est_clu = summary(est) # Now with "regular" standard-errors est_std = summary(est, se = "iid") # You can plot the two results at once coefplot(list(est_clu, est_std)) # Alternatively, you can use the argument x.shift # to do it sequentially: # First graph with clustered standard-errors coefplot(est, x.shift = -.2) # 'x.shift' was used to shift the coefficients on the left. # Second set of results: this time with # standard-errors that are not clustered. coefplot(est, se = "iid", x.shift = .2, add = TRUE, col = 2, ci.lty = 2, pch=15) # Note that we used 'se', an argument that will # be passed to summary.fixest legend("topright", col = 1:2, pch = 20, lwd = 1, lty = 1:2, legend = c("Clustered", "IID"), title = "Standard-Errors") # # Example 2: Interactions # # Now we estimate and plot the "yearly" treatment effects data(base_did) base_inter = base_did # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id+period, base_inter) # In the estimation, the variable treat is interacted # with each value of period but 5, set as a reference # coefplot will show all the coefficients: coefplot(est_did) # Note that the grouping of the coefficients is due to 'group = "auto"' # If you want to keep only the coefficients # created with i() (ie the interactions), use iplot iplot(est_did) # When estimations contain interactions, as before, # the default behavior of coefplot changes, # it now only plots interactions: coefplot(est_did) # We can see that the graph is different from before: # - only interactions are shown, # - the reference is present, # => this is fully flexible iplot(est_did, ref.line = FALSE, pt.join = TRUE) # # What if the interacted variable is not numeric? # Let's create a "month" variable all_months = c("aug", "sept", "oct", "nov", "dec", "jan", "feb", "mar", "apr", "may", "jun", "jul") base_inter$period_month = all_months[base_inter$period] # The new estimation est = feols(y ~ x1 + i(period_month, treat, "oct") | id+period, base_inter) # Since 'period_month' of type character, coefplot sorts it iplot(est) # To respect a plotting order, use a factor base_inter$month_factor = factor(base_inter$period_month, levels = all_months) est = feols(y ~ x1 + i(month_factor, treat, "oct") | id+period, base_inter) iplot(est) # # Example 3: Setting defaults # # coefplot has many arguments, which makes it highly flexible. # If you don't like the default style of coefplot. No worries, # you can set *your* default by using the function # setFixest_coefplot() dict = c("Petal.Length"="Length (Petal)", "Petal.Width"="Width (Petal)", "Sepal.Length"="Length (Sepal)", "Sepal.Width"="Width (Sepal)") setFixest_coefplot(ci.col = 2, pt.col = "darkblue", ci.lwd = 3, pt.cex = 2, pt.pch = 15, ci.width = 0, dict = dict) est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width + i(Species), iris) # And that's it coefplot(est) # You can set separate default values for iplot setFixest_coefplot("iplot", pt.join = TRUE, pt.join.par = list(lwd = 2, lty = 2)) iplot(est) # To reset to the default settings: setFixest_coefplot("all", reset = TRUE) coefplot(est) # # Example 4: group + cleaning # # You can use the argument group to group variables # You can further use the special character "^^" to clean # the beginning of the coef. name: particularly useful for factors est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width + Species, iris) # No grouping: coefplot(est) # now we group by Sepal and Species coefplot(est, group = list(Sepal = "Sepal", Species = "Species")) # now we group + clean the beginning of the names using the special character ^^ coefplot(est, group = list(Sepal = "^^Sepal.", Species = "^^Species"))# # Example 1: Stacking two sets of results on the same graph # # Estimation on Iris data with one fixed-effect (Species) est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width | Species, iris) # Estimation results with clustered standard-errors # (the default when fixed-effects are present) est_clu = summary(est) # Now with "regular" standard-errors est_std = summary(est, se = "iid") # You can plot the two results at once coefplot(list(est_clu, est_std)) # Alternatively, you can use the argument x.shift # to do it sequentially: # First graph with clustered standard-errors coefplot(est, x.shift = -.2) # 'x.shift' was used to shift the coefficients on the left. # Second set of results: this time with # standard-errors that are not clustered. coefplot(est, se = "iid", x.shift = .2, add = TRUE, col = 2, ci.lty = 2, pch=15) # Note that we used 'se', an argument that will # be passed to summary.fixest legend("topright", col = 1:2, pch = 20, lwd = 1, lty = 1:2, legend = c("Clustered", "IID"), title = "Standard-Errors") # # Example 2: Interactions # # Now we estimate and plot the "yearly" treatment effects data(base_did) base_inter = base_did # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id+period, base_inter) # In the estimation, the variable treat is interacted # with each value of period but 5, set as a reference # coefplot will show all the coefficients: coefplot(est_did) # Note that the grouping of the coefficients is due to 'group = "auto"' # If you want to keep only the coefficients # created with i() (ie the interactions), use iplot iplot(est_did) # When estimations contain interactions, as before, # the default behavior of coefplot changes, # it now only plots interactions: coefplot(est_did) # We can see that the graph is different from before: # - only interactions are shown, # - the reference is present, # => this is fully flexible iplot(est_did, ref.line = FALSE, pt.join = TRUE) # # What if the interacted variable is not numeric? # Let's create a "month" variable all_months = c("aug", "sept", "oct", "nov", "dec", "jan", "feb", "mar", "apr", "may", "jun", "jul") base_inter$period_month = all_months[base_inter$period] # The new estimation est = feols(y ~ x1 + i(period_month, treat, "oct") | id+period, base_inter) # Since 'period_month' of type character, coefplot sorts it iplot(est) # To respect a plotting order, use a factor base_inter$month_factor = factor(base_inter$period_month, levels = all_months) est = feols(y ~ x1 + i(month_factor, treat, "oct") | id+period, base_inter) iplot(est) # # Example 3: Setting defaults # # coefplot has many arguments, which makes it highly flexible. # If you don't like the default style of coefplot. No worries, # you can set *your* default by using the function # setFixest_coefplot() dict = c("Petal.Length"="Length (Petal)", "Petal.Width"="Width (Petal)", "Sepal.Length"="Length (Sepal)", "Sepal.Width"="Width (Sepal)") setFixest_coefplot(ci.col = 2, pt.col = "darkblue", ci.lwd = 3, pt.cex = 2, pt.pch = 15, ci.width = 0, dict = dict) est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width + i(Species), iris) # And that's it coefplot(est) # You can set separate default values for iplot setFixest_coefplot("iplot", pt.join = TRUE, pt.join.par = list(lwd = 2, lty = 2)) iplot(est) # To reset to the default settings: setFixest_coefplot("all", reset = TRUE) coefplot(est) # # Example 4: group + cleaning # # You can use the argument group to group variables # You can further use the special character "^^" to clean # the beginning of the coef. name: particularly useful for factors est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width + Species, iris) # No grouping: coefplot(est) # now we group by Sepal and Species coefplot(est, group = list(Sepal = "Sepal", Species = "Species")) # now we group + clean the beginning of the names using the special character ^^ coefplot(est, group = list(Sepal = "^^Sepal.", Species = "^^Species"))
Methods to extracts the coefficients table and its sub-components from an estimation.
coeftable(object, ...) se(object, ...) pvalue(object, ...) tstat(object, ...)coeftable(object, ...) se(object, ...) pvalue(object, ...) tstat(object, ...)
object |
An estimation (fitted model object), e.g. a |
... |
Other arguments to the methods. |
Returns a matrix (coeftable) or vectors.
Please look at the coeftable.fixest page for more detailed information.
est = lm(mpg ~ cyl, mtcars) coeftable(est)est = lm(mpg ~ cyl, mtcars) coeftable(est)
Default method to extracts the coefficients table and its sub-components from an estimation.
## Default S3 method: coeftable(object, keep, drop, order, ...) ## Default S3 method: se(object, keep, drop, order, ...) ## Default S3 method: tstat(object, keep, drop, order, ...) ## Default S3 method: pvalue(object, keep, drop, order, ...) ## S3 method for class 'matrix' se(object, keep, drop, order, ...)## Default S3 method: coeftable(object, keep, drop, order, ...) ## Default S3 method: se(object, keep, drop, order, ...) ## Default S3 method: tstat(object, keep, drop, order, ...) ## Default S3 method: pvalue(object, keep, drop, order, ...) ## S3 method for class 'matrix' se(object, keep, drop, order, ...)
object |
The result of an estimation (a fitted model object). Note that this function
is made to work with |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
... |
Other arguments that will be passed to First the method summary is applied if needed, then the coefficients table is extracted from its output. The default method is very naive and hopes that the resulting coefficients table
contained in the summary of the fitted model is well formed: this assumption is very
often wrong. Anyway, there is no development intended since the coeftable/se/pvalue/tstat
series of methods is only intended to work well with |
Returns a matrix (coeftable) or vectors.
se(default): Extracts the standard-errors from an estimation
tstat(default): Extracts the standard-errors from an estimation
pvalue(default): Extracts the p-values from an estimation
se(matrix): Extracts the standard-errors from a VCOV matrix
# NOTA: This function is really made to handle fixest objects # The default methods works for simple structures, but you'd be # likely better off with broom::tidy for other models est = lm(mpg ~ cyl, mtcars) coeftable(est) se(est)# NOTA: This function is really made to handle fixest objects # The default methods works for simple structures, but you'd be # likely better off with broom::tidy for other models est = lm(mpg ~ cyl, mtcars) coeftable(est) se(est)
Set of functions to directly extract some commonly used statistics, like the p-value or
the table of coefficients, from estimations. This was first implemented for
fixest estimations, but has some support for other models.
## S3 method for class 'fixest' coeftable( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, list = FALSE, ... ) ## S3 method for class 'fixest' se( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... ) ## S3 method for class 'fixest' tstat( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... ) ## S3 method for class 'fixest' pvalue( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... )## S3 method for class 'fixest' coeftable( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, list = FALSE, ... ) ## S3 method for class 'fixest' se( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... ) ## S3 method for class 'fixest' tstat( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... ) ## S3 method for class 'fixest' pvalue( object, vcov = NULL, ssc = NULL, cluster = NULL, keep = NULL, drop = NULL, order = NULL, ... )
object |
A |
vcov |
A function to be used to compute the standard-errors of each fixest object. You can
pass extra arguments to this function using the argument |
ssc |
An object of class |
cluster |
Tells how to cluster the standard-errors (if clustering is requested). Can
be either a list of vectors, a character vector of variable names, a formula or an
integer vector. Assume we want to perform 2-way clustering over |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
list |
Logical, default is |
... |
Other arguments to be passed to |
This set of tiny functions is primarily constructed for fixest estimations.
Returns a table of coefficients, with in rows the variables and four columns: the estimate, the standard-error, the t-statistic and the p-value.
If list = TRUE then a nested list is returned, the first layer is accessed with
the coefficients names; the second layer with the following values:
coef, se, tstat, pvalue. For example, with res = coeftable(est, list = TRUE)
you can access the SE of the coefficient x1 with res$x1$se; and its
coefficient with res$x1$coef, etc.
se(fixest): Extracts the standard-error of an estimation
tstat(fixest): Extracts the t-statistics of an estimation
pvalue(fixest): Extracts the p-value of an estimation
# Some data and estimation data(trade) est = fepois(Euros ~ log(dist_km) | Origin^Product + Year, trade) # # Coeftable/se/tstat/pvalue # # Default is clustering along Origin^Product coeftable(est) se(est) tstat(est) pvalue(est) # Now with two-way clustered standard-errors # and using coeftable() coeftable(est, cluster = ~Origin + Product) se(est, cluster = ~Origin + Product) pvalue(est, cluster = ~Origin + Product) tstat(est, cluster = ~Origin + Product) # Or you can cluster only once: est_sum = summary(est, cluster = ~Origin + Product) coeftable(est_sum) se(est_sum) tstat(est_sum) pvalue(est_sum) # You can use the arguments keep, drop, order # to rearrange the results base = iris names(base) = c("y", "x1", "x2", "x3", "species") est_iv = feols(y ~ x1 | x2 ~ x3, base) tstat(est_iv, keep = "x1") coeftable(est_iv, keep = "x1|Int") coeftable(est_iv, order = "!Int") # # Using lists # # Returning the coefficients table as a list can be useful for quick # reference in markdown documents. # Note that the "(Intercept)" is renamed into "constant" res = coeftable(est_iv, list = TRUE) # coefficient of the constant: res$constant$coef # pvalue of x1 res$x1$pvalue# Some data and estimation data(trade) est = fepois(Euros ~ log(dist_km) | Origin^Product + Year, trade) # # Coeftable/se/tstat/pvalue # # Default is clustering along Origin^Product coeftable(est) se(est) tstat(est) pvalue(est) # Now with two-way clustered standard-errors # and using coeftable() coeftable(est, cluster = ~Origin + Product) se(est, cluster = ~Origin + Product) pvalue(est, cluster = ~Origin + Product) tstat(est, cluster = ~Origin + Product) # Or you can cluster only once: est_sum = summary(est, cluster = ~Origin + Product) coeftable(est_sum) se(est_sum) tstat(est_sum) pvalue(est_sum) # You can use the arguments keep, drop, order # to rearrange the results base = iris names(base) = c("y", "x1", "x2", "x3", "species") est_iv = feols(y ~ x1 | x2 ~ x3, base) tstat(est_iv, keep = "x1") coeftable(est_iv, keep = "x1|Int") coeftable(est_iv, order = "!Int") # # Using lists # # Returning the coefficients table as a list can be useful for quick # reference in markdown documents. # Note that the "(Intercept)" is renamed into "constant" res = coeftable(est_iv, list = TRUE) # coefficient of the constant: res$constant$coef # pvalue of x1 res$x1$pvalue
fixest_multi estimationsSeries of methods to extract the coefficients table or its sub-components from a
fixest_multi objects (i.e. the outcome of multiple estimations).
## S3 method for class 'fixest_multi' coeftable( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, wide = FALSE, ... ) ## S3 method for class 'fixest_multi' se( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... ) ## S3 method for class 'fixest_multi' tstat( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... ) ## S3 method for class 'fixest_multi' pvalue( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... )## S3 method for class 'fixest_multi' coeftable( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, wide = FALSE, ... ) ## S3 method for class 'fixest_multi' se( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... ) ## S3 method for class 'fixest_multi' tstat( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... ) ## S3 method for class 'fixest_multi' pvalue( object, vcov = NULL, keep = NULL, drop = NULL, order = NULL, long = FALSE, ... )
object |
A |
vcov |
A function to be used to compute the standard-errors of each fixest object. You can
pass extra arguments to this function using the argument |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
long |
Logical scalar, default is |
wide |
A logical scalar, default is |
... |
Other arguments to be passed to |
It returns a data.frame containing the coefficients tables (or just the se/pvalue/tstat)
along with the information on which model was estimated.
If wide = TRUE, then a list is returned. The elements of the list are
coef/se/tstat/pvalue. Each element of the list is a wide table with a column per coefficient.
If long = TRUE, then all the information is stacked. This removes the 4 columns
containing the coefficient estimates to the p-values, and replace them with two
new columns: "param" and "value". The column param contains the
values coef/se/tstat/pvalue, and the column values the
associated numerical information.
se(fixest_multi): Extracts the standard-errors from fixest_multi estimations
tstat(fixest_multi): Extracts the t-stats from fixest_multi estimations
pvalue(fixest_multi): Extracts the p-values from fixest_multi estimations
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est_multi = feols(y ~ csw(x.[,1:3]), base, split = ~species) # we get all the coefficient tables at once coeftable(est_multi) # Now just the standard-errors se(est_multi) # wide = TRUE => leads toa list of wide tables coeftable(est_multi, wide = TRUE) # long = TRUE, all the information is stacked coeftable(est_multi, long = TRUE)base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est_multi = feols(y ~ csw(x.[,1:3]), base, split = ~species) # we get all the coefficient tables at once coeftable(est_multi) # Now just the standard-errors se(est_multi) # wide = TRUE => leads toa list of wide tables coeftable(est_multi, wide = TRUE) # long = TRUE, all the information is stacked coeftable(est_multi, long = TRUE)
fixest objectsIn some occasions, the optimization algorithm of femlm may fail to converge, or
the variance-covariance matrix may not be available. The most common reason of why
this happens is collinearity among variables. This function helps to find out which
set of variables is problematic.
collinearity(x, verbose)collinearity(x, verbose)
x |
A |
verbose |
An integer. If higher than or equal to 1, then a note is prompted at
each step of the algorithm. By default |
This function tests: 1) collinearity with the fixed-effect variables, 2) perfect multi-collinearity between the variables, 3) perfect multi-collinearity between several variables and the fixed-effects, and 4) identification issues when there are non-linear in parameters parts.
It returns a text message with the identified diagnostics.
Laurent Berge
# Creating an example data base: set.seed(1) fe_1 = sample(3, 100, TRUE) fe_2 = sample(20, 100, TRUE) x = rnorm(100, fe_1)**2 y = rnorm(100, fe_2)**2 z = rnorm(100, 3)**2 dep = rpois(100, x*y*z) base = data.frame(fe_1, fe_2, x, y, z, dep) # creating collinearity problems: base$v1 = base$v2 = base$v3 = base$v4 = 0 base$v1[base$fe_1 == 1] = 1 base$v2[base$fe_1 == 2] = 1 base$v3[base$fe_1 == 3] = 1 base$v4[base$fe_2 == 1] = 1 # Estimations: # Collinearity with the fixed-effects: res_1 = femlm(dep ~ log(x) + v1 + v2 + v4 | fe_1 + fe_2, base) collinearity(res_1) # => collinearity with the first fixed-effect identified, we drop v1 and v2 res_1bis = femlm(dep ~ log(x) + v4 | fe_1 + fe_2, base) collinearity(res_1bis) # Multi-Collinearity: res_2 = femlm(dep ~ log(x) + v1 + v2 + v3 + v4, base) collinearity(res_2)# Creating an example data base: set.seed(1) fe_1 = sample(3, 100, TRUE) fe_2 = sample(20, 100, TRUE) x = rnorm(100, fe_1)**2 y = rnorm(100, fe_2)**2 z = rnorm(100, 3)**2 dep = rpois(100, x*y*z) base = data.frame(fe_1, fe_2, x, y, z, dep) # creating collinearity problems: base$v1 = base$v2 = base$v3 = base$v4 = 0 base$v1[base$fe_1 == 1] = 1 base$v2[base$fe_1 == 2] = 1 base$v3[base$fe_1 == 3] = 1 base$v4[base$fe_2 == 1] = 1 # Estimations: # Collinearity with the fixed-effects: res_1 = femlm(dep ~ log(x) + v1 + v2 + v4 | fe_1 + fe_2, base) collinearity(res_1) # => collinearity with the first fixed-effect identified, we drop v1 and v2 res_1bis = femlm(dep ~ log(x) + v4 | fe_1 + fe_2, base) collinearity(res_1bis) # Multi-Collinearity: res_2 = femlm(dep ~ log(x) + v1 + v2 + v3 + v4, base) collinearity(res_2)
fixest
This function computes the confidence interval of parameter estimates obtained from a
model estimated with femlm, feols or feglm.
## S3 method for class 'fixest' confint( object, parm, level = 0.95, vcov, se, cluster, ssc = NULL, coef.col = FALSE, ... )## S3 method for class 'fixest' confint( object, parm, level = 0.95, vcov, se, cluster, ssc = NULL, coef.col = FALSE, ... )
object |
A |
parm |
The parameters for which to compute the confidence interval (either an integer vector OR a character vector with the parameter name). If missing, all parameters are used. |
level |
The confidence level. Default is 0.95. |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
coef.col |
Logical, default is |
... |
Not currently used. |
Returns a data.frame with two columns giving respectively the lower and upper bound of the confidence interval. There is as many rows as parameters.
Laurent Berge
# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination + Product, trade) # confidence interval with "normal" VCOV confint(est_pois) # confidence interval with "clustered" VCOV (w.r.t. the Origin factor) confint(est_pois, se = "cluster")# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination + Product, trade) # confidence interval with "normal" VCOV confint(est_pois) # confidence interval with "clustered" VCOV (w.r.t. the Origin factor) confint(est_pois, se = "cluster")
fixest_multi objectsComputes the confidence intervals of parameter estimates for fixest's multiple
estimation objects (aka fixest_multi).
## S3 method for class 'fixest_multi' confint( object, parm, level = 0.95, vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, ... )## S3 method for class 'fixest_multi' confint( object, parm, level = 0.95, vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, ... )
object |
A |
parm |
The parameters for which to compute the confidence interval (either an integer vector OR a character vector with the parameter name). If missing, all parameters are used. |
level |
The confidence level. Default is 0.95. |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
... |
Not currently used. |
It returns a data frame whose first columns indicate which model has been estimated. The last three columns indicate the coefficient name, and the lower and upper confidence intervals.
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x.[,1:3]) | sw0(species), base, vcov = "iid") confint(est) # focusing only on the coefficient 'x3' confint(est, "x3") # the 'id' provides the index of the estimation est[c(3, 6)]base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x.[,1:3]) | sw0(species), base, vcov = "iid") confint(est) # focusing only on the coefficient 'x3' confint(est, "x3") # the 'id' provides the index of the estimation est[c(3, 6)]
fixest estimationSimple utility to extract the degrees of freedom from a fixest estimation.
degrees_freedom( x, type, vars = NULL, vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, stage = 2 ) degrees_freedom_iid(x, type)degrees_freedom( x, type, vars = NULL, vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, stage = 2 ) degrees_freedom_iid(x, type)
x |
A |
type |
Character scalar, equal to "k", "resid", "t". If "k", then the number of
regressors is returned. If "resid", then it is the "residuals degree of freedom", i.e.
the number of observations minus the number of regressors. If "t", it is the degrees of
freedom used in the t-test. Note that these values are affected by how the VCOV of |
vars |
A vector of variable names, of the regressors. This is optional. If provided,
then |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
stage |
Either 1 or 2. Only concerns IV regressions, which stage to look at. The type of VCOV can have an influence on the degrees of freedom. In particular, when the
VCOV is clustered, the DoF returned will be in accordance with the way the small
sample correction was performed when computing the VCOV. That type of value is in general
not what we have in mind when we think of "degrees of freedom". To obtain the ones that are
more intuitive, please use |
degrees_freedom_iid(): Gets the degrees of freedom of a fixest estimation
# First: an estimation base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ x1 + x2 | species, base) # "Normal" standard-errors (SE) est_standard = summary(est, se = "st") # Clustered SEs est_clustered = summary(est, se = "clu") # The different degrees of freedom # => different type 1 DoF (because of the clustering) degrees_freedom(est_standard, type = "k") degrees_freedom(est_clustered, type = "k") # fixed-effects are excluded # => different type 2 DoF (because of the clustering) degrees_freedom(est_standard, type = "resid") # => equivalent to the df.residual from lm degrees_freedom(est_clustered, type = "resid")# First: an estimation base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ x1 + x2 | species, base) # "Normal" standard-errors (SE) est_standard = summary(est, se = "st") # Clustered SEs est_clustered = summary(est, se = "clu") # The different degrees of freedom # => different type 1 DoF (because of the clustering) degrees_freedom(est_standard, type = "k") degrees_freedom(est_clustered, type = "k") # fixed-effects are excluded # => different type 2 DoF (because of the clustering) degrees_freedom(est_standard, type = "resid") # => equivalent to the df.residual from lm degrees_freedom(est_clustered, type = "resid")
User-level access to internal demeaning algorithm of fixest.
demean( X, f, slope.vars, slope.flag, data, weights, nthreads = getFixest_nthreads(), notes = getFixest_notes(), iter = 2000, tol = 1e-06, fixef.reorder = TRUE, fixef.algo = NULL, na.rm = TRUE, as.matrix = is.atomic(X), im_confident = FALSE, ... )demean( X, f, slope.vars, slope.flag, data, weights, nthreads = getFixest_nthreads(), notes = getFixest_notes(), iter = 2000, tol = 1e-06, fixef.reorder = TRUE, fixef.algo = NULL, na.rm = TRUE, as.matrix = is.atomic(X), im_confident = FALSE, ... )
X |
A matrix, vector, data.frame or a list OR a formula OR a |
f |
A matrix, vector, data.frame or list. The factors used to center the variables in
argument |
slope.vars |
A vector, matrix or list representing the variables with varying slopes.
Matrices will be coerced using |
slope.flag |
An integer vector of the same length as the number of variables in |
data |
A data.frame containing all variables in the argument |
weights |
Vector, can be missing or NULL. If present, it must contain the same number of
observations as in |
nthreads |
Number of threads to be used. By default it is equal to |
notes |
Logical, whether to display a message when NA values are removed. By default it is
equal to |
iter |
Number of iterations, default is 2000. |
tol |
Stopping criterion of the algorithm. Default is |
fixef.reorder |
Logical, default is |
fixef.algo |
|
na.rm |
Logical, default is |
as.matrix |
Logical, if |
im_confident |
Logical, default is |
... |
Not currently used. |
It returns a data.frame of the same number of columns as the number of variables to be centered.
If na.rm = TRUE, then the number of rows is equal to the number of rows in input minus the
number of NA values (contained in X, f, slope.vars or weights). The default is to have
an output of the same number of observations as the input (filled with NAs where appropriate).
A matrix can be returned if as.matrix = TRUE.
You can add variables with varying slopes in the fixed-effect part of the formula.
The syntax is as follows: fixef_var[var1, var2]. Here the variables var1 and var2 will
be with varying slopes (one slope per value in fixef_var) and the fixed-effect
fixef_var will also be added.
To add only the variables with varying slopes and not the fixed-effect,
use double square brackets: fixef_var[[var1, var2]].
In other words:
fixef_var[var1, var2] is equivalent to fixef_var + fixef_var[[var1]] + fixef_var[[var2]]
fixef_var[[var1, var2]] is equivalent to fixef_var[[var1]] + fixef_var[[var2]]
In general, for convergence reasons, it is recommended to always add the fixed-effect and avoid using only the variable with varying slope (i.e. use single square brackets).
# Illustration of the FWL theorem data(trade) base = trade base$ln_dist = log(base$dist_km) base$ln_euros = log(base$Euros) # We center the two variables ln_dist and ln_euros # on the factors Origin and Destination X_demean = demean(X = base[, c("ln_dist", "ln_euros")], f = base[, c("Origin", "Destination")]) base[, c("ln_dist_dm", "ln_euros_dm")] = X_demean est = feols(ln_euros_dm ~ ln_dist_dm, base) est_fe = feols(ln_euros ~ ln_dist | Origin + Destination, base) # The results are the same as if we used the two factors # as fixed-effects etable(est, est_fe, se = "st") # # Variables with varying slopes # # You can center on factors but also on variables with varying slopes # Let's have an illustration base = iris names(base) = c("y", "x1", "x2", "x3", "species") # # We center y and x1 on species and x2 * species # using a formula base_dm = demean(y + x1 ~ species[x2], data = base) # using vectors base_dm_bis = demean(X = base[, c("y", "x1")], f = base$species, slope.vars = base$x2, slope.flag = 1) # Let's look at the equivalences res_vs_1 = feols(y ~ x1 + species + x2:species, base) res_vs_2 = feols(y ~ x1, base_dm) res_vs_3 = feols(y ~ x1, base_dm_bis) # only the small sample adj. differ in the SEs etable(res_vs_1, res_vs_2, res_vs_3, keep = "x1") # # center on x2 * species and on another FE base$fe = rep(1:5, 10) # using a formula => double square brackets! base_dm = demean(y + x1 ~ fe + species[[x2]], data = base) # using vectors => note slope.flag! base_dm_bis = demean(X = base[, c("y", "x1")], f = base[, c("fe", "species")], slope.vars = base$x2, slope.flag = c(0, -1)) # Explanations slope.flag = c(0, -1): # - the first 0: the first factor (fe) is associated to no variable # - the "-1": # * |-1| = 1: the second factor (species) is associated to ONE variable # * -1 < 0: the second factor should not be included as such # Let's look at the equivalences res_vs_1 = feols(y ~ x1 + i(fe) + x2:species, base) res_vs_2 = feols(y ~ x1, base_dm) res_vs_3 = feols(y ~ x1, base_dm_bis) # only the small sample adj. differ in the SEs etable(res_vs_1, res_vs_2, res_vs_3, keep = "x1")# Illustration of the FWL theorem data(trade) base = trade base$ln_dist = log(base$dist_km) base$ln_euros = log(base$Euros) # We center the two variables ln_dist and ln_euros # on the factors Origin and Destination X_demean = demean(X = base[, c("ln_dist", "ln_euros")], f = base[, c("Origin", "Destination")]) base[, c("ln_dist_dm", "ln_euros_dm")] = X_demean est = feols(ln_euros_dm ~ ln_dist_dm, base) est_fe = feols(ln_euros ~ ln_dist | Origin + Destination, base) # The results are the same as if we used the two factors # as fixed-effects etable(est, est_fe, se = "st") # # Variables with varying slopes # # You can center on factors but also on variables with varying slopes # Let's have an illustration base = iris names(base) = c("y", "x1", "x2", "x3", "species") # # We center y and x1 on species and x2 * species # using a formula base_dm = demean(y + x1 ~ species[x2], data = base) # using vectors base_dm_bis = demean(X = base[, c("y", "x1")], f = base$species, slope.vars = base$x2, slope.flag = 1) # Let's look at the equivalences res_vs_1 = feols(y ~ x1 + species + x2:species, base) res_vs_2 = feols(y ~ x1, base_dm) res_vs_3 = feols(y ~ x1, base_dm_bis) # only the small sample adj. differ in the SEs etable(res_vs_1, res_vs_2, res_vs_3, keep = "x1") # # center on x2 * species and on another FE base$fe = rep(1:5, 10) # using a formula => double square brackets! base_dm = demean(y + x1 ~ fe + species[[x2]], data = base) # using vectors => note slope.flag! base_dm_bis = demean(X = base[, c("y", "x1")], f = base[, c("fe", "species")], slope.vars = base$x2, slope.flag = c(0, -1)) # Explanations slope.flag = c(0, -1): # - the first 0: the first factor (fe) is associated to no variable # - the "-1": # * |-1| = 1: the second factor (species) is associated to ONE variable # * -1 < 0: the second factor should not be included as such # Let's look at the equivalences res_vs_1 = feols(y ~ x1 + i(fe) + x2:species, base) res_vs_2 = feols(y ~ x1, base_dm) res_vs_3 = feols(y ~ x1, base_dm_bis) # only the small sample adj. differ in the SEs etable(res_vs_1, res_vs_2, res_vs_3, keep = "x1")
Fine control of the demeaning procedure. Since the defaults are sensible,
only use this function in case of difficult convergence (e.g. in feols or demean).
That is, look at the slot $iterations of the returned object, if it's high (over 50),
then it might be worth playing around with these settings.
demeaning_algo( extraProj = 0, iter_warmup = 15, iter_projAfterAcc = 40, iter_grandAcc = 4, internal = FALSE )demeaning_algo( extraProj = 0, iter_warmup = 15, iter_projAfterAcc = 40, iter_grandAcc = 4, internal = FALSE )
extraProj |
Integer scalar, default is 0. Should there be more plain projection steps in between two accelerations? By default there is not. Each integer value adds 3 simple projections. This can be useful in cases where the acceleration algorithm does not work well but simple projections do. |
iter_warmup |
Integer scalar, default is 15. Only used in the presence of 3 or more fixed-effects (FE), ignored otherwise. For 3+ FEs, the algorithm is as follows:
|
iter_projAfterAcc |
Integer scalar, default is 40. After |
iter_grandAcc |
Integer scalar, default is 4. The regular fixed-point algorithm
applies an acceleration at each iteration. This acceleration is for |
internal |
Logical scalar, default is |
The demeaning algorithm is a fixed-point algorithm. Basically a function f is applied
until |f(X) - X| = 0, i.e. there is no difference between X and its image.
For terminology, let's call the application of f a "projection".
For well behaved problems, the algorithm in its simplest form, i.e. just applying f until
convergence, works fine and you only need a few iterations to reach convergence.
The problems arise for non well behaved problems. In these cases, simply applying the
function f can lead to extremely slow convergence. To handle these cases, this algorithm
applies a fixed-point acceleration algorithm, namely the "Irons and Tuck" acceleration.
The main algorithm combines regular projections with accelerations. Unfortunately sometimes this is not enough, so we also resort on internal cuisine, detailed below.
Sometimes the acceleration in its simplest form does not work well, and garbles the convergence properties. In those cases:
the argument extraProj adds several standard projections in between two accelerations,
which can improve the performance of the algorithm. By default there are no extra
projections. Note that while it can reduce the total number of iterations until convergence,
each iterations is almost twice expensive in terms of computing time.
the argument iter_projAfterAcc controls whether, and when, to apply a simple projection
right after the acceleration step. This projection adds roughly a 33% increase in
computing time per iteration but can improve the convergence properties and speed. By default
this step starts at iteration 40 (when the convergence rate is already not great).
On top of this, in case of very difficult convergence, a "grand" acceleration is added to
the algorithm. The regular acceleration is over f. Say g is the function equivalent to
the application of one regular iteration (which is a combination of one acceleration with
several projections).
By default the grand acceleration is over h = g o g o g o g, otherwise g applied four times.
The grand acceleration is controled with the argument iter_grandAcc which corresponds
to the number of iterations of the regular algorithm defining h.
Finally in case of 3+ fixed-effects (FE), the convergence in general takes more iterations.
In cases of the absence of quick convergence, applying a first demeaning over the first
two largest FEs before applying the demeaning over all FEs can improve convergence speed.
This is controlled with the argument iter_warmup which gives the number of iterations
over all the FEs to run before going to the 2 FEs demeaning. By default, the deameaning
over all FEs is run for 15 iterations before switching to the 2 FEs case.
The above defaults are the outcome of extended empirical applications, and try to strike a balance across a majority of cases. Of course you can always get better results by tailoring the settings to your problem at hand.
This function returns a list of 4 integers, equal to the arguments passed by the user.
That list is of class demeaning_algo.
B. M. Irons, R. Tuck, "A version of the Aitken accelerator for computer iteration", International journal of numerical methods in engineering 1 (1969) 670 275–277.
Returns the deviance from a fixest estimation.
## S3 method for class 'fixest' deviance(object, ...)## S3 method for class 'fixest' deviance(object, ...)
object |
A |
... |
Not currently used. |
Returns a numeric scalar equal to the deviance.
feols, fepois, feglm, fenegbin, feNmlm.
est = feols(Petal.Length ~ Petal.Width, iris) deviance(est) est_pois = fepois(Petal.Length ~ Petal.Width, iris) deviance(est_pois)est = feols(Petal.Length ~ Petal.Width, iris) deviance(est) est_pois = fepois(Petal.Length ~ Petal.Width, iris) deviance(est_pois)
fixest objectsReturns the residual degrees of freedom for a fitted fixest object
## S3 method for class 'fixest' df.residual(object, ...)## S3 method for class 'fixest' df.residual(object, ...)
object |
|
... |
Not currently used |
It returns an integer scalar giving the residuals degrees of freedom of the estimation.
The function degrees_freedom in fixest.
est = feols(mpg ~ hp, mtcars) df.residual(est)est = feols(mpg ~ hp, mtcars) df.residual(est)
This function shows the means and standard-deviations of several variables conditional on whether they are from the treated or the control group. The groups can further be split according to a pre/post variable. Results can be seamlessly be exported to Latex.
did_means( fml, base, treat_var, post_var, tex = FALSE, treat_dict, dict = getFixest_dict(), file, replace = FALSE, title, label, raw = FALSE, indiv, treat_first, prepostnames = c("Before", "After"), diff.inv = FALSE )did_means( fml, base, treat_var, post_var, tex = FALSE, treat_dict, dict = getFixest_dict(), file, replace = FALSE, title, label, raw = FALSE, indiv, treat_first, prepostnames = c("Before", "After"), diff.inv = FALSE )
fml |
Either a formula of the type |
base |
A data base containing all the variables in the formula |
treat_var |
Only if argument |
post_var |
Only if argument |
tex |
Should the result be displayed in Latex? Default is |
treat_dict |
A character vector of length two. What are the names of the treated
and the control? This should be a dictionary: e.g. |
dict |
A named character vector. A dictionary between the variables names and an alias.
For instance |
file |
A file path. If given, the table is written in Latex into this file. |
replace |
Default is |
title |
Character string giving the Latex title of the table. (Only if exported.) |
label |
Character string giving the Latex label of the table. (Only if exported.) |
raw |
Logical, default is |
indiv |
Either the variable name of individual identifiers, a one sided formula, or a vector. If the data is that of a panel, this can be used to track the number of individuals per group. |
treat_first |
Which value of the 'treatment' vector should appear on the left? By default the max value appears first (e.g. if the treatment variable is a 0/1 vector, 1 appears first). |
prepostnames |
Only if there is a 'post' variable. The names of the pre and post
periods to be displayed in Latex. Default is |
diff.inv |
Logical, default to |
By default, when the user tries to apply this function to nun-numeric variables, an error is raised. The exception is when the all variables are selected with the dot (like in . ~ treat. In this case, non-numeric variables are automatically omitted (with a message).
NAs are removed automatically: if the data contains NAs an information message will be prompted. First all observations containing NAs relating to the treatment or post variables are removed. Then if there are still NAs for the variables, they are excluded separately for each variable, and a new message detailing the NA breakup is prompted.
It returns a data.frame or a Latex table with the conditional means and statistical differences between the groups.
# Playing around with the DiD data data(base_did) # means of treat/control did_means(y+x1+period~treat, base_did) # same but inverting the difference did_means(y+x1+period~treat, base_did, diff.inv = TRUE) # now treat/control, before/after did_means(y+x1+period~treat|post, base_did) # same but with a new line giving the number of unique "indiv" for each case did_means(y+x1+period~treat|post, base_did, indiv = "id") # same but with the treat case "0" coming first did_means(y+x1+period~treat|post, base_did, indiv = ~id, treat_first = 0) # Selecting all the variables with "." did_means(.~treat|post, base_did, indiv = "id")# Playing around with the DiD data data(base_did) # means of treat/control did_means(y+x1+period~treat, base_did) # same but inverting the difference did_means(y+x1+period~treat, base_did, diff.inv = TRUE) # now treat/control, before/after did_means(y+x1+period~treat|post, base_did) # same but with a new line giving the number of unique "indiv" for each case did_means(y+x1+period~treat|post, base_did, indiv = "id") # same but with the treat case "0" coming first did_means(y+x1+period~treat|post, base_did, indiv = ~id, treat_first = 0) # Selecting all the variables with "." did_means(.~treat|post, base_did, indiv = "id")
Compactly performs many low level string operations. Advanced support for pluralization.
dsb( ..., frame = parent.frame(), sep = "", vectorize = FALSE, nest = TRUE, collapse = NULL )dsb( ..., frame = parent.frame(), sep = "", vectorize = FALSE, nest = TRUE, collapse = NULL )
... |
Character scalars that will be collapsed with the argument |
frame |
An environment used to evaluate the variables in |
sep |
Character scalar, default is |
vectorize |
Logical, default is |
nest |
Logical, default is |
collapse |
Character scalar or There are over 30 basic string operations, it supports pluralization, it's fast (e.g. faster than See detailed help on the console with |
It returns a character vector whose length depends on the elements and operations in ".[]".
# # BASIC USAGE #### # x = c("Romeo", "Juliet") # .[x] inserts x dsb("Hello .[x]!") # elements in ... are collapsed with "" (default) dsb("Hello .[x[1]], ", "how is .[x[2]] doing?") # Splitting a comma separated string # The mechanism is explained later dsb("/J. Mills, David, Agnes, Dr Strong") # Nota: this is equivalent to (explained later) dsb("', *'S !J. Mills, David, Agnes, Dr Strong") # # Applying low level operations to strings # # Two main syntax: # A) expression evaluation # .[operation ? x] # | | # | \-> the expression to be evaluated # \-> ? means that the expression will be evaluated # B) verbatim # .[operation ! x] # | | # | \-> the expression taken as verbatim (here ' x') # \-> ! means that the expression is taken as verbatim # operation: usually 'arg'op with op an operation code. # Example: splitting x = "hello dear" dsb(".[' 's ? x]") # x is split by ' ' dsb(".[' 's !hello dear]") # 'hello dear' is split by ' ' # had we used ?, there would have been an error # By default, the string is nested in .[], so in that case no need to use .[]: dsb("' 's ? x") dsb("' 's !hello dear") # There are 35 string operators # Operators usually have a default value # Operations can be chained by separating them with a comma # Example: default of 's' is ' ' + chaining with collapse dsb("s, ' my 'c!hello dear") # # Nesting # # .[operations ! s1.[expr]s2] # | | # | \-> expr will be evaluated then added to the string # \-> nesting requires verbatim evaluation: '!' dsb("The variables are: .[C!x.[1:4]].") # This one is a bit ugly but it shows triple nesting dsb("The variables are: .[w, C!.[2* ! x.[1:4]].[S, 4** ! , _sq]].") # # Splitting # # s: split with fixed pattern, default is ' ' dsb("s !a b c") dsb("' b 's !a b c") # S: split with regex pattern, default is ', *' dsb("S !a, b, c") dsb("'[[:punct:] ]'S !a! b; c") # # Collapsing # # c and C do the same, their default is different # syntax: 's1||s2' with # - s1 the string used for collapsing # - s2 (optional) the string used for the last collapse # c: default is ' ' dsb("c?1:3") # C: default is ', || and ' dsb("C?1:3") dsb("', || or 'c?1:4") # # Extraction # # x: extracts the first pattern # X: extracts all patterns # syntax: 'pattern'x # Default is '[[:alnum:]]+' x = "This years is... 2020" dsb("x ? x") dsb("X ? x") dsb("'\\d+'x ? x") # # STRING FORMATTING #### # # # u, U: uppercase first/all letters # first letter dsb("u!julia mills") # title case: split -> upper first letter -> collapse dsb("s, u, c!julia mills") # upper all letters dsb("U!julia mills") # # L: lowercase dsb("L!JULIA MILLS") # # q, Q: single or double quote dsb("S, q, C!Julia, David, Wilkins") dsb("S, Q, C!Julia, David, Wilkins") # # f, F: formats the string to fit the same length score = c(-10, 2050) nm = c("Wilkins", "David") dsb("Monopoly scores:\n.['\n'c ! - .[f ? nm]: .[F ? score] US$]") # OK that example may have been a bit too complex, # let's make it simple: dsb("Scores: .[f ? score]") dsb("Names: .[F ? nm]") # # w, W: reformat the white spaces # w: suppresses trimming white spaces + normalizes successive white spaces # W: same but also includes punctuation dsb("w ! The white spaces are now clean. ") dsb("W ! I, really -- truly; love punctuation!!!") # # %: applies sprintf formatting dsb("pi = .['.2f'% ? pi]") # # a: appends text on each item # syntax: 's1|s2'a, adds s1 at the beginning and s2 at the end of the string # It accepts the special values :1:, :i:, :I:, :a:, :A: # These values create enumerations (only one such value is accepted) # appending square brackets dsb("'[|]'a, ' + 'c!x.[1:4]") # Enumerations acad = dsb("/you like admin, you enjoy working on weekends, you really love emails") dsb("Main reasons to pursue an academic career:\n .[':i:) 'a, C ? acad].") # # A: same as 'a' but adds at the begging/end of the full string (not on the elements) # special values: :n:, :N:, give the number of elements characters = dsb("/David, Wilkins, Dora, Agnes") dsb("There are .[':N: characters: 'A, C ? characters].") # # stop: removes basic English stopwords # the list is from the Snowball project: http://snowball.tartarus.org/algorithms/english/stop.txt dsb("stop, w!It is a tale told by an idiot, full of sound and fury, signifying nothing.") # # k: keeps the first n characters # syntax: nk: keeps the first n characters # 'n|s'k: same + adds 's' at the end of shortened strings # 'n||s'k: same but 's' counts in the n characters kept words = dsb("/short, constitutional") dsb("5k ? words") dsb("'5|..'k ? words") dsb("'5||..'k ? words") # # K: keeps the first n elements # syntax: nK: keeps the first n elements # 'n|s'K: same + adds the element 's' at the end # 'n||s'K: same but 's' counts in the n elements kept # # Special values :rest: and :REST:, give the number of items dropped bx = dsb("/Pessac Leognan, Saint Emilion, Marguaux, Saint Julien, Pauillac") dsb("Bordeaux wines I like: .[3K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3|etc..'K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3||etc..'K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3|and at least :REST: others'K, ', 'C ? bx].") # # Ko, KO: special operator which keeps the first n elements and adds "others" # syntax: nKo # KO gives the rest in letters dsb("Bordeaux wines I like: .[4KO, C ? bx].") # # r, R: string replacement # syntax: 's'R: deletes the content in 's' (replaces with the empty string) # 's1 => s2'R replaces s1 into s2 # r: fixed / R: perl = TRUE dsb("'e'r !The letter e is deleted") # adding a perl look-behind dsb("'(?<! )e'R !The letter e is deleted") dsb("'e => a'r !The letter e becomes a") dsb("'([[:alpha:]]{3})[[:alpha:]]+ => \\1.'R !Trimming the words") # # *, *c, **, **c: replication, replication + collapse # syntax: n* or n*c # ** is the same as * but uses "each" in the replication dsb("N.[10*c!o]!") dsb("3*c ? 1:3") dsb("3**c ? 1:3") # # d: replaces the items by the empty string # -> useful in conditions dsb("d!I am going to be annihilated") # # ELEMENT MANIPULATION #### # # # D: deletes all elements # -> useful in conditions x = dsb("/I'll, be, deleted") dsb("D ? x") # # i, I: inserts an item # syntax: 's1|s2'i: inserts s1 first and s2 last # I: is the same as i but is 'invisibly' included characters = dsb("/David, Wilkins, Dora, Agnes, Trotwood") dsb("'Heep|Spenlow'i, C ? characters") dsb("'Heep|Spenlow'I, C ? characters") # # PLURALIZATION #### # # There is support for pluralization # # *s, *s_: adds 's' or 's ' depending on the number of elements nb = 1:5 dsb("Number.[*s, D ? nb]: .[C ? nb]") dsb("Number.[*s, D ? 2 ]: .[C ? 2 ]") # or dsb("Number.[*s, ': 'A, C ? nb]") # # v, V: adds a verb at the beginning/end of the string # syntax: 'verb'v # Unpopular opinion? brand = c("Apple", "Samsung") dsb(".[V, C ? brand] overrated.") dsb(".[V, C ? brand[1]] overrated.") win = dsb("/Peggoty, Agnes, Emily") dsb("The winner.[*s_, v, C ? win].") dsb("The winner.[*s_, v, C ? win[1]].") # Other verbs dsb(".[' have'V, C ? win] won a prize.") dsb(".[' have'V, C ? win[1]] won a prize.") dsb(".[' was'V, C ? win] unable to come.") dsb(".[' was'V, C ? win[1]] unable to come.") # # *A: appends text depending on the length of the vector # syntax: 's1|s2 / s3|s4' # if length == 1: applies 's1|s2'A # if length > 1: applies 's3|s4'A win = dsb("/Barkis, Micawber, Murdstone") dsb("The winner.[' is /s are '*A, C ? win].") dsb("The winner.[' is /s are '*A, C ? win[1]].") # # CONDITIONS #### # # Conditions can be applied with 'if' statements.", # The syntax is 'type comp value'if(true : false), with # - type: either 'len', 'char', 'fixed' or 'regex' # + len: number of elements in the vector # + char: number of characters # + fixed: fixed pattern # + regex: regular expression pattern # - comp: a comparator: # + valid for len/char: >, <, >=, <=, !=, == # + valid for fixed/regex: !=, == # - value: a value for which the comparison is applied. # - true: operations to be applied if true (can be void) # - false: operations to be applied if false (can be void) dsb("'char <= 2'if('(|)'a : '[|]'a), ' + 'c ? c(1, 12, 123)") sentence = "This is a sentence with some longish words." dsb("s, 'char<=4'if(D), c ? sentence") dsb("s, 'fixed == e'if(:D), c ! Only words with an e are selected.") # # ARGUMENTS FROM THE FRAME #### # # Arguments can be evaluated from the calling frame. # Simply use backticks instead of quotes. dollar = 6 reason = "glory" dsb("Why do you develop packages? For .[`dollar`*c!$]?", "For money? No... for .[U,''s, c?reason]!", sep = "\n")# # BASIC USAGE #### # x = c("Romeo", "Juliet") # .[x] inserts x dsb("Hello .[x]!") # elements in ... are collapsed with "" (default) dsb("Hello .[x[1]], ", "how is .[x[2]] doing?") # Splitting a comma separated string # The mechanism is explained later dsb("/J. Mills, David, Agnes, Dr Strong") # Nota: this is equivalent to (explained later) dsb("', *'S !J. Mills, David, Agnes, Dr Strong") # # Applying low level operations to strings # # Two main syntax: # A) expression evaluation # .[operation ? x] # | | # | \-> the expression to be evaluated # \-> ? means that the expression will be evaluated # B) verbatim # .[operation ! x] # | | # | \-> the expression taken as verbatim (here ' x') # \-> ! means that the expression is taken as verbatim # operation: usually 'arg'op with op an operation code. # Example: splitting x = "hello dear" dsb(".[' 's ? x]") # x is split by ' ' dsb(".[' 's !hello dear]") # 'hello dear' is split by ' ' # had we used ?, there would have been an error # By default, the string is nested in .[], so in that case no need to use .[]: dsb("' 's ? x") dsb("' 's !hello dear") # There are 35 string operators # Operators usually have a default value # Operations can be chained by separating them with a comma # Example: default of 's' is ' ' + chaining with collapse dsb("s, ' my 'c!hello dear") # # Nesting # # .[operations ! s1.[expr]s2] # | | # | \-> expr will be evaluated then added to the string # \-> nesting requires verbatim evaluation: '!' dsb("The variables are: .[C!x.[1:4]].") # This one is a bit ugly but it shows triple nesting dsb("The variables are: .[w, C!.[2* ! x.[1:4]].[S, 4** ! , _sq]].") # # Splitting # # s: split with fixed pattern, default is ' ' dsb("s !a b c") dsb("' b 's !a b c") # S: split with regex pattern, default is ', *' dsb("S !a, b, c") dsb("'[[:punct:] ]'S !a! b; c") # # Collapsing # # c and C do the same, their default is different # syntax: 's1||s2' with # - s1 the string used for collapsing # - s2 (optional) the string used for the last collapse # c: default is ' ' dsb("c?1:3") # C: default is ', || and ' dsb("C?1:3") dsb("', || or 'c?1:4") # # Extraction # # x: extracts the first pattern # X: extracts all patterns # syntax: 'pattern'x # Default is '[[:alnum:]]+' x = "This years is... 2020" dsb("x ? x") dsb("X ? x") dsb("'\\d+'x ? x") # # STRING FORMATTING #### # # # u, U: uppercase first/all letters # first letter dsb("u!julia mills") # title case: split -> upper first letter -> collapse dsb("s, u, c!julia mills") # upper all letters dsb("U!julia mills") # # L: lowercase dsb("L!JULIA MILLS") # # q, Q: single or double quote dsb("S, q, C!Julia, David, Wilkins") dsb("S, Q, C!Julia, David, Wilkins") # # f, F: formats the string to fit the same length score = c(-10, 2050) nm = c("Wilkins", "David") dsb("Monopoly scores:\n.['\n'c ! - .[f ? nm]: .[F ? score] US$]") # OK that example may have been a bit too complex, # let's make it simple: dsb("Scores: .[f ? score]") dsb("Names: .[F ? nm]") # # w, W: reformat the white spaces # w: suppresses trimming white spaces + normalizes successive white spaces # W: same but also includes punctuation dsb("w ! The white spaces are now clean. ") dsb("W ! I, really -- truly; love punctuation!!!") # # %: applies sprintf formatting dsb("pi = .['.2f'% ? pi]") # # a: appends text on each item # syntax: 's1|s2'a, adds s1 at the beginning and s2 at the end of the string # It accepts the special values :1:, :i:, :I:, :a:, :A: # These values create enumerations (only one such value is accepted) # appending square brackets dsb("'[|]'a, ' + 'c!x.[1:4]") # Enumerations acad = dsb("/you like admin, you enjoy working on weekends, you really love emails") dsb("Main reasons to pursue an academic career:\n .[':i:) 'a, C ? acad].") # # A: same as 'a' but adds at the begging/end of the full string (not on the elements) # special values: :n:, :N:, give the number of elements characters = dsb("/David, Wilkins, Dora, Agnes") dsb("There are .[':N: characters: 'A, C ? characters].") # # stop: removes basic English stopwords # the list is from the Snowball project: http://snowball.tartarus.org/algorithms/english/stop.txt dsb("stop, w!It is a tale told by an idiot, full of sound and fury, signifying nothing.") # # k: keeps the first n characters # syntax: nk: keeps the first n characters # 'n|s'k: same + adds 's' at the end of shortened strings # 'n||s'k: same but 's' counts in the n characters kept words = dsb("/short, constitutional") dsb("5k ? words") dsb("'5|..'k ? words") dsb("'5||..'k ? words") # # K: keeps the first n elements # syntax: nK: keeps the first n elements # 'n|s'K: same + adds the element 's' at the end # 'n||s'K: same but 's' counts in the n elements kept # # Special values :rest: and :REST:, give the number of items dropped bx = dsb("/Pessac Leognan, Saint Emilion, Marguaux, Saint Julien, Pauillac") dsb("Bordeaux wines I like: .[3K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3|etc..'K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3||etc..'K, ', 'C ? bx].") dsb("Bordeaux wines I like: .['3|and at least :REST: others'K, ', 'C ? bx].") # # Ko, KO: special operator which keeps the first n elements and adds "others" # syntax: nKo # KO gives the rest in letters dsb("Bordeaux wines I like: .[4KO, C ? bx].") # # r, R: string replacement # syntax: 's'R: deletes the content in 's' (replaces with the empty string) # 's1 => s2'R replaces s1 into s2 # r: fixed / R: perl = TRUE dsb("'e'r !The letter e is deleted") # adding a perl look-behind dsb("'(?<! )e'R !The letter e is deleted") dsb("'e => a'r !The letter e becomes a") dsb("'([[:alpha:]]{3})[[:alpha:]]+ => \\1.'R !Trimming the words") # # *, *c, **, **c: replication, replication + collapse # syntax: n* or n*c # ** is the same as * but uses "each" in the replication dsb("N.[10*c!o]!") dsb("3*c ? 1:3") dsb("3**c ? 1:3") # # d: replaces the items by the empty string # -> useful in conditions dsb("d!I am going to be annihilated") # # ELEMENT MANIPULATION #### # # # D: deletes all elements # -> useful in conditions x = dsb("/I'll, be, deleted") dsb("D ? x") # # i, I: inserts an item # syntax: 's1|s2'i: inserts s1 first and s2 last # I: is the same as i but is 'invisibly' included characters = dsb("/David, Wilkins, Dora, Agnes, Trotwood") dsb("'Heep|Spenlow'i, C ? characters") dsb("'Heep|Spenlow'I, C ? characters") # # PLURALIZATION #### # # There is support for pluralization # # *s, *s_: adds 's' or 's ' depending on the number of elements nb = 1:5 dsb("Number.[*s, D ? nb]: .[C ? nb]") dsb("Number.[*s, D ? 2 ]: .[C ? 2 ]") # or dsb("Number.[*s, ': 'A, C ? nb]") # # v, V: adds a verb at the beginning/end of the string # syntax: 'verb'v # Unpopular opinion? brand = c("Apple", "Samsung") dsb(".[V, C ? brand] overrated.") dsb(".[V, C ? brand[1]] overrated.") win = dsb("/Peggoty, Agnes, Emily") dsb("The winner.[*s_, v, C ? win].") dsb("The winner.[*s_, v, C ? win[1]].") # Other verbs dsb(".[' have'V, C ? win] won a prize.") dsb(".[' have'V, C ? win[1]] won a prize.") dsb(".[' was'V, C ? win] unable to come.") dsb(".[' was'V, C ? win[1]] unable to come.") # # *A: appends text depending on the length of the vector # syntax: 's1|s2 / s3|s4' # if length == 1: applies 's1|s2'A # if length > 1: applies 's3|s4'A win = dsb("/Barkis, Micawber, Murdstone") dsb("The winner.[' is /s are '*A, C ? win].") dsb("The winner.[' is /s are '*A, C ? win[1]].") # # CONDITIONS #### # # Conditions can be applied with 'if' statements.", # The syntax is 'type comp value'if(true : false), with # - type: either 'len', 'char', 'fixed' or 'regex' # + len: number of elements in the vector # + char: number of characters # + fixed: fixed pattern # + regex: regular expression pattern # - comp: a comparator: # + valid for len/char: >, <, >=, <=, !=, == # + valid for fixed/regex: !=, == # - value: a value for which the comparison is applied. # - true: operations to be applied if true (can be void) # - false: operations to be applied if false (can be void) dsb("'char <= 2'if('(|)'a : '[|]'a), ' + 'c ? c(1, 12, 123)") sentence = "This is a sentence with some longish words." dsb("s, 'char<=4'if(D), c ? sentence") dsb("s, 'fixed == e'if(:D), c ! Only words with an e are selected.") # # ARGUMENTS FROM THE FRAME #### # # Arguments can be evaluated from the calling frame. # Simply use backticks instead of quotes. dollar = 6 reason = "glory" dsb("Why do you develop packages? For .[`dollar`*c!$]?", "For money? No... for .[U,''s, c?reason]!", sep = "\n")
If emmeans is installed, its functionality is supported for fixest
or fixest_multi objects. Its reference grid is based on the main part
of the model, and does not include fixed effects or instrumental variables.
Note that any desired arguments to vcov() may be passed as optional
arguments in emmeans::emmeans() or emmeans::ref_grid().
When fixed effects are present, estimated marginal means (EMMs) are estimated correctly, provided equal weighting is used. However, the SEs of these EMMs will be incorrect - often dramatically - because the estimated variance of the intercept is not available. However, contrasts among EMMs can be estimated and tested with no issues, because these do not involve the intercept.
Russell V. Lenth
if(requireNamespace("emmeans") && requireNamespace("AER")) { data(Fatalities, package = "AER") Fatalities$frate = with(Fatalities, fatal/pop * 10000) fat.mod = feols(frate ~ breath * jail * beertax | state + year, data = Fatalities) emm = emmeans::emmeans(fat.mod, ~ breath*jail, cluster = ~ state + year) emm ### SEs and CIs are incorrect emmeans::contrast(emm, "consec", by = "breath") ### results are reliable }if(requireNamespace("emmeans") && requireNamespace("AER")) { data(Fatalities, package = "AER") Fatalities$frate = with(Fatalities, fatal/pop * 10000) fat.mod = feols(frate ~ breath * jail * beertax | state + year, data = Fatalities) emm = emmeans::emmeans(fat.mod, ~ breath*jail, cluster = ~ state + year) emm ### SEs and CIs are incorrect emmeans::contrast(emm, "consec", by = "breath") ### results are reliable }
fixest estimation from a fixest environmentThis is a function advanced users which allows to estimate any fixest estimation from a
fixest environment obtained with only.env = TRUE in a fixest estimation.
est_env(env, y, X, weights, endo, inst)est_env(env, y, X, weights, endo, inst)
env |
An environment obtained from a |
y |
A vector representing the dependent variable. Should be of the same length as the number of observations in the initial estimation. |
X |
A matrix representing the independent variables. Should be of the same dimension as in the initial estimation. |
weights |
A vector of weights (i.e. with only positive values). Should be of the same length as the number of observations in the initial estimation. If identical to the scalar 1, this will mean that no weights will be used in the estimation. |
endo |
A matrix representing the endogenous regressors in IV estimations. It should be of the same dimension as the original endogenous regressors. |
inst |
A matrix representing the instruments in IV estimations. It should be of the same dimension as the original instruments. |
This function has been created for advanced users, mostly to avoid overheads
when making simulations with fixest.
How can it help you make simulations? First make a core estimation with only.env = TRUE,
and usually with only.coef = TRUE (to avoid having extra things that take time to compute).
Then loop while modifying the appropriate things directly in the environment. Beware that
if you make a mistake here (typically giving stuff of the wrong length),
then you can make the R session crash because there is no more error-handling!
Finally estimate with est_env(env = core_env) and store the results.
Instead of est_env, you could use directly fixest estimations too, like feols,
since they accept the env argument. The function est_env is only here to add a
bit of generality to avoid the trouble to the user to write conditions
(look at the source, it's just a one liner).
Objects of main interest in the environment are:
The left hand side, or dependent variable.
The matrix of the right-hand-side, or explanatory variables.
The matrix of the endogenous variables in IV regressions.
The matrix of the instruments in IV regressions.
The vector of weights.
I strongly discourage changing the dimension of any of these elements, or else crash can occur.
However, you can change their values at will (given the dimension stay the same).
The only exception is the weights, which tolerates changing its dimension: it can
be identical to the scalar 1 (meaning no weights), or to something of the length the
number of observations.
I also discourage changing anything in the fixed-effects (even their value) since this will almost surely lead to a crash.
Note that this function is mostly useful when the overheads/estimation ratio is high. This means that OLS will benefit the most from this function. For GLM/Max.Lik. estimations, the ratio is small since the overheads is only a tiny portion of the total estimation time. Hence this function will be less useful for these models.
It returns the results of a fixest estimation: the one that was summoned when
obtaining the environment.
Laurent Berge
# Let's make a short simulation # Inspired from Grant McDermott bboot function # See https://twitter.com/grant_mcdermott/status/1487528757418102787 # Simple function that computes a Bayesian bootstrap bboot = function(x, n_sim = 100){ # We bootstrap on the weights # Works with fixed-effects/IVs # and with any fixest function that accepts weights core_env = update(x, only.coef = TRUE, only.env = TRUE) n_obs = x$nobs res_all = vector("list", n_sim) for(i in 1:n_sim){ ## begin: NOT RUN ## We could directly assign in the environment: # assign("weights.value", rexp(n_obs, rate = 1), core_env) # res_all[[i]] = est_env(env = core_env) ## end: NOT RUN ## Instead we can use the argument weights, which does the same res_all[[i]] = est_env(env = core_env, weights = rexp(n_obs, rate = 1)) } do.call(rbind, res_all) } est = feols(mpg ~ wt + hp, mtcars) boot_res = bboot(est) coef = colMeans(boot_res) std_err = apply(boot_res, 2, sd) # Comparing the results with the main estimation coeftable(est) cbind(coef, std_err)# Let's make a short simulation # Inspired from Grant McDermott bboot function # See https://twitter.com/grant_mcdermott/status/1487528757418102787 # Simple function that computes a Bayesian bootstrap bboot = function(x, n_sim = 100){ # We bootstrap on the weights # Works with fixed-effects/IVs # and with any fixest function that accepts weights core_env = update(x, only.coef = TRUE, only.env = TRUE) n_obs = x$nobs res_all = vector("list", n_sim) for(i in 1:n_sim){ ## begin: NOT RUN ## We could directly assign in the environment: # assign("weights.value", rexp(n_obs, rate = 1), core_env) # res_all[[i]] = est_env(env = core_env) ## end: NOT RUN ## Instead we can use the argument weights, which does the same res_all[[i]] = est_env(env = core_env, weights = rexp(n_obs, rate = 1)) } do.call(rbind, res_all) } est = feols(mpg ~ wt + hp, mtcars) boot_res = bboot(est) coef = colMeans(boot_res) std_err = apply(boot_res, 2, sd) # Comparing the results with the main estimation coeftable(est) cbind(coef, std_err)
Extracts the scores from a fixest estimation.
## S3 method for class 'fixest' estfun(x, ...)## S3 method for class 'fixest' estfun(x, ...)
x |
A |
... |
Not currently used. |
Returns a matrix of the same number of rows as the number of observations used for the estimation, and the same number of columns as there were variables.
data(iris) est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) head(estfun(est))data(iris) est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) head(estfun(est))
Aggregates the results of multiple estimations and displays them in the form of either a Latex
table or a data.frame. Note that you will need the booktabs package for the Latex table to
render properly. See setFixest_etable to set the default values, and style.tex to customize Latex output.
esttable( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, fitstat = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.df = NULL, group = NULL, extralines = NULL, fixef.group = NULL, drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.df = NULL, fit_format = "__var__", coef.just = NULL, highlight = NULL, coef.style = NULL, export = NULL, page.width = "fit", div.class = "etable" ) esttex( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, fitstat = NULL, title = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, label = NULL, float = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.tex = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.tex = NULL, tpt = FALSE, arraystretch = NULL, adjustbox = NULL, fontsize = NULL, fit_format = "__var__", tabular = "normal", highlight = NULL, coef.style = NULL, meta = NULL, meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, view = FALSE, export = NULL, markdown = NULL, page.width = "fit", div.class = "etable" ) etable( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, tex, fitstat = NULL, title = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, label = NULL, float = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.tex = NULL, style.df = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.tex = NULL, postprocess.df = NULL, tpt = FALSE, arraystretch = NULL, adjustbox = NULL, fontsize = NULL, fit_format = "__var__", coef.just = NULL, tabular = "normal", highlight = NULL, coef.style = NULL, meta = NULL, meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, view = FALSE, export = NULL, markdown = NULL, page.width = "fit", div.class = "etable" ) setFixest_etable( digits = 4, digits.stats = 5, fitstat, coefstat = c("se", "tstat", "confint"), ci = 0.95, se.below = TRUE, keep, drop, order, dict, float, fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, family, powerBelow = -5, interaction.order = NULL, depvar, style.tex = NULL, style.df = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, view = FALSE, markdown = NULL, view.cache = FALSE, page.width = "fit", postprocess.tex = NULL, postprocess.df = NULL, fit_format = "__var__", meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, reset = FALSE, save = FALSE ) getFixest_etable() ## S3 method for class 'etable_tex' print(x, ...) ## S3 method for class 'etable_df' print(x, ...) log_etable(type = "pdflatex")esttable( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, fitstat = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.df = NULL, group = NULL, extralines = NULL, fixef.group = NULL, drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.df = NULL, fit_format = "__var__", coef.just = NULL, highlight = NULL, coef.style = NULL, export = NULL, page.width = "fit", div.class = "etable" ) esttex( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, fitstat = NULL, title = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, label = NULL, float = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.tex = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.tex = NULL, tpt = FALSE, arraystretch = NULL, adjustbox = NULL, fontsize = NULL, fit_format = "__var__", tabular = "normal", highlight = NULL, coef.style = NULL, meta = NULL, meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, view = FALSE, export = NULL, markdown = NULL, page.width = "fit", div.class = "etable" ) etable( ..., vcov = NULL, stage = 2, agg = NULL, se = NULL, ssc = NULL, cluster = NULL, .vcov = NULL, .vcov_args = NULL, digits = 4, digits.stats = 5, tex, fitstat = NULL, title = NULL, coefstat = "se", ci = 0.95, se.row = NULL, se.below = NULL, keep = NULL, drop = NULL, order = NULL, dict = TRUE, file = NULL, replace = FALSE, convergence = NULL, signif.code = NULL, label = NULL, float = NULL, headers = list("auto"), fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, keepFactors = TRUE, family = NULL, powerBelow = -5, interaction.combine = NULL, interaction.order = NULL, i.equal = NULL, depvar = TRUE, style.tex = NULL, style.df = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, poly_dict = c("", " square", " cube"), postprocess.tex = NULL, postprocess.df = NULL, tpt = FALSE, arraystretch = NULL, adjustbox = NULL, fontsize = NULL, fit_format = "__var__", coef.just = NULL, tabular = "normal", highlight = NULL, coef.style = NULL, meta = NULL, meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, view = FALSE, export = NULL, markdown = NULL, page.width = "fit", div.class = "etable" ) setFixest_etable( digits = 4, digits.stats = 5, fitstat, coefstat = c("se", "tstat", "confint"), ci = 0.95, se.below = TRUE, keep, drop, order, dict, float, fixef_sizes = FALSE, fixef_sizes.simplify = TRUE, family, powerBelow = -5, interaction.order = NULL, depvar, style.tex = NULL, style.df = NULL, notes = NULL, group = NULL, extralines = NULL, fixef.group = NULL, placement = "htbp", drop.section = NULL, view = FALSE, markdown = NULL, view.cache = FALSE, page.width = "fit", postprocess.tex = NULL, postprocess.df = NULL, fit_format = "__var__", meta.time = NULL, meta.author = NULL, meta.sys = NULL, meta.call = NULL, meta.comment = NULL, reset = FALSE, save = FALSE ) getFixest_etable() ## S3 method for class 'etable_tex' print(x, ...) ## S3 method for class 'etable_df' print(x, ...) log_etable(type = "pdflatex")
... |
Used to capture different |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
stage |
Can be equal to |
agg |
A character scalar describing the variable names to be aggregated,
it is pattern-based. For |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
ssc |
An object of class |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
.vcov |
A function to be used to compute the standard-errors of each fixest object. You can
pass extra arguments to this function using the argument |
.vcov_args |
A list containing arguments to be passed to the function |
digits |
Integer or character scalar. Default is 4 and represents the number of significant
digits to be displayed for the coefficients and standard-errors. To apply rounding instead of
significance use, e.g., |
digits.stats |
Integer or character scalar. Default is 5 and represents the number of
significant digits to be displayed for the fit statistics. To apply rounding instead of
significance use, e.g., |
fitstat |
A character vector or a one sided formula (both with only lowercase letters). A
vector listing which fit statistics to display. The valid types are 'n', 'll', 'aic', 'bic' and
r2 types like 'r2', 'pr2', 'war2', etc (see all valid types in |
coefstat |
One of |
ci |
Level of the confidence interval, defaults to |
se.row |
Logical scalar, default is |
se.below |
Logical or |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
order |
Character vector. This element is used if the user wants the variables to be
ordered in a certain way. This should be a vector of regular expressions (see |
dict |
A named character vector or a logical scalar. It changes the original variable names
to the ones contained in the |
file |
A character scalar. If provided, the Latex (or data frame) table will be saved in a
file whose path is |
replace |
Logical, default is |
convergence |
Logical, default is missing. Should the convergence state of the algorithm be displayed? By default, convergence information is displayed if at least one model did not converge. |
signif.code |
Named numeric vector, used to provide the significance codes with respect to
the p-value of the coefficients. Default is |
headers |
Character vector or list. Adds one or more header lines in the table. A header
line can be represented by a character vector or a named list of numbers where the names are the
cell values and the numbers are the span. Example: |
fixef_sizes |
(Tex only.) Logical, default is |
fixef_sizes.simplify |
Logical, default is |
keepFactors |
Logical, default is |
family |
Logical, default is missing. Whether to display the families of the models. By default this line is displayed when at least two models are from different families. |
powerBelow |
(Tex only.) Integer, default is -5. A coefficient whose value is below
|
interaction.combine |
Character scalar, defaults to |
interaction.order |
Character vector of regular expressions. Only affects variables that
are interacted like x1 and x2 in |
i.equal |
Character scalar, defaults to |
depvar |
Logical, default is |
style.df |
An object created by the function |
group |
A list. The list elements should be vectors of regular expressions. For each
elements of this list: A new line in the table is created, all variables that are matched by the
regular expressions are discarded (same effect as the argument |
extralines |
A vector, a list or a one sided formula. The list elements should be either a
vector representing the value of each cell, a list of the form
|
fixef.group |
Logical scalar or list (default is |
drop.section |
Character vector which can be of length 0 (i.e. equal to |
poly_dict |
Character vector, default is |
postprocess.df |
A function that will postprocess.tex the resulting data.frame. Only when
|
fit_format |
Character scalar, default is |
coef.just |
(DF only.) Either |
highlight |
List containing coefficients to highlight.
Highlighting is of the form |
coef.style |
Named list containing styles to be applied to the coefficients. It must be of
the form |
export |
Character scalar giving the path to a PNG file to be created, default is |
page.width |
Character scalar equal to |
div.class |
Character scalar, default is |
title |
(Tex only.) Character scalar. The title of the Latex table. |
label |
(Tex only.) Character scalar. The label of the Latex table. |
float |
(Tex only.) Logical. By default, if the argument |
style.tex |
An object created by the function |
notes |
(Tex only.) Character vector. If provided, a |
placement |
(Tex only.) Character string giving the position of the float in Latex. Default is "htbp". It must consist of only the characters 'h', 't', 'b', 'p', 'H' and '!'. Reminder: h: here; t: top; b: bottom; p: float page; H: definitely here; !: prevents Latex to look for other positions. Note that it can be equal to the empty string (and you'll get the default placement). |
postprocess.tex |
A function that will postprocess the character vector defining the latex
table. Only when |
tpt |
(Tex only.) Logical scalar, default is FALSE. Whether to use the |
arraystretch |
(Tex only.) A numeric scalar, default is |
adjustbox |
(Tex only.) A logical, numeric or character scalar, default is |
fontsize |
(Tex only.) A character scalar, default is |
tabular |
(Tex only.) Character scalar equal to "normal" (default), |
meta |
(Tex only.) A one-sided formula that shall contain the following elements:
date or time, sys, author, comment and call. Default is |
meta.time |
(Tex only.) Either a logical scalar (default is |
meta.author |
(Tex only.) A logical scalar (default is |
meta.sys |
(Tex only.) A logical scalar, default is |
meta.call |
(Tex only.) Logical scalar, default is |
meta.comment |
(Tex only.) A character vector containing free-form comments to be inserted right before the table. |
view |
Logical, default is |
markdown |
Character scalar giving the location of a directory, or a logical scalar.
Default is |
tex |
Logical: whether the results should be a data.frame or a Latex table. By default,
this argument is |
view.cache |
Logical, default is |
reset |
( |
save |
Either a logical or equal to |
x |
An object returned by |
type |
Character scalar equal to 'pdflatex' (default), 'magick', 'dir' or 'tex'. Which log file to report; if 'tex', the full source code of the tex file is returned, if 'dir': the directory of the log files is returned. |
The function esttex is equivalent to the function etable with argument tex = TRUE.
The function esttable is equivalent to the function etable with argument tex = FALSE.
To display the table, you will need the Latex package booktabs which contains
the \\toprule, \\midrule and \\bottomrule commands.
You can permanently change the way your table looks in Latex by using setFixest_etable.
The following vignette gives an example as well as illustrates how to use the style and
postprocessing functions: Exporting estimation tables.
When the argument postprocess.tex is not missing, two additional tags will be included in the
character vector returned by etable: "%start:tab\\n" and "%end:tab\\n". These can be used
to identify the start and end of the tabular and are useful to insert code within the table
environment.
If tex = TRUE, the lines composing the Latex table are returned invisibly while the table
is directly prompted on the console.
If tex = FALSE, the data.frame is directly returned. If the argument file is not missing,
the data.frame is printed and returned invisibly.
esttable(): Exports the results of multiple fixest estimations in a Latex table.
esttex(): Exports the results of multiple fixest estimations in a Latex table.
digits handle the number of decimals displayed?The default display of decimals is the outcome of an algorithm. Let's take the example
of digits = 3 which "kind of" requires 3 significant digits to be displayed.
For numbers greater than 1 (in absolute terms), their integral part is always displayed and
the number of decimals shown is equal to digits minus the number of digits in the integral
part. This means that 12.345 will be displayed as 12.3. If the number of decimals should
be 0, then a single decimal is displayed to suggest that the number is not whole. This means
that 1234.56 will be displayed as 1234.5. Note that if the number is whole, no decimals
are shown.
For numbers lower than 1 (in absolute terms), the number of decimals displayed is equal
to digits except if there are only 0s in which case the first significant digit is shown.
This means that 0.01234 will be displayed as 0.012 (first rule), and that 0.000123 will
be displayed as 0.0001 (second rule).
The arguments keep, drop and order use regular expressions. If you are not aware
of regular expressions, I urge you to learn it, since it is an extremely powerful way
to manipulate character strings (and it exists across most programming languages).
For example drop = "Wind" would drop any variable whose name contains "Wind". Note that
variables such as "Temp:Wind" or "StrongWind" do contain "Wind", so would be dropped.
To drop only the variable named "Wind", you need to use drop = "^Wind$" (with "^" meaning
beginning, resp. "$" meaning end, of the string => this is the language of regular expressions).
Although you can combine several regular expressions in a single character string using pipes,
drop also accepts a vector of regular expressions.
You can use the special character "!" (exclamation mark) to reverse the effect of the regular
expression (this feature is specific to this function). For example drop = "!Wind" would drop
any variable that does not contain "Wind".
You can use the special character "%" (percentage) to make reference to the original variable
name instead of the aliased name. For example, you have a variable named "Month6", and use a
dictionary dict = c(Month6="June"). Thus the variable will be displayed as "June". If you
want to delete that variable, you can use either drop="June", or drop="%Month6" (which makes
reference to its original name).
The argument order takes in a vector of regular expressions, the order will follow the
elements of this vector. The vector gives a list of priorities, on the left the elements with
highest priority. For example, order = c("Wind", "!Inter", "!Temp") would give highest
priorities to the variables containing "Wind" (which would then appear first), second highest
priority is the variables not containing "Inter", last, with lowest priority, the variables not
containing "Temp". If you had the following variables: (Intercept), Temp:Wind, Wind, Temp you
would end up with the following order: Wind, Temp:Wind, Temp, (Intercept).
extralines
The argument extralines adds well... extra lines to the table. It accepts either a list, or a
one-sided formula.
For each line, you can define the values taken by each cell using 4 different ways: a) a vector, b) a list, c) a function, and d) a formula.
If a vector, it should represent the values taken by each cell. Note that if the length of the vector is smaller than the number of models, its values are recycled across models, but the length of the vector is required to be a divisor of the number of models.
If a list, it should be of the form list("item1" = #item1, "item2" = #item2, etc). For example
list("A"=2, "B"=3) leads to c("A", "A", "B", "B", "B"). Note that if the number of items is
1, you don't need to add = 1. For example list("A"=2, "B") is valid and leads to
c("A", "A", "B". As for the vector the values are recycled if necessary.
If a function, it will be applied to each model and should return a scalar (NA values
returned are accepted).
If a formula, it must be one-sided and the elements in the formula must represent either
extralines macros, either fit statistics (i.e. valid types of the function fitstat). One
new line will be added for each element of the formula. To register extralines macros, you
must first register them in extralines_register.
Finally, you can combine as many lines as wished by nesting them in a list. The names of the
nesting list are the row titles (values in the leftmost cell). For example
extralines = list(~r2, Controls = TRUE, Group = list("A"=2, "B")) will add three lines,
the titles of which are "R2", "Controls" and "Group".
The arguments group, extralines and fixef.group allow to add customized lines in the
table. They can be defined via a list where the list name will be the row name. By default, the
placement of the extra line is right after the coefficients (except for fixef.group, covered
in the last paragraph). For instance, group = list("Controls" = "x[[:digit:]]") will create a
line right after the coefficients telling which models contain the control variables.
But the placement can be customized. The previous example (of the controls) will be used for
illustration (the mechanism for extralines and fixef.group is identical).
The row names accept 2 special characters at the very start. The first character tells in which
section the line should appear: it can be equal to "^", "-", or "_", meaning respectively
the coefficients, the fixed-effects and the statistics section (which typically appear at the
top, mid and bottom of the table). The second one governs the placement of the new line within
the section: it can be equal to "^", meaning first line, or "_", meaning last line.
Let's have some examples. Using the previous example, writing "_^Controls" would place the new
line at the top of the statistics section. Writing "-_Controls" places it as the last row of
the fixed-effects section; "^^Controls" at the top row of the coefficients section; etc...
The second character is optional, the default placement being in the bottom. This means that
"_Controls" would place it at the bottom of the statistics section.
The placement in fixef.group is defined similarly, only the default placement is different.
Its default placement is at the top of the fixed-effects section.
By default on all instances (with the notable exception of the elements of style.tex)
special Latex characters are escaped. This means that title="Exports in million $." will be
exported as "Exports in million \\$.": the dollar sign will be escaped. This is true for the
following characters: &, $, %, _, ^ and #.
Note, importantly, that equations are NOT escaped. This means that
title="Functional form $a_i \\times x^b$, variation in %." will be displayed as:
"Functional form $a_i \\times x^b$, variation in \\%.": only the
last percentage will be escaped.
If for some reason you don't want the escaping to take place, the arguments headers and
extralines are the only ones allowing that. To disable escaping, add the special token
":tex:" in the row names.
Example: in headers=list(":tex:Row title"="weird & & %\\n tex stuff\\\\"),
the elements will be displayed verbatim. Of course, since it can easily ruin your table,
it is only recommended to super users.
Within anything that is Latex-escaped (see previous section), you can use a markdown-style
markup to put the text in italic and/or bold. Use *text*, **text** or ***text*** to
put some text in, respectively, italic (with \\textit), bold (with \\textbf) and italic-bold.
The markup can be escaped by using an backslash first. For example "***This: \\***, are three stars***" will leave the three stars in the middle untouched.
Laurent Berge
For styling the table: setFixest_etable, style.tex, style.df.
See also the main estimation functions femlm, feols or feglm.
Use summary.fixest
to see the results with the appropriate standard-errors, fixef.fixest to extract the
fixed-effects coefficients.
est1 = feols(Ozone ~ i(Month) / Wind + Temp, data = airquality) est2 = feols(Ozone ~ i(Month, Wind) + Temp | Month, data = airquality) # Displaying the two results in a single table etable(est1, est2) # keep/drop: keeping only interactions etable(est1, est2, keep = " x ") # or using drop (see regexp help): etable(est1, est2, drop = "^(Month|Temp|\\()") # keep/drop: dropping interactions etable(est1, est2, drop = " x ") # or using keep ("!" reverses the effect): etable(est1, est2, keep = "! x ") # order: Wind variable first, intercept last (note the "!" to reverse the effect) etable(est1, est2, order = c("Wind", "!Inter")) # Month, then interactions, then the rest etable(est1, est2, order = c("^Month", " x ")) # # dict # # You can rename variables with dict = c(var1 = alias1, var2 = alias2, etc) # You can also rename values taken by factors. # Here's a full example: dict = c(Temp = "Temperature", "Month::5"="May", "6"="Jun") etable(est1, est2, dict = dict) # Note the difference of treatment between Jun and May # Assume the following dictionary: dict = c("Month::5"="May", "Month::6"="Jun", "Month::7"="Jul", "Month::8"="Aug", "Month::9"="Sep") # We would like to keep only the Months, but now the names are all changed... # How to do? # We can use the special character '%' to make reference to the original names. etable(est1, est2, dict = dict, keep = "%Month") # # signif.code # etable(est1, est2, signif.code = c(" A"=0.01, " B"=0.05, " C"=0.1, " D"=0.15, " F"=1)) # # Using the argument style to customize Latex exports # # If you don't like the default layout of the table, no worries! # You can modify many parameters with the argument style # To drop the headers before each section, use: # Note that a space adds an extra line style_noHeaders = style.tex(var.title = "", fixef.title = "", stats.title = " ") etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_noHeaders) # To change the lines of the table + dropping the table footer style_lines = style.tex(line.top = "\\toprule", line.bottom = "\\bottomrule", tablefoot = FALSE) etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_lines) # Or you have the predefined type "aer" etable(est1, est2, dict = dict, tex = TRUE, style.tex = style.tex("aer")) # # Group and extralines # # Sometimes it's useful to group control variables into a single line # You can achieve that with the group argument setFixest_fml(..ctrl = ~ poly(Wind, 2) + poly(Temp, 2)) est_c0 = feols(Ozone ~ Solar.R, data = airquality) est_c1 = feols(Ozone ~ Solar.R + ..ctrl, data = airquality) est_c2 = feols(Ozone ~ Solar.R + Solar.R^2 + ..ctrl, data = airquality) etable(est_c0, est_c1, est_c2, group = list(Controls = "poly")) # 'group' here does the same as drop = "poly", but adds an extra line # with TRUE/FALSE where the variables were found # 'extralines' adds an extra line, where you can add the value for each model est_all = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality) est_sub1 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month %in% 5:6) est_sub2 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month %in% 7:8) est_sub3 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month == 9) etable(est_all, est_sub1, est_sub2, est_sub3, extralines = list("Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept."))) # You can monitor the placement of the new lines with two special characters # at the beginning of the row name. # 1) "^", "-" or "_" which mean the coefficients, the fixed-effects or the # statistics section. # 2) "^" or "_" which mean first or last line of the section # # Ex: starting with "_^" will place the line at the top of the stat. section # starting with "-_" will place the line at the bottom of the FEs section # etc. # # You can use a single character which will represent the section, # the line would then appear at the bottom of the section. # Examples etable(est_c0, est_c1, est_c2, group = list("_Controls" = "poly")) etable(est_all, est_sub1, est_sub2, est_sub3, extralines = list("^^Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept."))) # # headers # # You can add header lines with 'headers' # These lines will appear at the top of the table # first, 3 estimations est_header = feols(c(Ozone, Solar.R, Wind) ~ poly(Temp, 2), airquality) # header => vector: adds a line w/t title etable(est_header, headers = c("A", "A", "B")) # header => list: identical way to do the previous header # The form is: list(item1 = #item1, item2 = #item2, etc) etable(est_header, headers = list("A" = 2, "B" = 1)) # Adding a title + # when an element is to be repeated only once, you can avoid the "= 1": etable(est_header, headers = list(Group = list("A" = 2, "B"))) # To change the placement, add as first character: # - "^" => top # - "-" => mid (default) # - "_" => bottom # Note that "mid" and "top" are only distinguished when tex = TRUE # Placing the new header line at the bottom etable(est_header, headers = list("_Group" = c("A", "A", "B"), "^Currency" = list("US $" = 2, "CA $" = 1))) # In Latex, you can add "grouped underlines" (cmidrule from the booktabs package) # by adding ":_:" in the title: etable(est_header, tex = TRUE, headers = list("^:_:Group" = c("A", "A", "B"))) # # extralines and headers: .() for list() # # In the two arguments extralines and headers, .() can be used for list() # For example: etable(est_header, headers = .("^Currency" = .("US $" = 2, "CA $" = 1))) # # fixef.group # # You can group the fixed-effects line with fixef.group est_0fe = feols(Ozone ~ Solar.R + Temp + Wind, airquality) est_1fe = feols(Ozone ~ Solar.R + Temp + Wind | Month, airquality) est_2fe = feols(Ozone ~ Solar.R + Temp + Wind | Month + Day, airquality) # A) automatic way => simply use fixef.group = TRUE etable(est_0fe, est_2fe, fixef.group = TRUE) # Note that when grouping would lead to inconsistencies across models, # it is avoided etable(est_0fe, est_1fe, est_2fe, fixef.group = TRUE) # B) customized way => use a list etable(est_0fe, est_2fe, fixef.group = list("Dates" = "Month|Day")) # Note that when a user grouping would lead to inconsistencies, # the term partial replaces yes/no and the fixed-effects are not removed. etable(est_0fe, est_1fe, est_2fe, fixef.group = list("Dates" = "Month|Day")) # Using customized placement => as with 'group' and 'extralines', # the user can control the placement of the new line. # See the previous 'group' examples and the dedicated section in the help. # On top of the coefficients: etable(est_0fe, est_2fe, fixef.group = list("^^Dates" = "Month|Day")) # Last line of the statistics etable(est_0fe, est_2fe, fixef.group = list("_Dates" = "Month|Day")) # # Using custom functions to compute the standard errors # # You can use external functions to compute the VCOVs # by feeding functions in the 'vcov' argument. # Let's use some covariances from the sandwich package etable(est_c0, est_c1, est_c2, vcov = sandwich::vcovHC) # To add extra arguments to vcovHC, you need to write your wrapper: etable(est_c0, est_c1, est_c2, vcov = function(x) sandwich::vcovHC(x, type = "HC0")) # # Customize which fit statistic to display # # You can change the fit statistics with the argument fitstat # and you can rename them with the dictionary etable(est1, est2, fitstat = ~ r2 + n + G) # If you use a formula, '.' means the default: etable(est1, est2, fitstat = ~ ll + .) # # Computing a different SE for each model # est = feols(Ozone ~ Solar.R + Wind + Temp, data = airquality) # # Method 1: use summary s1 = summary(est, "iid") s2 = summary(est, cluster = ~ Month) s3 = summary(est, cluster = ~ Day) s4 = summary(est, cluster = ~ Day + Month) etable(list(s1, s2, s3, s4)) # # Method 2: using a list in the argument 'vcov' est_bis = feols(Ozone ~ Solar.R + Wind + Temp | Month, data = airquality) etable(est, est_bis, vcov = list("hetero", ~ Month)) # When you have only one model, this model is replicated # along the elements of the vcov list. etable(est, vcov = list("hetero", ~ Month)) # # Method 3: Using "each" or "times" in vcov # If the first element of the list in 'vcov' is "each" or "times", # then all models will be replicated and all the VCOVs will be # applied to each model. The order in which they are replicated # are governed by the each/times keywords. # each etable(est, est_bis, vcov = list("each", "iid", ~ Month, ~ Day)) # times etable(est, est_bis, vcov = list("times", "iid", ~ Month, ~ Day)) # # Notes and markup # # Notes can be also be set in a dictionary # You can use markdown markup to put text into italic/bold dict = c("note 1" = "*Notes:* This data is not really random.", "source 1" = "**Source:** the internet?") est = feols(Ozone ~ csw(Solar.R, Wind, Temp), data = airquality) etable(est, dict = dict, tex = TRUE, notes = c("note 1", "source 1"))est1 = feols(Ozone ~ i(Month) / Wind + Temp, data = airquality) est2 = feols(Ozone ~ i(Month, Wind) + Temp | Month, data = airquality) # Displaying the two results in a single table etable(est1, est2) # keep/drop: keeping only interactions etable(est1, est2, keep = " x ") # or using drop (see regexp help): etable(est1, est2, drop = "^(Month|Temp|\\()") # keep/drop: dropping interactions etable(est1, est2, drop = " x ") # or using keep ("!" reverses the effect): etable(est1, est2, keep = "! x ") # order: Wind variable first, intercept last (note the "!" to reverse the effect) etable(est1, est2, order = c("Wind", "!Inter")) # Month, then interactions, then the rest etable(est1, est2, order = c("^Month", " x ")) # # dict # # You can rename variables with dict = c(var1 = alias1, var2 = alias2, etc) # You can also rename values taken by factors. # Here's a full example: dict = c(Temp = "Temperature", "Month::5"="May", "6"="Jun") etable(est1, est2, dict = dict) # Note the difference of treatment between Jun and May # Assume the following dictionary: dict = c("Month::5"="May", "Month::6"="Jun", "Month::7"="Jul", "Month::8"="Aug", "Month::9"="Sep") # We would like to keep only the Months, but now the names are all changed... # How to do? # We can use the special character '%' to make reference to the original names. etable(est1, est2, dict = dict, keep = "%Month") # # signif.code # etable(est1, est2, signif.code = c(" A"=0.01, " B"=0.05, " C"=0.1, " D"=0.15, " F"=1)) # # Using the argument style to customize Latex exports # # If you don't like the default layout of the table, no worries! # You can modify many parameters with the argument style # To drop the headers before each section, use: # Note that a space adds an extra line style_noHeaders = style.tex(var.title = "", fixef.title = "", stats.title = " ") etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_noHeaders) # To change the lines of the table + dropping the table footer style_lines = style.tex(line.top = "\\toprule", line.bottom = "\\bottomrule", tablefoot = FALSE) etable(est1, est2, dict = dict, tex = TRUE, style.tex = style_lines) # Or you have the predefined type "aer" etable(est1, est2, dict = dict, tex = TRUE, style.tex = style.tex("aer")) # # Group and extralines # # Sometimes it's useful to group control variables into a single line # You can achieve that with the group argument setFixest_fml(..ctrl = ~ poly(Wind, 2) + poly(Temp, 2)) est_c0 = feols(Ozone ~ Solar.R, data = airquality) est_c1 = feols(Ozone ~ Solar.R + ..ctrl, data = airquality) est_c2 = feols(Ozone ~ Solar.R + Solar.R^2 + ..ctrl, data = airquality) etable(est_c0, est_c1, est_c2, group = list(Controls = "poly")) # 'group' here does the same as drop = "poly", but adds an extra line # with TRUE/FALSE where the variables were found # 'extralines' adds an extra line, where you can add the value for each model est_all = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality) est_sub1 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month %in% 5:6) est_sub2 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month %in% 7:8) est_sub3 = feols(Ozone ~ Solar.R + Temp + Wind, data = airquality, subset = ~ Month == 9) etable(est_all, est_sub1, est_sub2, est_sub3, extralines = list("Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept."))) # You can monitor the placement of the new lines with two special characters # at the beginning of the row name. # 1) "^", "-" or "_" which mean the coefficients, the fixed-effects or the # statistics section. # 2) "^" or "_" which mean first or last line of the section # # Ex: starting with "_^" will place the line at the top of the stat. section # starting with "-_" will place the line at the bottom of the FEs section # etc. # # You can use a single character which will represent the section, # the line would then appear at the bottom of the section. # Examples etable(est_c0, est_c1, est_c2, group = list("_Controls" = "poly")) etable(est_all, est_sub1, est_sub2, est_sub3, extralines = list("^^Sub-sample" = c("All", "May-June", "Jul.-Aug.", "Sept."))) # # headers # # You can add header lines with 'headers' # These lines will appear at the top of the table # first, 3 estimations est_header = feols(c(Ozone, Solar.R, Wind) ~ poly(Temp, 2), airquality) # header => vector: adds a line w/t title etable(est_header, headers = c("A", "A", "B")) # header => list: identical way to do the previous header # The form is: list(item1 = #item1, item2 = #item2, etc) etable(est_header, headers = list("A" = 2, "B" = 1)) # Adding a title + # when an element is to be repeated only once, you can avoid the "= 1": etable(est_header, headers = list(Group = list("A" = 2, "B"))) # To change the placement, add as first character: # - "^" => top # - "-" => mid (default) # - "_" => bottom # Note that "mid" and "top" are only distinguished when tex = TRUE # Placing the new header line at the bottom etable(est_header, headers = list("_Group" = c("A", "A", "B"), "^Currency" = list("US $" = 2, "CA $" = 1))) # In Latex, you can add "grouped underlines" (cmidrule from the booktabs package) # by adding ":_:" in the title: etable(est_header, tex = TRUE, headers = list("^:_:Group" = c("A", "A", "B"))) # # extralines and headers: .() for list() # # In the two arguments extralines and headers, .() can be used for list() # For example: etable(est_header, headers = .("^Currency" = .("US $" = 2, "CA $" = 1))) # # fixef.group # # You can group the fixed-effects line with fixef.group est_0fe = feols(Ozone ~ Solar.R + Temp + Wind, airquality) est_1fe = feols(Ozone ~ Solar.R + Temp + Wind | Month, airquality) est_2fe = feols(Ozone ~ Solar.R + Temp + Wind | Month + Day, airquality) # A) automatic way => simply use fixef.group = TRUE etable(est_0fe, est_2fe, fixef.group = TRUE) # Note that when grouping would lead to inconsistencies across models, # it is avoided etable(est_0fe, est_1fe, est_2fe, fixef.group = TRUE) # B) customized way => use a list etable(est_0fe, est_2fe, fixef.group = list("Dates" = "Month|Day")) # Note that when a user grouping would lead to inconsistencies, # the term partial replaces yes/no and the fixed-effects are not removed. etable(est_0fe, est_1fe, est_2fe, fixef.group = list("Dates" = "Month|Day")) # Using customized placement => as with 'group' and 'extralines', # the user can control the placement of the new line. # See the previous 'group' examples and the dedicated section in the help. # On top of the coefficients: etable(est_0fe, est_2fe, fixef.group = list("^^Dates" = "Month|Day")) # Last line of the statistics etable(est_0fe, est_2fe, fixef.group = list("_Dates" = "Month|Day")) # # Using custom functions to compute the standard errors # # You can use external functions to compute the VCOVs # by feeding functions in the 'vcov' argument. # Let's use some covariances from the sandwich package etable(est_c0, est_c1, est_c2, vcov = sandwich::vcovHC) # To add extra arguments to vcovHC, you need to write your wrapper: etable(est_c0, est_c1, est_c2, vcov = function(x) sandwich::vcovHC(x, type = "HC0")) # # Customize which fit statistic to display # # You can change the fit statistics with the argument fitstat # and you can rename them with the dictionary etable(est1, est2, fitstat = ~ r2 + n + G) # If you use a formula, '.' means the default: etable(est1, est2, fitstat = ~ ll + .) # # Computing a different SE for each model # est = feols(Ozone ~ Solar.R + Wind + Temp, data = airquality) # # Method 1: use summary s1 = summary(est, "iid") s2 = summary(est, cluster = ~ Month) s3 = summary(est, cluster = ~ Day) s4 = summary(est, cluster = ~ Day + Month) etable(list(s1, s2, s3, s4)) # # Method 2: using a list in the argument 'vcov' est_bis = feols(Ozone ~ Solar.R + Wind + Temp | Month, data = airquality) etable(est, est_bis, vcov = list("hetero", ~ Month)) # When you have only one model, this model is replicated # along the elements of the vcov list. etable(est, vcov = list("hetero", ~ Month)) # # Method 3: Using "each" or "times" in vcov # If the first element of the list in 'vcov' is "each" or "times", # then all models will be replicated and all the VCOVs will be # applied to each model. The order in which they are replicated # are governed by the each/times keywords. # each etable(est, est_bis, vcov = list("each", "iid", ~ Month, ~ Day)) # times etable(est, est_bis, vcov = list("times", "iid", ~ Month, ~ Day)) # # Notes and markup # # Notes can be also be set in a dictionary # You can use markdown markup to put text into italic/bold dict = c("note 1" = "*Notes:* This data is not really random.", "source 1" = "**Source:** the internet?") est = feols(Ozone ~ csw(Solar.R, Wind, Temp), data = airquality) etable(est, dict = dict, tex = TRUE, notes = c("note 1", "source 1"))
extralines macros to be used in etable
This function is used to create extralines (which is an argument of etable) macros
that can be easily summoned in etable.
extralines_register(type, fun, alias)extralines_register(type, fun, alias)
type |
A character scalar giving the type-name. |
fun |
A function to be applied to a |
alias |
A character scalar. This is the alias to be used in lieu of the type name to form the row name. |
You can register as many macros as you wish, the only constraint is that the type name should not conflict with a fitstat type name.
# We register a function computing the standard-deviation of the dependent variable my_fun = function(x) sd(model.matrix(x, type = "lhs")) extralines_register("sdy", my_fun, "SD(y)") # An estimation data(iris) est = feols(Petal.Length ~ Sepal.Length | Species, iris) # Now we can easily create a row with the SD of y. # We just "summon" it in a one-sided formula etable(est, extralines = ~ sdy) # We can change the alias on the fly: etable(est, extralines = list("_Standard deviation of the dep. var." = ~ sdy))# We register a function computing the standard-deviation of the dependent variable my_fun = function(x) sd(model.matrix(x, type = "lhs")) extralines_register("sdy", my_fun, "SD(y)") # An estimation data(iris) est = feols(Petal.Length ~ Sepal.Length | Species, iris) # Now we can easily create a row with the SD of y. # We just "summon" it in a one-sided formula etable(est, extralines = ~ sdy) # We can change the alias on the fly: etable(est, extralines = list("_Standard deviation of the dep. var." = ~ sdy))
fixest estimationProduce lags or leads in the formulas of fixest estimations or when creating variables in
a data.table::data.table. The data must be set as a panel beforehand (either with
the function panel or with the argument panel.id in the estimation).
f(x, k = 1, fill = NA) d(x, k = 1, fill = NA) l(x, k = 1, fill = NA)f(x, k = 1, fill = NA) d(x, k = 1, fill = NA) l(x, k = 1, fill = NA)
x |
The variable. |
k |
A vector of integers giving the number of lags (for |
fill |
A scalar, default is |
These functions can only be used i) in a formula of a fixest estimation, or ii) when
creating variables within a fixest_panel object (obtained with function panel) which
is alaos a data.table::data.table.
f(): Forwards a variable (inverse of lagging) in a fixest estimation
d(): Creates differences (i.e. x - lag(x)) in a fixest estimation
The function panel changes data.frames into a panel from which the functions l
and f can be called. Otherwise you can set the panel 'live' during the estimation using
the argument panel.id (see for example in the function feols).
data(base_did) # Setting a data set as a panel... pdat = panel(base_did, ~ id + period) # ...then using the functions l and f est1 = feols(y ~ l(x1, 0:1), pdat) est2 = feols(f(y) ~ l(x1, -1:1), pdat) est3 = feols(l(y) ~ l(x1, 0:3), pdat) etable(est1, est2, est3, order = c("f", "^x"), drop = "Int") # or using the argument panel.id feols(f(y) ~ l(x1, -1:1), base_did, panel.id = ~id + period) feols(d(y) ~ d(x1), base_did, panel.id = ~id + period) # l() and f() can also be used within a data.table: if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Now since pdat_dt is also a data.table # you can create lags/leads directly pdat_dt[, x1_l1 := l(x1)] pdat_dt[, x1_d1 := d(x1)] pdat_dt[, c("x1_l1_fill0", "y_f2") := .(l(x1, fill = 0), f(y, 2))] }data(base_did) # Setting a data set as a panel... pdat = panel(base_did, ~ id + period) # ...then using the functions l and f est1 = feols(y ~ l(x1, 0:1), pdat) est2 = feols(f(y) ~ l(x1, -1:1), pdat) est3 = feols(l(y) ~ l(x1, 0:3), pdat) etable(est1, est2, est3, order = c("f", "^x"), drop = "Int") # or using the argument panel.id feols(f(y) ~ l(x1, -1:1), base_did, panel.id = ~id + period) feols(d(y) ~ d(x1), base_did, panel.id = ~id + period) # l() and f() can also be used within a data.table: if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Now since pdat_dt is also a data.table # you can create lags/leads directly pdat_dt[, x1_l1 := l(x1)] pdat_dt[, x1_d1 := d(x1)] pdat_dt[, c("x1_l1_fill0", "y_f2") := .(l(x1, fill = 0), f(y, 2))] }
Prints the dimension of a data set, in an user-readable way
fdim(x)fdim(x)
x |
An R object, usually a data.frame (but can also be a vector). |
It does not return anything, the output is directly printed on the console.
Laurent Berge
fdim(iris) fdim(iris$Species)fdim(iris) fdim(iris$Species)
Estimates GLM models with any number of fixed-effects.
feglm( fml, data, family = "gaussian", vcov, offset, weights, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = NULL, etastart = NULL, mustart = NULL, fixef, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), verbose = 0, only.coef = FALSE, data.save = FALSE, combine.quick, mem.clean = FALSE, only.env = FALSE, env, ... ) feglm.fit( y, X, fixef_df, family = "gaussian", vcov, offset, split, fsplit, split.keep, split.drop, cluster, se, ssc, weights, subset, start = NULL, etastart = NULL, mustart = NULL, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), mem.clean = FALSE, verbose = 0, only.env = FALSE, only.coef = FALSE, env, ... ) fepois( fml, data, vcov, offset, weights, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = NULL, etastart = NULL, mustart = NULL, fixef, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), verbose = 0, combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )feglm( fml, data, family = "gaussian", vcov, offset, weights, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = NULL, etastart = NULL, mustart = NULL, fixef, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), verbose = 0, only.coef = FALSE, data.save = FALSE, combine.quick, mem.clean = FALSE, only.env = FALSE, env, ... ) feglm.fit( y, X, fixef_df, family = "gaussian", vcov, offset, split, fsplit, split.keep, split.drop, cluster, se, ssc, weights, subset, start = NULL, etastart = NULL, mustart = NULL, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), mem.clean = FALSE, verbose = 0, only.env = FALSE, only.coef = FALSE, env, ... ) fepois( fml, data, vcov, offset, weights, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = NULL, etastart = NULL, mustart = NULL, fixef, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, glm.iter = 25, glm.tol = 1e-08, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), verbose = 0, combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )
fml |
A formula representing the relation to be estimated. For example: |
data |
A data.frame containing the necessary variables to run the model.
The variables of the non-linear right hand side of the formula are identified
with this |
family |
Family to be used for the estimation. Defaults to |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
offset |
A formula or a numeric vector. An offset can be added to the estimation.
If equal to a formula, it should be of the form (for example) |
weights |
A formula or a numeric vector. Each observation can be weighted,
the weights must be greater than 0. If equal to a formula, it should be one-sided:
for example |
subset |
A vector (logical or numeric) or a one-sided formula. If provided, then the estimation will be performed only on the observations defined by this argument. |
split |
A one sided formula representing a variable (eg |
fsplit |
A one sided formula representing a variable (eg |
split.keep |
A character vector. Only used when |
split.drop |
A character vector. Only used when |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
ssc |
An object of class |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
start |
Starting values for the coefficients. Can be: i) a numeric of length 1
(e.g. |
etastart |
Numeric vector of the same length as the data. Starting values for the linear predictor. Default is missing. |
mustart |
Numeric vector of the same length as the data. Starting values for the vector of means. Default is missing. |
fixef |
Character vector. The names of variables to be used as fixed-effects. These variables should contain the identifier of each observation (e.g., think of it as a panel identifier). Note that the recommended way to include fixed-effects is to insert them directly in the formula. |
fixef.rm |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. Note, importantly, that singletons are removed in just one pass, there is no recursivity implemented. The meaning of "both" and "none" is direct. |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
fixef.algo |
|
collin.tol |
Numeric scalar, default is |
glm.iter |
Number of iterations of the glm algorithm. Default is 25. |
glm.tol |
Tolerance level for the glm algorithm. Default is |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
lean |
Logical, default is |
warn |
Logical, default is |
notes |
Logical. By default, three notes are displayed: when NAs are removed,
when some fixed-effects are removed because of only 0 (or 0/1) outcomes, or when a
variable is dropped because of collinearity. To avoid displaying these messages,
you can set |
verbose |
Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algoritmh (the first number is the left-hand-side, the other numbers are the right-hand-side variables). It can also detail the step-halving algorithm. |
only.coef |
Logical, default is |
data.save |
Logical scalar, default is |
combine.quick |
Logical. When you combine different variables to transform them
into a single fixed-effects you can do e.g. |
mem.clean |
Logical, default is |
only.env |
(Advanced users.) Logical, default is |
env |
(Advanced users.) A |
... |
Not currently used. |
y |
Numeric vector/matrix/data.frame of the dependent variable(s). Multiple dependent
variables will return a |
X |
Numeric matrix of the regressors. |
fixef_df |
Matrix/data.frame of the fixed-effects. |
The core of the GLM are the weighted OLS estimations. These estimations are performed
with feols. The method used to demean each variable along the fixed-effects
is based on Berge (2018), since this is the same problem to solve as for the Gaussian
case in a ML setup.
A fixest object. Note that fixest objects contain many elements and most of them
are for internal use, they are presented here only for information. To access them,
it is safer to use the user-level methods (e.g. vcov.fixest, resid.fixest,
etc) or functions (like for instance fitstat to access any fit statistic).
nobs |
The number of observations. |
fml |
The linear formula of the call. |
call |
The call of the function. |
method |
The method used to estimate the model. |
family |
The family used to estimate the model. |
data |
The original data set used when calling the function. Only available when
the estimation was called with |
fml_all |
A list containing different parts of the formula. Always contain the
linear formula. Then, if relevant: |
nparams |
The number of parameters of the model. |
fixef_vars |
The names of each fixed-effect dimension. |
fixef_id |
The list (of length the number of fixed-effects) of the fixed-effects identifiers for each observation. |
fixef_sizes |
The size of each fixed-effect (i.e. the number of unique identifier for each fixed-effect dimension). |
y |
(When relevant.) The dependent variable (used to compute the within-R2 when fixed-effects are present). |
convStatus |
Logical, convergence status of the IRWLS algorithm. |
irls_weights |
The weights of the last iteration of the IRWLS algorithm. |
obs_selection |
(When relevant.) List containing vectors of integers. It represents the sequential selection of observation vis a vis the original data set. |
fixef_removed |
(When relevant.) In the case there were fixed-effects and some observations were removed because of only 0/1 outcome within a fixed-effect, it gives the list (for each fixed-effect dimension) of the fixed-effect identifiers that were removed. |
coefficients |
The named vector of estimated coefficients. |
coeftable |
The table of the coefficients with their standard errors, z-values and p-values. |
loglik |
The loglikelihood. |
deviance |
Deviance of the fitted model. |
iterations |
Number of iterations of the algorithm. |
ll_null |
Log-likelihood of the null model (i.e. with the intercept only). |
ssr_null |
Sum of the squared residuals of the null model (containing only with the intercept). |
pseudo_r2 |
The adjusted pseudo R2. |
fitted.values |
The fitted values are the expected value of the dependent
variable for the fitted model: that is |
linear.predictors |
The linear predictors. |
residuals |
The residuals (y minus the fitted values). |
sq.cor |
Squared correlation between the dependent variable and the expected predictor (i.e. fitted.values) obtained by the estimation. |
hessian |
The Hessian of the parameters. |
cov.iid |
The variance-covariance matrix of the parameters. |
se |
The standard-error of the parameters. |
scores |
The matrix of the scores (first derivative for each observation). |
residuals |
The difference between the dependent variable and the expected predictor. |
sumFE |
The sum of the fixed-effects coefficients for each observation. |
offset |
(When relevant.) The offset formula. |
weights |
(When relevant.) The weights formula. |
collin.var |
(When relevant.) Vector containing the variables removed because of collinearity. |
collin.coef |
(When relevant.) Vector of coefficients, where the values of the variables removed because of collinearity are NA. |
You can combine two variables to make it a new fixed-effect using ^.
The syntax is as follows: fe_1^fe_2. Here you created a new variable which is the combination
of the two variables fe_1 and fe_2. This is identical to doing paste0(fe_1, "_", fe_2)
but more convenient.
Note that pasting is a costly operation, especially for large data sets.
Thus, the internal algorithm uses a numerical trick which is fast, but the drawback is
that the identity of each observation is lost (i.e. they are now equal to a meaningless
number instead of being equal to paste0(fe_1, "_", fe_2)). These “identities”
are useful only if you're interested in the value of the fixed-effects (that you can
extract with fixef.fixest). If you're only interested in coefficients of the variables,
it doesn't matter. Anyway, you can use combine.quick = FALSE to tell the internal
algorithm to use paste instead of the numerical trick. By default, the numerical
trick is performed only for large data sets.
You can add variables with varying slopes in the fixed-effect part of the formula.
The syntax is as follows: fixef_var[var1, var2]. Here the variables var1 and var2 will
be with varying slopes (one slope per value in fixef_var) and the fixed-effect
fixef_var will also be added.
To add only the variables with varying slopes and not the fixed-effect,
use double square brackets: fixef_var[[var1, var2]].
In other words:
fixef_var[var1, var2] is equivalent to fixef_var + fixef_var[[var1]] + fixef_var[[var2]]
fixef_var[[var1, var2]] is equivalent to fixef_var[[var1]] + fixef_var[[var2]]
In general, for convergence reasons, it is recommended to always add the fixed-effect and avoid using only the variable with varying slope (i.e. use single square brackets).
To use leads/lags of variables in the estimation, you can: i) either provide the argument
panel.id, ii) either set your data set as a panel with the function
panel, f and d.
You can provide several leads/lags/differences at once: e.g. if your formula is equal to
f(y) ~ l(x, -1:1), it means that the dependent variable is equal to the lead of y,
and you will have as explanatory variables the lead of x1, x1 and the lag of x1.
See the examples in function l for more details.
You can interact a numeric variable with a "factor-like" variable by using
i(factor_var, continuous_var, ref), where continuous_var will be interacted with
each value of factor_var and the argument ref is a value of factor_var
taken as a reference (optional).
Using this specific way to create interactions leads to a different display of the
interacted values in etable. See examples.
It is important to note that if you do not care about the standard-errors of
the interactions, then you can add interactions in the fixed-effects part of the formula,
it will be incomparably faster (using the syntax factor_var[continuous_var], as explained
in the section “Varying slopes”).
The function i has in fact more arguments, please see details in its associated help page.
Standard-errors can be computed in different ways, you can use the arguments se and ssc
in summary.fixest to define how to compute them. By default, in the presence
of fixed-effects, standard-errors are automatically clustered.
The following vignette: On standard-errors describes in details how the standard-errors are computed in
fixest and how you can replicate standard-errors from other software.
You can use the functions setFixest_vcov and setFixest_ssc to
permanently set the way the standard-errors are computed.
Multiple estimations can be performed at once, they just have to be specified in the formula.
Multiple estimations yield a fixest_multi object which is ‘kind of’ a list of
all the results but includes specific methods to access the results in a handy way.
Please have a look at the dedicated vignette:
Multiple estimations.
To include multiple dependent variables, wrap them in c() (list() also works).
For instance fml = c(y1, y2) ~ x1 would estimate the model fml = y1 ~ x1 and
then the model fml = y2 ~ x1.
To include multiple independent variables, you need to use the stepwise functions.
There are 4 stepwise functions: sw, sw0, csw, csw0, and mvsw. Of course sw
stands for stepwise, and csw for cumulative stepwise. Finally mvsw is a bit special,
it stands for multiverse stepwise. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3).
The stepwise function sw will estimate the following two models: y ~ x1 + x2 and
y ~ x1 + x3. That is, each element in sw() is sequentially, and separately,
added to the formula. Would have you used sw0 in lieu of sw, then the model
y ~ x1 would also have been estimated. The 0 in the name means that the model
without any stepwise element also needs to be estimated.
The prefix c means cumulative: each stepwise element is added to the next. That is,
fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2 and
y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model without
the stepwise elements to be estimated: in other words, fml = y ~ x1 + csw0(x2, x3)
leads to the following three models: y ~ x1, y ~ x1 + x2 and y ~ x1 + x2 + x3.
Finally mvsw will add, in a stepwise fashion all possible combinations of the variables
in its arguments. For example mvsw(x1, x2, x3) is equivalent to
sw0(x1, x2, x3, x1 + x2, x1 + x3, x2 + x3, x1 + x2 + x3). The number of models
to estimate grows at a factorial rate: so be cautious!
Multiple independent variables can be combined with multiple dependent variables, as in
fml = c(y1, y2) ~ cw(x1, x2, x3) which would lead to 6 estimations. Multiple
estimations can also be combined to split samples (with the arguments split, fsplit).
You can also add fixed-effects in a stepwise fashion. Note that you cannot perform
stepwise estimations on the IV part of the formula (feols only).
If NAs are present in the sample, to avoid too many messages, only NA removal concerning the variables common to all estimations is reported.
A note on performance. The feature of multiple estimations has been highly optimized for
feols, in particular in the presence of fixed-effects. It is faster to estimate
multiple models using the formula rather than with a loop. For non-feols models using
the formula is roughly similar to using a loop performance-wise.
When the data set has been set up globally using
setFixest_estimation(data = data_set), the argument vcov can be used implicitly.
This means that calls such as feols(y ~ x, "HC1"), or feols(y ~ x, ~id), are valid:
i) the data is automatically deduced from the global settings, and ii) the vcov
is deduced to be the second argument.
Although the argument 'data' is placed in second position, the data can be piped to the
estimation functions. For example, with R >= 4.1, mtcars |> feols(mpg ~ cyl) works as
feols(mpg ~ cyl, mtcars).
To use multiple dependent variables in fixest estimations, you need to include them
in a vector: like in c(y1, y2, y3).
First, if names are stored in a vector, they can readily be inserted in a formula to
perform multiple estimations using the dot square bracket operator. For instance if
my_lhs = c("y1", "y2"), calling fixest with, say feols(.[my_lhs] ~ x1, etc) is
equivalent to using feols(c(y1, y2) ~ x1, etc). Beware that this is a special feature
unique to the left-hand-side of fixest estimations (the default behavior of the DSB
operator is to aggregate with sums, see xpd).
Second, you can use a regular expression to grep the left-hand-sides on the fly. When the
..("regex") feature is used naked on the LHS, the variables grepped are inserted into
c(). For example ..("Pe") ~ Sepal.Length, iris is equivalent to
c(Petal.Length, Petal.Width) ~ Sepal.Length, iris. Beware that this is a
special feature unique to the left-hand-side of fixest estimations
(the default behavior of ..("regex") is to aggregate with sums, see xpd).
In a formula, the dot square bracket (DSB) operator can: i) create manifold variables at once, or ii) capture values from the current environment and put them verbatim in the formula.
Say you want to include the variables x1 to x3 in your formula. You can use
xpd(y ~ x.[1:3]) and you'll get y ~ x1 + x2 + x3.
To summon values from the environment, simply put the variable in square brackets. For example:
for(i in 1:3) xpd(y.[i] ~ x) will create the formulas y1 ~ x to y3 ~ x depending on the
value of i.
You can include a full variable from the environment in the same way:
for(y in c("a", "b")) xpd(.[y] ~ x) will create the two formulas a ~ x and b ~ x.
The DSB can even be used within variable names, but then the variable must be nested in
character form. For example y ~ .["x.[1:2]_sq"] will create y ~ x1_sq + x2_sq. Using the
character form is important to avoid a formula parsing error. Double quotes must be used. Note
that the character string that is nested will be parsed with the function dsb, and thus it
will return a vector.
By default, the DSB operator expands vectors into sums. You can add a comma, like in .[, x],
to expand with commas–the content can then be used within functions. For instance:
c(x.[, 1:2]) will create c(x1, x2) (and not c(x1 + x2)).
In all fixest estimations, this special parsing is enabled, so you don't need to use xpd.
One-sided formulas can be expanded with the DSB operator: let x = ~sepal + petal, then
xpd(y ~ .[x]) leads to color ~ sepal + petal.
You can even use multiple square brackets within a single variable, but then the use of nesting
is required. For example, the following xpd(y ~ .[".[letters[1:2]]_.[1:2]"]) will create
y ~ a_1 + b_2. Remember that the nested character string is parsed with dsb,
which explains this behavior.
When the element to be expanded i) is equal to the empty string or, ii) is of length 0, it is
replaced with a neutral element, namely 1. For example, x = "" ; xpd(y ~ .[x]) leads to
y ~ 1.
Laurent Berge
Berge, Laurent, 2018, "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm." CREA Discussion Papers, 13 ().
For models with multiple fixed-effects:
Gaure, Simen, 2013, "OLS with multiple high dimensional category variables", Computational Statistics & Data Analysis 66 pp. 8–18
See also summary.fixest to see the results with the appropriate standard-errors,
fixef.fixest to extract the fixed-effects coefficients, and the function etable
to visualize the results of multiple estimations.
And other estimation methods: feols, femlm, fenegbin, feNmlm.
# Poisson estimation res = feglm(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris, "poisson") # You could also use fepois res_pois = fepois(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris) # With the fit method: res_fit = feglm.fit(iris$Sepal.Length, iris[, 2:3], iris$Species, "poisson") # All results are identical: etable(res, res_pois, res_fit) # Note that you have many more examples in feols # # Multiple estimations: # # 6 estimations est_mult = fepois(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = fepois(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1]# Poisson estimation res = feglm(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris, "poisson") # You could also use fepois res_pois = fepois(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris) # With the fit method: res_fit = feglm.fit(iris$Sepal.Length, iris[, 2:3], iris$Species, "poisson") # All results are identical: etable(res, res_pois, res_fit) # Note that you have many more examples in feols # # Multiple estimations: # # 6 estimations est_mult = fepois(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = fepois(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1]
This function estimates maximum likelihood models with any number of fixed-effects.
femlm( fml, data, family = c("poisson", "negbin", "logit", "gaussian"), vcov, start = 0, fixef, fixef.rm = "perfect", offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef.tol = 1e-05, fixef.iter = 10000, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), theta.init, combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... ) fenegbin( fml, data, vcov, theta.init, start = 0, fixef, fixef.rm = "perfect", offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef.tol = 1e-05, fixef.iter = 10000, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )femlm( fml, data, family = c("poisson", "negbin", "logit", "gaussian"), vcov, start = 0, fixef, fixef.rm = "perfect", offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef.tol = 1e-05, fixef.iter = 10000, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), theta.init, combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... ) fenegbin( fml, data, vcov, theta.init, start = 0, fixef, fixef.rm = "perfect", offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef.tol = 1e-05, fixef.iter = 10000, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )
fml |
A formula representing the relation to be estimated. For example: |
data |
A data.frame containing the necessary variables to run the model.
The variables of the non-linear right hand side of the formula are identified
with this |
family |
Character scalar. It should provide the family. The possible values are "poisson" (Poisson model with log-link, the default), "negbin" (Negative Binomial model with log-link), "logit" (LOGIT model with log-link), "gaussian" (Gaussian model). |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
start |
Starting values for the coefficients. Can be: i) a numeric of length 1
(e.g. |
fixef |
Character vector. The names of variables to be used as fixed-effects. These variables should contain the identifier of each observation (e.g., think of it as a panel identifier). Note that the recommended way to include fixed-effects is to insert them directly in the formula. |
fixef.rm |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. Note, importantly, that singletons are removed in just one pass, there is no recursivity implemented. The meaning of "both" and "none" is direct. |
offset |
A formula or a numeric vector. An offset can be added to the estimation.
If equal to a formula, it should be of the form (for example) |
subset |
A vector (logical or numeric) or a one-sided formula. If provided, then the estimation will be performed only on the observations defined by this argument. |
split |
A one sided formula representing a variable (eg |
fsplit |
A one sided formula representing a variable (eg |
split.keep |
A character vector. Only used when |
split.drop |
A character vector. Only used when |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
ssc |
An object of class |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
lean |
Logical, default is |
verbose |
Integer, default is 0. It represents the level of information that
should be reported during the optimisation process. If |
warn |
Logical, default is |
notes |
Logical. By default, two notes are displayed: when NAs are removed
(to show additional information) and when some observations are removed because
of only 0 (or 0/1) outcomes in a fixed-effect setup (in Poisson/Neg. Bin./Logit models).
To avoid displaying these messages, you can set |
theta.init |
Positive numeric scalar. The starting value of the dispersion
parameter if |
combine.quick |
Logical. When you combine different variables to transform them
into a single fixed-effects you can do e.g. |
mem.clean |
Logical, default is |
only.env |
(Advanced users.) Logical, default is |
only.coef |
Logical, default is |
data.save |
Logical scalar, default is |
env |
(Advanced users.) A |
... |
Not currently used. |
Note that the functions feglm and femlm provide the same results when using
the same families but differ in that the latter is a direct maximum likelihood
optimization (so the two can really have different convergence rates).
A fixest object. Note that fixest objects contain many elements and most of
them are for internal use, they are presented here only for information.
To access them, it is safer to use the user-level methods
(e.g. vcov.fixest, resid.fixest, etc) or functions (like for instance
fitstat to access any fit statistic).
nobs |
The number of observations. |
fml |
The linear formula of the call. |
call |
The call of the function. |
method |
The method used to estimate the model. |
family |
The family used to estimate the model. |
data |
The original data set used when calling the function. Only available when
the estimation was called with |
fml_all |
A list containing different parts of the formula. Always contain the
linear formula. Then, if relevant: |
nparams |
The number of parameters of the model. |
fixef_vars |
The names of each fixed-effect dimension. |
fixef_id |
The list (of length the number of fixed-effects) of the fixed-effects identifiers for each observation. |
fixef_sizes |
The size of each fixed-effect (i.e. the number of unique identifier for each fixed-effect dimension). |
convStatus |
Logical, convergence status. |
message |
The convergence message from the optimization procedures. |
obs_selection |
(When relevant.) List containing vectors of integers. It represents the sequential selection of observation vis a vis the original data set. |
fixef_removed |
(When relevant.) In the case there were fixed-effects and some observations were removed because of only 0/1 outcome within a fixed-effect, it gives the list (for each fixed-effect dimension) of the fixed-effect identifiers that were removed. |
coefficients |
The named vector of estimated coefficients. |
coeftable |
The table of the coefficients with their standard errors, z-values and p-values. |
loglik |
The log-likelihood. |
iterations |
Number of iterations of the algorithm. |
ll_null |
Log-likelihood of the null model (i.e. with the intercept only). |
ll_fe_only |
Log-likelihood of the model with only the fixed-effects. |
ssr_null |
Sum of the squared residuals of the null model (containing only with the intercept). |
pseudo_r2 |
The adjusted pseudo R2. |
fitted.values |
The fitted values are the expected value of the dependent variable
for the fitted model: that is |
residuals |
The residuals (y minus the fitted values). |
sq.cor |
Squared correlation between the dependent variable and the expected predictor (i.e. fitted.values) obtained by the estimation. |
hessian |
The Hessian of the parameters. |
cov.iid |
The variance-covariance matrix of the parameters. |
se |
The standard-error of the parameters. |
scores |
The matrix of the scores (first derivative for each observation). |
residuals |
The difference between the dependent variable and the expected predictor. |
sumFE |
The sum of the fixed-effects coefficients for each observation. |
offset |
(When relevant.) The offset formula. |
weights |
(When relevant.) The weights formula. |
You can combine two variables to make it a new fixed-effect using ^.
The syntax is as follows: fe_1^fe_2. Here you created a new variable which is the combination
of the two variables fe_1 and fe_2. This is identical to doing paste0(fe_1, "_", fe_2)
but more convenient.
Note that pasting is a costly operation, especially for large data sets.
Thus, the internal algorithm uses a numerical trick which is fast, but the drawback is
that the identity of each observation is lost (i.e. they are now equal to a meaningless
number instead of being equal to paste0(fe_1, "_", fe_2)). These “identities”
are useful only if you're interested in the value of the fixed-effects (that you can
extract with fixef.fixest). If you're only interested in coefficients of the variables,
it doesn't matter. Anyway, you can use combine.quick = FALSE to tell the internal
algorithm to use paste instead of the numerical trick. By default, the numerical
trick is performed only for large data sets.
To use leads/lags of variables in the estimation, you can: i) either provide the argument
panel.id, ii) either set your data set as a panel with the function
panel, f and d.
You can provide several leads/lags/differences at once: e.g. if your formula is equal to
f(y) ~ l(x, -1:1), it means that the dependent variable is equal to the lead of y,
and you will have as explanatory variables the lead of x1, x1 and the lag of x1.
See the examples in function l for more details.
You can interact a numeric variable with a "factor-like" variable by using
i(factor_var, continuous_var, ref), where continuous_var will be interacted with
each value of factor_var and the argument ref is a value of factor_var
taken as a reference (optional).
Using this specific way to create interactions leads to a different display of the
interacted values in etable. See examples.
It is important to note that if you do not care about the standard-errors of
the interactions, then you can add interactions in the fixed-effects part of the formula,
it will be incomparably faster (using the syntax factor_var[continuous_var], as explained
in the section “Varying slopes”).
The function i has in fact more arguments, please see details in its associated help page.
Standard-errors can be computed in different ways, you can use the arguments se and ssc
in summary.fixest to define how to compute them. By default, in the presence
of fixed-effects, standard-errors are automatically clustered.
The following vignette: On standard-errors describes in details how the standard-errors are computed in
fixest and how you can replicate standard-errors from other software.
You can use the functions setFixest_vcov and setFixest_ssc to
permanently set the way the standard-errors are computed.
Multiple estimations can be performed at once, they just have to be specified in the formula.
Multiple estimations yield a fixest_multi object which is ‘kind of’ a list of
all the results but includes specific methods to access the results in a handy way.
Please have a look at the dedicated vignette:
Multiple estimations.
To include multiple dependent variables, wrap them in c() (list() also works).
For instance fml = c(y1, y2) ~ x1 would estimate the model fml = y1 ~ x1 and
then the model fml = y2 ~ x1.
To include multiple independent variables, you need to use the stepwise functions.
There are 4 stepwise functions: sw, sw0, csw, csw0, and mvsw. Of course sw
stands for stepwise, and csw for cumulative stepwise. Finally mvsw is a bit special,
it stands for multiverse stepwise. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3).
The stepwise function sw will estimate the following two models: y ~ x1 + x2 and
y ~ x1 + x3. That is, each element in sw() is sequentially, and separately,
added to the formula. Would have you used sw0 in lieu of sw, then the model
y ~ x1 would also have been estimated. The 0 in the name means that the model
without any stepwise element also needs to be estimated.
The prefix c means cumulative: each stepwise element is added to the next. That is,
fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2 and
y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model without
the stepwise elements to be estimated: in other words, fml = y ~ x1 + csw0(x2, x3)
leads to the following three models: y ~ x1, y ~ x1 + x2 and y ~ x1 + x2 + x3.
Finally mvsw will add, in a stepwise fashion all possible combinations of the variables
in its arguments. For example mvsw(x1, x2, x3) is equivalent to
sw0(x1, x2, x3, x1 + x2, x1 + x3, x2 + x3, x1 + x2 + x3). The number of models
to estimate grows at a factorial rate: so be cautious!
Multiple independent variables can be combined with multiple dependent variables, as in
fml = c(y1, y2) ~ cw(x1, x2, x3) which would lead to 6 estimations. Multiple
estimations can also be combined to split samples (with the arguments split, fsplit).
You can also add fixed-effects in a stepwise fashion. Note that you cannot perform
stepwise estimations on the IV part of the formula (feols only).
If NAs are present in the sample, to avoid too many messages, only NA removal concerning the variables common to all estimations is reported.
A note on performance. The feature of multiple estimations has been highly optimized for
feols, in particular in the presence of fixed-effects. It is faster to estimate
multiple models using the formula rather than with a loop. For non-feols models using
the formula is roughly similar to using a loop performance-wise.
When the data set has been set up globally using
setFixest_estimation(data = data_set), the argument vcov can be used implicitly.
This means that calls such as feols(y ~ x, "HC1"), or feols(y ~ x, ~id), are valid:
i) the data is automatically deduced from the global settings, and ii) the vcov
is deduced to be the second argument.
Although the argument 'data' is placed in second position, the data can be piped to the
estimation functions. For example, with R >= 4.1, mtcars |> feols(mpg ~ cyl) works as
feols(mpg ~ cyl, mtcars).
To use multiple dependent variables in fixest estimations, you need to include them
in a vector: like in c(y1, y2, y3).
First, if names are stored in a vector, they can readily be inserted in a formula to
perform multiple estimations using the dot square bracket operator. For instance if
my_lhs = c("y1", "y2"), calling fixest with, say feols(.[my_lhs] ~ x1, etc) is
equivalent to using feols(c(y1, y2) ~ x1, etc). Beware that this is a special feature
unique to the left-hand-side of fixest estimations (the default behavior of the DSB
operator is to aggregate with sums, see xpd).
Second, you can use a regular expression to grep the left-hand-sides on the fly. When the
..("regex") feature is used naked on the LHS, the variables grepped are inserted into
c(). For example ..("Pe") ~ Sepal.Length, iris is equivalent to
c(Petal.Length, Petal.Width) ~ Sepal.Length, iris. Beware that this is a
special feature unique to the left-hand-side of fixest estimations
(the default behavior of ..("regex") is to aggregate with sums, see xpd).
In a formula, the dot square bracket (DSB) operator can: i) create manifold variables at once, or ii) capture values from the current environment and put them verbatim in the formula.
Say you want to include the variables x1 to x3 in your formula. You can use
xpd(y ~ x.[1:3]) and you'll get y ~ x1 + x2 + x3.
To summon values from the environment, simply put the variable in square brackets. For example:
for(i in 1:3) xpd(y.[i] ~ x) will create the formulas y1 ~ x to y3 ~ x depending on the
value of i.
You can include a full variable from the environment in the same way:
for(y in c("a", "b")) xpd(.[y] ~ x) will create the two formulas a ~ x and b ~ x.
The DSB can even be used within variable names, but then the variable must be nested in
character form. For example y ~ .["x.[1:2]_sq"] will create y ~ x1_sq + x2_sq. Using the
character form is important to avoid a formula parsing error. Double quotes must be used. Note
that the character string that is nested will be parsed with the function dsb, and thus it
will return a vector.
By default, the DSB operator expands vectors into sums. You can add a comma, like in .[, x],
to expand with commas–the content can then be used within functions. For instance:
c(x.[, 1:2]) will create c(x1, x2) (and not c(x1 + x2)).
In all fixest estimations, this special parsing is enabled, so you don't need to use xpd.
One-sided formulas can be expanded with the DSB operator: let x = ~sepal + petal, then
xpd(y ~ .[x]) leads to color ~ sepal + petal.
You can even use multiple square brackets within a single variable, but then the use of nesting
is required. For example, the following xpd(y ~ .[".[letters[1:2]]_.[1:2]"]) will create
y ~ a_1 + b_2. Remember that the nested character string is parsed with dsb,
which explains this behavior.
When the element to be expanded i) is equal to the empty string or, ii) is of length 0, it is
replaced with a neutral element, namely 1. For example, x = "" ; xpd(y ~ .[x]) leads to
y ~ 1.
Laurent Berge
Berge, Laurent, 2018, "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm." CREA Discussion Papers, 13 ().
For models with multiple fixed-effects:
Gaure, Simen, 2013, "OLS with multiple high dimensional category variables", Computational Statistics & Data Analysis 66 pp. 8–18
On the unconditionnal Negative Binomial model:
Allison, Paul D and Waterman, Richard P, 2002, "Fixed-Effects Negative Binomial Regression Models", Sociological Methodology 32(1) pp. 247–265
See also summary.fixest to see the results with the appropriate standard-errors,
fixef.fixest to extract the fixed-effects coefficients, and the function
etable to visualize the results of multiple estimations.
And other estimation methods: feols, feglm, fepois, feNmlm.
# Load trade data data(trade) # We estimate the effect of distance on trade => we account for 3 fixed-effects # 1) Poisson estimation est_pois = femlm(Euros ~ log(dist_km) | Origin + Destination + Product, trade) # 2) Log-Log Gaussian estimation (with same FEs) est_gaus = update(est_pois, log(Euros+1) ~ ., family = "gaussian") # Comparison of the results using the function etable etable(est_pois, est_gaus) # Now using two way clustered standard-errors etable(est_pois, est_gaus, se = "twoway") # Comparing different types of standard errors sum_hetero = summary(est_pois, se = "hetero") sum_oneway = summary(est_pois, se = "cluster") sum_twoway = summary(est_pois, se = "twoway") sum_threeway = summary(est_pois, se = "threeway") etable(sum_hetero, sum_oneway, sum_twoway, sum_threeway) # # Multiple estimations: # # 6 estimations est_mult = femlm(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = fepois(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1]# Load trade data data(trade) # We estimate the effect of distance on trade => we account for 3 fixed-effects # 1) Poisson estimation est_pois = femlm(Euros ~ log(dist_km) | Origin + Destination + Product, trade) # 2) Log-Log Gaussian estimation (with same FEs) est_gaus = update(est_pois, log(Euros+1) ~ ., family = "gaussian") # Comparison of the results using the function etable etable(est_pois, est_gaus) # Now using two way clustered standard-errors etable(est_pois, est_gaus, se = "twoway") # Comparing different types of standard errors sum_hetero = summary(est_pois, se = "hetero") sum_oneway = summary(est_pois, se = "cluster") sum_twoway = summary(est_pois, se = "twoway") sum_threeway = summary(est_pois, se = "threeway") etable(sum_hetero, sum_oneway, sum_twoway, sum_threeway) # # Multiple estimations: # # 6 estimations est_mult = femlm(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = fepois(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1]
This function estimates maximum likelihood models (e.g., Poisson or Logit) with non-linear
in parameters right-hand-sides and is efficient to handle any number of fixed effects.
If you do not use non-linear in parameters right-hand-side, use femlm or feglm
instead (their design is simpler).
feNmlm( fml, data, family = c("poisson", "negbin", "logit", "gaussian"), NL.fml, vcov, fixef, fixef.rm = "perfect", NL.start, lower, upper, NL.start.init, offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = 0, jacobian.method = "simple", useHessian = TRUE, hessian.args = NULL, opt.control = list(), nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, theta.init, fixef.tol = 1e-05, fixef.iter = 10000, deriv.tol = 1e-04, deriv.iter = 1000, warn = TRUE, notes = getFixest_notes(), combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )feNmlm( fml, data, family = c("poisson", "negbin", "logit", "gaussian"), NL.fml, vcov, fixef, fixef.rm = "perfect", NL.start, lower, upper, NL.start.init, offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, start = 0, jacobian.method = "simple", useHessian = TRUE, hessian.args = NULL, opt.control = list(), nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, theta.init, fixef.tol = 1e-05, fixef.iter = 10000, deriv.tol = 1e-04, deriv.iter = 1000, warn = TRUE, notes = getFixest_notes(), combine.quick, mem.clean = FALSE, only.env = FALSE, only.coef = FALSE, data.save = FALSE, env, ... )
fml |
A formula. This formula gives the linear formula to be estimated
(it is similar to a |
data |
A data.frame containing the necessary variables to run the model.
The variables of the non-linear right hand side of the formula are identified
with this |
family |
Character scalar. It should provide the family. The possible values are "poisson" (Poisson model with log-link, the default), "negbin" (Negative Binomial model with log-link), "logit" (LOGIT model with log-link), "gaussian" (Gaussian model). |
NL.fml |
A formula. If provided, this formula represents the non-linear part of
the right hand side (RHS). Note that contrary to the |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
fixef |
Character vector. The names of variables to be used as fixed-effects. These variables should contain the identifier of each observation (e.g., think of it as a panel identifier). Note that the recommended way to include fixed-effects is to insert them directly in the formula. |
fixef.rm |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. Note, importantly, that singletons are removed in just one pass, there is no recursivity implemented. The meaning of "both" and "none" is direct. |
NL.start |
(For NL models only) A list of starting values for the non-linear parameters.
ALL the parameters are to be named and given a staring value.
Example: |
lower |
(For NL models only) A list. The lower bound for each of the non-linear
parameters that requires one. Example: |
upper |
(For NL models only) A list. The upper bound for each of the non-linear
parameters that requires one. Example: |
NL.start.init |
(For NL models only) Numeric scalar. If the argument |
offset |
A formula or a numeric vector. An offset can be added to the estimation.
If equal to a formula, it should be of the form (for example) |
subset |
A vector (logical or numeric) or a one-sided formula. If provided, then the estimation will be performed only on the observations defined by this argument. |
split |
A one sided formula representing a variable (eg |
fsplit |
A one sided formula representing a variable (eg |
split.keep |
A character vector. Only used when |
split.drop |
A character vector. Only used when |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
ssc |
An object of class |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
start |
Starting values for the coefficients in the linear part (for the non-linear
part, use NL.start). Can be: i) a numeric of length 1 (e.g. |
jacobian.method |
(For NL models only) Character scalar. Provides the method
used to numerically compute the Jacobian of the non-linear part.
Can be either |
useHessian |
Logical. Should the Hessian be computed in the optimization stage?
Default is |
hessian.args |
List of arguments to be passed to function |
opt.control |
List of elements to be passed to the optimization method |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
lean |
Logical, default is |
verbose |
Integer, default is 0. It represents the level of information that
should be reported during the optimisation process. If |
theta.init |
Positive numeric scalar. The starting value of the dispersion
parameter if |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
deriv.tol |
Precision used to obtain the fixed-effects derivatives. Defaults to |
deriv.iter |
Maximum number of iterations in the algorithm to obtain the derivative of the fixed-effects (only in use for 2+ fixed-effects). Default is 1000. |
warn |
Logical, default is |
notes |
Logical. By default, two notes are displayed: when NAs are removed
(to show additional information) and when some observations are removed because
of only 0 (or 0/1) outcomes in a fixed-effect setup (in Poisson/Neg. Bin./Logit models).
To avoid displaying these messages, you can set |
combine.quick |
Logical. When you combine different variables to transform them
into a single fixed-effects you can do e.g. |
mem.clean |
Logical, default is |
only.env |
(Advanced users.) Logical, default is |
only.coef |
Logical, default is |
data.save |
Logical scalar, default is |
env |
(Advanced users.) A |
... |
Not currently used. |
This function estimates maximum likelihood models where the conditional expectations are as follows:
Gaussian likelihood:
Poisson and Negative Binomial likelihoods:
where in the Negative Binomial there is the parameter used to
model the variance as , with the
conditional expectation.
Logit likelihood:
When there are one or more fixed-effects, the conditional expectation can be written as:
where is the function corresponding to the likelihood function as shown before.
is the matrix associated to fixed-effect dimension such that
is equal to 1 if observation is of category in the
fixed-effect dimension and 0 otherwise.
When there are non linear in parameters functions, we can schematically split the set of regressors in two:
with first a linear term and then a non linear part expressed by the function g. That is,
we add a non-linear term to the linear terms (which are and
the fixed-effects coefficients). It is always better (more efficient) to put
into the argument NL.fml only the non-linear in parameter terms, and
add all linear terms in the fml argument.
To estimate only a non-linear formula without even the intercept, you must
exclude the intercept from the linear formula by using, e.g., fml = z~0.
The over-dispersion parameter of the Negative Binomial family, theta, is capped at 10,000. If theta reaches this high value, it means that there is no overdispersion.
A fixest object. Note that fixest objects contain many elements and most of them
are for internal use, they are presented here only for information. To access them,
it is safer to use the user-level methods (e.g. vcov.fixest, resid.fixest,
etc) or functions (like for instance fitstat to access any fit statistic).
coefficients |
The named vector of coefficients. |
coeftable |
The table of the coefficients with their standard errors, z-values and p-values. |
loglik |
The loglikelihood. |
iterations |
Number of iterations of the algorithm. |
nobs |
The number of observations. |
nparams |
The number of parameters of the model. |
call |
The call. |
fml |
The linear formula of the call. |
fml_all |
A list containing different parts of the formula. Always contain
the linear formula. Then, if relevant: |
ll_null |
Log-likelihood of the null model (i.e. with the intercept only). |
pseudo_r2 |
The adjusted pseudo R2. |
message |
The convergence message from the optimization procedures. |
sq.cor |
Squared correlation between the dependent variable and the expected predictor (i.e. fitted.values) obtained by the estimation. |
hessian |
The Hessian of the parameters. |
fitted.values |
The fitted values are the expected value of the dependent variable
for the fitted model: that is |
cov.iid |
The variance-covariance matrix of the parameters. |
se |
The standard-error of the parameters. |
scores |
The matrix of the scores (first derivative for each observation). |
family |
The ML family that was used for the estimation. |
data |
The original data set used when calling the function. Only available when
the estimation was called with |
residuals |
The difference between the dependent variable and the expected predictor. |
sumFE |
The sum of the fixed-effects for each observation. |
offset |
The offset formula. |
NL.fml |
The nonlinear formula of the call. |
bounds |
Whether the coefficients were upper or lower bounded. – This can only be the case when a non-linear formula is included and the arguments 'lower' or 'upper' are provided. |
isBounded |
The logical vector that gives for each coefficient whether it was bounded or not. This can only be the case when a non-linear formula is included and the arguments 'lower' or 'upper' are provided. |
fixef_vars |
The names of each fixed-effect dimension. |
fixef_id |
The list (of length the number of fixed-effects) of the fixed-effects identifiers for each observation. |
fixef_sizes |
The size of each fixed-effect (i.e. the number of unique identifier for each fixed-effect dimension). |
obs_selection |
(When relevant.) List containing vectors of integers. It represents the sequential selection of observation vis a vis the original data set. |
fixef_removed |
In the case there were fixed-effects and some observations were removed because of only 0/1 outcome within a fixed-effect, it gives the list (for each fixed-effect dimension) of the fixed-effect identifiers that were removed. |
theta |
In the case of a negative binomial estimation: the overdispersion parameter. |
@seealso
See also summary.fixest to see the results with the appropriate standard-errors,
fixef.fixest to extract the fixed-effects coefficients, and the function etable
to visualize the results of multiple estimations.
And other estimation methods: feols, femlm, feglm,
fepois, fenegbin.
To use leads/lags of variables in the estimation, you can: i) either provide the argument
panel.id, ii) either set your data set as a panel with the function
panel, f and d.
You can provide several leads/lags/differences at once: e.g. if your formula is equal to
f(y) ~ l(x, -1:1), it means that the dependent variable is equal to the lead of y,
and you will have as explanatory variables the lead of x1, x1 and the lag of x1.
See the examples in function l for more details.
You can interact a numeric variable with a "factor-like" variable by using
i(factor_var, continuous_var, ref), where continuous_var will be interacted with
each value of factor_var and the argument ref is a value of factor_var
taken as a reference (optional).
Using this specific way to create interactions leads to a different display of the
interacted values in etable. See examples.
It is important to note that if you do not care about the standard-errors of
the interactions, then you can add interactions in the fixed-effects part of the formula,
it will be incomparably faster (using the syntax factor_var[continuous_var], as explained
in the section “Varying slopes”).
The function i has in fact more arguments, please see details in its associated help page.
Standard-errors can be computed in different ways, you can use the arguments se and ssc
in summary.fixest to define how to compute them. By default, in the presence
of fixed-effects, standard-errors are automatically clustered.
The following vignette: On standard-errors describes in details how the standard-errors are computed in
fixest and how you can replicate standard-errors from other software.
You can use the functions setFixest_vcov and setFixest_ssc to
permanently set the way the standard-errors are computed.
Multiple estimations can be performed at once, they just have to be specified in the formula.
Multiple estimations yield a fixest_multi object which is ‘kind of’ a list of
all the results but includes specific methods to access the results in a handy way.
Please have a look at the dedicated vignette:
Multiple estimations.
To include multiple dependent variables, wrap them in c() (list() also works).
For instance fml = c(y1, y2) ~ x1 would estimate the model fml = y1 ~ x1 and
then the model fml = y2 ~ x1.
To include multiple independent variables, you need to use the stepwise functions.
There are 4 stepwise functions: sw, sw0, csw, csw0, and mvsw. Of course sw
stands for stepwise, and csw for cumulative stepwise. Finally mvsw is a bit special,
it stands for multiverse stepwise. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3).
The stepwise function sw will estimate the following two models: y ~ x1 + x2 and
y ~ x1 + x3. That is, each element in sw() is sequentially, and separately,
added to the formula. Would have you used sw0 in lieu of sw, then the model
y ~ x1 would also have been estimated. The 0 in the name means that the model
without any stepwise element also needs to be estimated.
The prefix c means cumulative: each stepwise element is added to the next. That is,
fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2 and
y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model without
the stepwise elements to be estimated: in other words, fml = y ~ x1 + csw0(x2, x3)
leads to the following three models: y ~ x1, y ~ x1 + x2 and y ~ x1 + x2 + x3.
Finally mvsw will add, in a stepwise fashion all possible combinations of the variables
in its arguments. For example mvsw(x1, x2, x3) is equivalent to
sw0(x1, x2, x3, x1 + x2, x1 + x3, x2 + x3, x1 + x2 + x3). The number of models
to estimate grows at a factorial rate: so be cautious!
Multiple independent variables can be combined with multiple dependent variables, as in
fml = c(y1, y2) ~ cw(x1, x2, x3) which would lead to 6 estimations. Multiple
estimations can also be combined to split samples (with the arguments split, fsplit).
You can also add fixed-effects in a stepwise fashion. Note that you cannot perform
stepwise estimations on the IV part of the formula (feols only).
If NAs are present in the sample, to avoid too many messages, only NA removal concerning the variables common to all estimations is reported.
A note on performance. The feature of multiple estimations has been highly optimized for
feols, in particular in the presence of fixed-effects. It is faster to estimate
multiple models using the formula rather than with a loop. For non-feols models using
the formula is roughly similar to using a loop performance-wise.
When the data set has been set up globally using
setFixest_estimation(data = data_set), the argument vcov can be used implicitly.
This means that calls such as feols(y ~ x, "HC1"), or feols(y ~ x, ~id), are valid:
i) the data is automatically deduced from the global settings, and ii) the vcov
is deduced to be the second argument.
Although the argument 'data' is placed in second position, the data can be piped to the
estimation functions. For example, with R >= 4.1, mtcars |> feols(mpg ~ cyl) works as
feols(mpg ~ cyl, mtcars).
To use multiple dependent variables in fixest estimations, you need to include them
in a vector: like in c(y1, y2, y3).
First, if names are stored in a vector, they can readily be inserted in a formula to
perform multiple estimations using the dot square bracket operator. For instance if
my_lhs = c("y1", "y2"), calling fixest with, say feols(.[my_lhs] ~ x1, etc) is
equivalent to using feols(c(y1, y2) ~ x1, etc). Beware that this is a special feature
unique to the left-hand-side of fixest estimations (the default behavior of the DSB
operator is to aggregate with sums, see xpd).
Second, you can use a regular expression to grep the left-hand-sides on the fly. When the
..("regex") feature is used naked on the LHS, the variables grepped are inserted into
c(). For example ..("Pe") ~ Sepal.Length, iris is equivalent to
c(Petal.Length, Petal.Width) ~ Sepal.Length, iris. Beware that this is a
special feature unique to the left-hand-side of fixest estimations
(the default behavior of ..("regex") is to aggregate with sums, see xpd).
In a formula, the dot square bracket (DSB) operator can: i) create manifold variables at once, or ii) capture values from the current environment and put them verbatim in the formula.
Say you want to include the variables x1 to x3 in your formula. You can use
xpd(y ~ x.[1:3]) and you'll get y ~ x1 + x2 + x3.
To summon values from the environment, simply put the variable in square brackets. For example:
for(i in 1:3) xpd(y.[i] ~ x) will create the formulas y1 ~ x to y3 ~ x depending on the
value of i.
You can include a full variable from the environment in the same way:
for(y in c("a", "b")) xpd(.[y] ~ x) will create the two formulas a ~ x and b ~ x.
The DSB can even be used within variable names, but then the variable must be nested in
character form. For example y ~ .["x.[1:2]_sq"] will create y ~ x1_sq + x2_sq. Using the
character form is important to avoid a formula parsing error. Double quotes must be used. Note
that the character string that is nested will be parsed with the function dsb, and thus it
will return a vector.
By default, the DSB operator expands vectors into sums. You can add a comma, like in .[, x],
to expand with commas–the content can then be used within functions. For instance:
c(x.[, 1:2]) will create c(x1, x2) (and not c(x1 + x2)).
In all fixest estimations, this special parsing is enabled, so you don't need to use xpd.
One-sided formulas can be expanded with the DSB operator: let x = ~sepal + petal, then
xpd(y ~ .[x]) leads to color ~ sepal + petal.
You can even use multiple square brackets within a single variable, but then the use of nesting
is required. For example, the following xpd(y ~ .[".[letters[1:2]]_.[1:2]"]) will create
y ~ a_1 + b_2. Remember that the nested character string is parsed with dsb,
which explains this behavior.
When the element to be expanded i) is equal to the empty string or, ii) is of length 0, it is
replaced with a neutral element, namely 1. For example, x = "" ; xpd(y ~ .[x]) leads to
y ~ 1.
Laurent Berge
Berge, Laurent, 2018, "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm." CREA Discussion Papers, 13 ().
For models with multiple fixed-effects:
Gaure, Simen, 2013, "OLS with multiple high dimensional category variables", Computational Statistics & Data Analysis 66 pp. 8–18
On the unconditionnal Negative Binomial model:
Allison, Paul D and Waterman, Richard P, 2002, "Fixed-Effects Negative Binomial Regression Models", Sociological Methodology 32(1) pp. 247–265
# This section covers only non-linear in parameters examples # For linear relationships: use femlm or feglm instead # Generating data for a simple example set.seed(1) n = 100 x = rnorm(n, 1, 5)**2 y = rnorm(n, -1, 5)**2 z1 = rpois(n, x*y) + rpois(n, 2) base = data.frame(x, y, z1) # Estimating a 'linear' relation: est1_L = femlm(z1 ~ log(x) + log(y), base) # Estimating the same 'linear' relation using a 'non-linear' call est1_NL = feNmlm(z1 ~ 1, base, NL.fml = ~a*log(x)+b*log(y), NL.start = list(a=0, b=0)) # we compare the estimates with the function esttable (they are identical) etable(est1_L, est1_NL) # Now generating a non-linear relation (E(z2) = x + y + 1): z2 = rpois(n, x + y) + rpois(n, 1) base$z2 = z2 # Estimation using this non-linear form est2_NL = feNmlm(z2 ~ 0, base, NL.fml = ~log(a*x + b*y), NL.start = 2, lower = list(a=0, b=0)) # we can't estimate this relation linearily # => closest we can do: est2_L = femlm(z2 ~ log(x) + log(y), base) # Difference between the two models: etable(est2_L, est2_NL) # Plotting the fits: plot(x, z2, pch = 18) points(x, fitted(est2_L), col = 2, pch = 1) points(x, fitted(est2_NL), col = 4, pch = 2)# This section covers only non-linear in parameters examples # For linear relationships: use femlm or feglm instead # Generating data for a simple example set.seed(1) n = 100 x = rnorm(n, 1, 5)**2 y = rnorm(n, -1, 5)**2 z1 = rpois(n, x*y) + rpois(n, 2) base = data.frame(x, y, z1) # Estimating a 'linear' relation: est1_L = femlm(z1 ~ log(x) + log(y), base) # Estimating the same 'linear' relation using a 'non-linear' call est1_NL = feNmlm(z1 ~ 1, base, NL.fml = ~a*log(x)+b*log(y), NL.start = list(a=0, b=0)) # we compare the estimates with the function esttable (they are identical) etable(est1_L, est1_NL) # Now generating a non-linear relation (E(z2) = x + y + 1): z2 = rpois(n, x + y) + rpois(n, 1) base$z2 = z2 # Estimation using this non-linear form est2_NL = feNmlm(z2 ~ 0, base, NL.fml = ~log(a*x + b*y), NL.start = 2, lower = list(a=0, b=0)) # we can't estimate this relation linearily # => closest we can do: est2_L = femlm(z2 ~ log(x) + log(y), base) # Difference between the two models: etable(est2_L, est2_NL) # Plotting the fits: plot(x, z2, pch = 18) points(x, fitted(est2_L), col = 2, pch = 1) points(x, fitted(est2_NL), col = 4, pch = 2)
Estimates OLS with any number of fixed-effects.
feols( fml, data, vcov, weights, offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef, fixef.rm = "none", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), only.coef = FALSE, data.save = FALSE, combine.quick, demeaned = FALSE, mem.clean = FALSE, only.env = FALSE, env, ... ) feols.fit( y, X, fixef_df, vcov, offset, split, fsplit, split.keep, split.drop, cluster, se, ssc, weights, subset, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), mem.clean = FALSE, verbose = 0, only.env = FALSE, only.coef = FALSE, env, ... )feols( fml, data, vcov, weights, offset, subset, split, fsplit, split.keep, split.drop, cluster, se, ssc, panel.id, fixef, fixef.rm = "none", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, nthreads = getFixest_nthreads(), lean = FALSE, verbose = 0, warn = TRUE, notes = getFixest_notes(), only.coef = FALSE, data.save = FALSE, combine.quick, demeaned = FALSE, mem.clean = FALSE, only.env = FALSE, env, ... ) feols.fit( y, X, fixef_df, vcov, offset, split, fsplit, split.keep, split.drop, cluster, se, ssc, weights, subset, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, fixef.algo = NULL, collin.tol = 1e-10, nthreads = getFixest_nthreads(), lean = FALSE, warn = TRUE, notes = getFixest_notes(), mem.clean = FALSE, verbose = 0, only.env = FALSE, only.coef = FALSE, env, ... )
fml |
A formula representing the relation to be estimated. For example: |
data |
A data.frame containing the necessary variables to run the model.
The variables of the non-linear right hand side of the formula are identified
with this |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
weights |
A formula or a numeric vector. Each observation can be weighted,
the weights must be greater than 0. If equal to a formula, it should be one-sided:
for example |
offset |
A formula or a numeric vector. An offset can be added to the estimation.
If equal to a formula, it should be of the form (for example) |
subset |
A vector (logical or numeric) or a one-sided formula. If provided, then the estimation will be performed only on the observations defined by this argument. |
split |
A one sided formula representing a variable (eg |
fsplit |
A one sided formula representing a variable (eg |
split.keep |
A character vector. Only used when |
split.drop |
A character vector. Only used when |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
ssc |
An object of class |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
fixef |
Character vector. The names of variables to be used as fixed-effects. These variables should contain the identifier of each observation (e.g., think of it as a panel identifier). Note that the recommended way to include fixed-effects is to insert them directly in the formula. |
fixef.rm |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. Note, importantly, that singletons are removed in just one pass, there is no recursivity implemented. The meaning of "both" and "none" is direct. |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
fixef.algo |
|
collin.tol |
Numeric scalar, default is |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
lean |
Logical, default is |
verbose |
Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algorithm (the first number is the left-hand-side, the other numbers are the right-hand-side variables). |
warn |
Logical, default is |
notes |
Logical. By default, two notes are displayed: when NAs are removed
(to show additional information) and when some observations are removed because
of collinearity. To avoid displaying these messages, you can set |
only.coef |
Logical, default is |
data.save |
Logical scalar, default is |
combine.quick |
Logical. When you combine different variables to transform them
into a single fixed-effects you can do e.g. |
demeaned |
Logical, default is |
mem.clean |
Logical, default is |
only.env |
(Advanced users.) Logical, default is |
env |
(Advanced users.) A |
... |
Not currently used. |
y |
Numeric vector/matrix/data.frame of the dependent variable(s). Multiple dependent
variables will return a |
X |
Numeric matrix of the regressors. |
fixef_df |
Matrix/data.frame of the fixed-effects. |
The method used to demean each variable along the fixed-effects is based on Berge (2018), since this is the same problem to solve as for the Gaussian case in a ML setup.
A fixest object. Note that fixest objects contain many elements and most of them are
for internal use, they are presented here only for information. To access them, it is safer
to use the user-level methods (e.g. vcov.fixest, resid.fixest, etc) or functions
(like for instance fitstat to access any fit statistic).
nobs |
The number of observations. |
fml |
The linear formula of the call. |
call |
The call of the function. |
method |
The method used to estimate the model. |
data |
The original data set used when calling the function. Only available when
the estimation was called with |
fml_all |
A list containing different parts of the formula. Always contain the linear formula. Then depending on the cases: |
fixef_vars |
The names of each fixed-effect dimension. |
fixef_id |
The list (of length the number of fixed-effects) of the fixed-effects identifiers for each observation. |
fixef_sizes |
The size of each fixed-effect (i.e. the number of unique identifierfor each fixed-effect dimension). |
coefficients |
The named vector of estimated coefficients. |
multicol |
Logical, if multicollinearity was found. |
coeftable |
The table of the coefficients with their standard errors, z-values and p-values. |
loglik |
The loglikelihood. |
ssr_null |
Sum of the squared residuals of the null model (containing only with the intercept). |
ssr_fe_only |
Sum of the squared residuals of the model estimated with fixed-effects only. |
ll_null |
The log-likelihood of the null model (containing only with the intercept). |
ll_fe_only |
The log-likelihood of the model estimated with fixed-effects only. |
fitted.values |
The fitted values. |
linear.predictors |
The linear predictors. |
residuals |
The residuals (y minus the fitted values). |
sq.cor |
Squared correlation between the dependent variable and the expected predictor (i.e. fitted.values) obtained by the estimation. |
hessian |
The Hessian of the parameters. |
cov.iid |
The variance-covariance matrix of the parameters. |
se |
The standard-error of the parameters. |
scores |
The matrix of the scores (first derivative for each observation). |
residuals |
The difference between the dependent variable and the expected predictor. |
sumFE |
The sum of the fixed-effects coefficients for each observation. |
offset |
(When relevant.) The offset formula. |
weights |
(When relevant.) The weights formula. |
obs_selection |
(When relevant.) List containing vectors of integers. It represents the sequential selection of observation vis a vis the original data set. |
collin.var |
(When relevant.) Vector containing the variables removed because of collinearity. |
collin.coef |
(When relevant.) Vector of coefficients, where the values of the variables removed because of collinearity are NA. |
collin.min_norm |
The minimal diagonal value of the Cholesky decomposition. Small values indicate possible presence collinearity. |
y_demeaned |
Only when |
X_demeaned |
Only when |
You can combine two variables to make it a new fixed-effect using ^.
The syntax is as follows: fe_1^fe_2. Here you created a new variable which is the combination
of the two variables fe_1 and fe_2. This is identical to doing paste0(fe_1, "_", fe_2)
but more convenient.
Note that pasting is a costly operation, especially for large data sets.
Thus, the internal algorithm uses a numerical trick which is fast, but the drawback is
that the identity of each observation is lost (i.e. they are now equal to a meaningless
number instead of being equal to paste0(fe_1, "_", fe_2)). These “identities”
are useful only if you're interested in the value of the fixed-effects (that you can
extract with fixef.fixest). If you're only interested in coefficients of the variables,
it doesn't matter. Anyway, you can use combine.quick = FALSE to tell the internal
algorithm to use paste instead of the numerical trick. By default, the numerical
trick is performed only for large data sets.
You can add variables with varying slopes in the fixed-effect part of the formula.
The syntax is as follows: fixef_var[var1, var2]. Here the variables var1 and var2 will
be with varying slopes (one slope per value in fixef_var) and the fixed-effect
fixef_var will also be added.
To add only the variables with varying slopes and not the fixed-effect,
use double square brackets: fixef_var[[var1, var2]].
In other words:
fixef_var[var1, var2] is equivalent to fixef_var + fixef_var[[var1]] + fixef_var[[var2]]
fixef_var[[var1, var2]] is equivalent to fixef_var[[var1]] + fixef_var[[var2]]
In general, for convergence reasons, it is recommended to always add the fixed-effect and avoid using only the variable with varying slope (i.e. use single square brackets).
To use leads/lags of variables in the estimation, you can: i) either provide the argument
panel.id, ii) either set your data set as a panel with the function
panel, f and d.
You can provide several leads/lags/differences at once: e.g. if your formula is equal to
f(y) ~ l(x, -1:1), it means that the dependent variable is equal to the lead of y,
and you will have as explanatory variables the lead of x1, x1 and the lag of x1.
See the examples in function l for more details.
You can interact a numeric variable with a "factor-like" variable by using
i(factor_var, continuous_var, ref), where continuous_var will be interacted with
each value of factor_var and the argument ref is a value of factor_var
taken as a reference (optional).
Using this specific way to create interactions leads to a different display of the
interacted values in etable. See examples.
It is important to note that if you do not care about the standard-errors of
the interactions, then you can add interactions in the fixed-effects part of the formula,
it will be incomparably faster (using the syntax factor_var[continuous_var], as explained
in the section “Varying slopes”).
The function i has in fact more arguments, please see details in its associated help page.
Standard-errors can be computed in different ways, you can use the arguments se and ssc
in summary.fixest to define how to compute them. By default, in the presence
of fixed-effects, standard-errors are automatically clustered.
The following vignette: On standard-errors describes in details how the standard-errors are computed in
fixest and how you can replicate standard-errors from other software.
You can use the functions setFixest_vcov and setFixest_ssc to
permanently set the way the standard-errors are computed.
To estimate two stage least square regressions, insert the relationship between the endogenous regressor(s) and the instruments in a formula, after a pipe.
For example, fml = y ~ x1 | x_endo ~ x_inst will use the variables x1 and x_inst in
the first stage to explain x_endo. Then will use the fitted value of x_endo
(which will be named fit_x_endo) and x1 to explain y.
To include several endogenous regressors, just use "+",
like in: fml = y ~ x1 | x_endo1 + x_end2 ~ x_inst1 + x_inst2.
Of course you can still add the fixed-effects, but the IV formula must always come last,
like in fml = y ~ x1 | fe1 + fe2 | x_endo ~ x_inst.
If you want to estimate a model without exogenous variables, use "1" as a
placeholder: e.g. fml = y ~ 1 | x_endo ~ x_inst.
By default, the second stage regression is returned. You can access the first stage(s)
regressions either directly in the slot iv_first_stage (not recommended),
or using the argument stage = 1 from the function summary.fixest.
For example summary(iv_est, stage = 1) will give the first stage(s).
Note that using summary you can display both the second and first stages at
the same time using, e.g., stage = 1:2 (using 2:1 would reverse the order).
Multiple estimations can be performed at once, they just have to be specified in the formula.
Multiple estimations yield a fixest_multi object which is ‘kind of’ a list of
all the results but includes specific methods to access the results in a handy way.
Please have a look at the dedicated vignette:
Multiple estimations.
To include multiple dependent variables, wrap them in c() (list() also works).
For instance fml = c(y1, y2) ~ x1 would estimate the model fml = y1 ~ x1 and
then the model fml = y2 ~ x1.
To include multiple independent variables, you need to use the stepwise functions.
There are 4 stepwise functions: sw, sw0, csw, csw0, and mvsw. Of course sw
stands for stepwise, and csw for cumulative stepwise. Finally mvsw is a bit special,
it stands for multiverse stepwise. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3).
The stepwise function sw will estimate the following two models: y ~ x1 + x2 and
y ~ x1 + x3. That is, each element in sw() is sequentially, and separately,
added to the formula. Would have you used sw0 in lieu of sw, then the model
y ~ x1 would also have been estimated. The 0 in the name means that the model
without any stepwise element also needs to be estimated.
The prefix c means cumulative: each stepwise element is added to the next. That is,
fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2 and
y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model without
the stepwise elements to be estimated: in other words, fml = y ~ x1 + csw0(x2, x3)
leads to the following three models: y ~ x1, y ~ x1 + x2 and y ~ x1 + x2 + x3.
Finally mvsw will add, in a stepwise fashion all possible combinations of the variables
in its arguments. For example mvsw(x1, x2, x3) is equivalent to
sw0(x1, x2, x3, x1 + x2, x1 + x3, x2 + x3, x1 + x2 + x3). The number of models
to estimate grows at a factorial rate: so be cautious!
Multiple independent variables can be combined with multiple dependent variables, as in
fml = c(y1, y2) ~ cw(x1, x2, x3) which would lead to 6 estimations. Multiple
estimations can also be combined to split samples (with the arguments split, fsplit).
You can also add fixed-effects in a stepwise fashion. Note that you cannot perform
stepwise estimations on the IV part of the formula (feols only).
If NAs are present in the sample, to avoid too many messages, only NA removal concerning the variables common to all estimations is reported.
A note on performance. The feature of multiple estimations has been highly optimized for
feols, in particular in the presence of fixed-effects. It is faster to estimate
multiple models using the formula rather than with a loop. For non-feols models using
the formula is roughly similar to using a loop performance-wise.
To use multiple dependent variables in fixest estimations, you need to include them
in a vector: like in c(y1, y2, y3).
First, if names are stored in a vector, they can readily be inserted in a formula to
perform multiple estimations using the dot square bracket operator. For instance if
my_lhs = c("y1", "y2"), calling fixest with, say feols(.[my_lhs] ~ x1, etc) is
equivalent to using feols(c(y1, y2) ~ x1, etc). Beware that this is a special feature
unique to the left-hand-side of fixest estimations (the default behavior of the DSB
operator is to aggregate with sums, see xpd).
Second, you can use a regular expression to grep the left-hand-sides on the fly. When the
..("regex") feature is used naked on the LHS, the variables grepped are inserted into
c(). For example ..("Pe") ~ Sepal.Length, iris is equivalent to
c(Petal.Length, Petal.Width) ~ Sepal.Length, iris. Beware that this is a
special feature unique to the left-hand-side of fixest estimations
(the default behavior of ..("regex") is to aggregate with sums, see xpd).
When the data set has been set up globally using
setFixest_estimation(data = data_set), the argument vcov can be used implicitly.
This means that calls such as feols(y ~ x, "HC1"), or feols(y ~ x, ~id), are valid:
i) the data is automatically deduced from the global settings, and ii) the vcov
is deduced to be the second argument.
Although the argument 'data' is placed in second position, the data can be piped to the
estimation functions. For example, with R >= 4.1, mtcars |> feols(mpg ~ cyl) works as
feols(mpg ~ cyl, mtcars).
In a formula, the dot square bracket (DSB) operator can: i) create manifold variables at once, or ii) capture values from the current environment and put them verbatim in the formula.
Say you want to include the variables x1 to x3 in your formula. You can use
xpd(y ~ x.[1:3]) and you'll get y ~ x1 + x2 + x3.
To summon values from the environment, simply put the variable in square brackets. For example:
for(i in 1:3) xpd(y.[i] ~ x) will create the formulas y1 ~ x to y3 ~ x depending on the
value of i.
You can include a full variable from the environment in the same way:
for(y in c("a", "b")) xpd(.[y] ~ x) will create the two formulas a ~ x and b ~ x.
The DSB can even be used within variable names, but then the variable must be nested in
character form. For example y ~ .["x.[1:2]_sq"] will create y ~ x1_sq + x2_sq. Using the
character form is important to avoid a formula parsing error. Double quotes must be used. Note
that the character string that is nested will be parsed with the function dsb, and thus it
will return a vector.
By default, the DSB operator expands vectors into sums. You can add a comma, like in .[, x],
to expand with commas–the content can then be used within functions. For instance:
c(x.[, 1:2]) will create c(x1, x2) (and not c(x1 + x2)).
In all fixest estimations, this special parsing is enabled, so you don't need to use xpd.
One-sided formulas can be expanded with the DSB operator: let x = ~sepal + petal, then
xpd(y ~ .[x]) leads to color ~ sepal + petal.
You can even use multiple square brackets within a single variable, but then the use of nesting
is required. For example, the following xpd(y ~ .[".[letters[1:2]]_.[1:2]"]) will create
y ~ a_1 + b_2. Remember that the nested character string is parsed with dsb,
which explains this behavior.
When the element to be expanded i) is equal to the empty string or, ii) is of length 0, it is
replaced with a neutral element, namely 1. For example, x = "" ; xpd(y ~ .[x]) leads to
y ~ 1.
Laurent Berge
Berge, Laurent, 2018, "Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm." CREA Discussion Papers, 13 ().
For models with multiple fixed-effects:
Gaure, Simen, 2013, "OLS with multiple high dimensional category variables", Computational Statistics & Data Analysis 66 pp. 8–18
See also summary.fixest to see the results with the appropriate standard-errors,
fixef.fixest to extract the fixed-effects coefficients, and the function etable
to visualize the results of multiple estimations. For plotting coefficients: see coefplot.
And other estimation methods: femlm, feglm, fepois, fenegbin, feNmlm.
# # Basic estimation # res = feols(Sepal.Length ~ Sepal.Width + Petal.Length, iris) # You can specify clustered standard-errors in summary: summary(res, cluster = ~Species) # # Just one set of fixed-effects: # res = feols(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris) # By default, the SEs are clustered according to the first fixed-effect summary(res) # # Varying slopes: # res = feols(Sepal.Length ~ Petal.Length | Species[Sepal.Width], iris) summary(res) # # Combining the FEs: # base = iris base$fe_2 = rep(1:10, 15) res_comb = feols(Sepal.Length ~ Petal.Length | Species^fe_2, base) summary(res_comb) fixef(res_comb)[[1]] # # Using leads/lags: # data(base_did) # We need to set up the panel with the arg. panel.id est1 = feols(y ~ l(x1, 0:1), base_did, panel.id = ~id+period) est2 = feols(f(y) ~ l(x1, -1:1), base_did, panel.id = ~id+period) etable(est1, est2, order = "f", drop="Int") # # Using interactions: # data(base_did) # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id+period, base_did) # Now we can plot the result of the interaction with coefplot coefplot(est_did) # You have many more example in coefplot help # # Instrumental variables # # To estimate Two stage least squares, # insert a formula describing the endo. vars./instr. relation after a pipe: base = iris names(base) = c("y", "x1", "x2", "x3", "fe1") base$x_inst1 = 0.2 * base$x1 + 0.7 * base$x2 + rpois(150, 2) base$x_inst2 = 0.2 * base$x2 + 0.7 * base$x3 + rpois(150, 3) base$x_endo1 = 0.5 * base$y + 0.5 * base$x3 + rnorm(150, sd = 2) base$x_endo2 = 1.5 * base$y + 0.5 * base$x3 + 3 * base$x_inst1 + rnorm(150, sd = 5) # Using 2 controls, 1 endogenous var. and 1 instrument res_iv = feols(y ~ x1 + x2 | x_endo1 ~ x_inst1, base) # The second stage is the default summary(res_iv) # To show the first stage: summary(res_iv, stage = 1) # To show both the first and second stages: summary(res_iv, stage = 1:2) # Adding a fixed-effect => IV formula always last! res_iv_fe = feols(y ~ x1 + x2 | fe1 | x_endo1 ~ x_inst1, base) # With two endogenous regressors res_iv2 = feols(y ~ x1 + x2 | x_endo1 + x_endo2 ~ x_inst1 + x_inst2, base) # Now there's two first stages => a fixest_multi object is returned sum_res_iv2 = summary(res_iv2, stage = 1) # You can navigate through it by subsetting: sum_res_iv2[iv = 1] # The stage argument also works in etable: etable(res_iv, res_iv_fe, res_iv2, order = "endo") etable(res_iv, res_iv_fe, res_iv2, stage = 1:2, order = c("endo", "inst"), group = list(control = "!endo|inst")) # # Multiple estimations: # # 6 estimations est_mult = feols(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = feols(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1] # # Split sample estimations # base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ x.[1:3], base, split = ~species) etable(est) # You can select specific values with the %keep% and %drop% operators # By default, partial matching is enabled. It should refer to a single variable. est = feols(y ~ x.[1:3], base, split = ~species %keep% c("set", "vers")) etable(est) # You can supply regular expression by using an @ first. # regex can match several values. est = feols(y ~ x.[1:3], base, split = ~species %keep% c("@set|vers")) etable(est) # # Argument sliding # # When the data set is set up globally, you can use the vcov argument implicitly base = setNames(iris, c("y", "x1", "x2", "x3", "species")) no_sliding = feols(y ~ x1 + x2, base, ~species) # With sliding setFixest_estimation(data = base) # ~species is implicitly deduced to be equal to 'vcov' sliding = feols(y ~ x1 + x2, ~species) etable(no_sliding, sliding) # Resetting the global options setFixest_estimation(data = NULL) # # Formula expansions # # By default, the features of the xpd function are enabled in # all fixest estimations # Here's a few examples base = setNames(iris, c("y", "x1", "x2", "x3", "species")) # dot square bracket operator feols(y ~ x.[1:3], base) # fetching variables via regular expressions: ..("regex") feols(y ~ ..("1|2"), base) # NOTA: it also works for multiple LHS mult1 = feols(x.[1:2] ~ y + species, base) mult2 = feols(..("y|3") ~ x.[1:2] + species, base) etable(mult1, mult2) # Use .[, stuff] to include variables in functions: feols(y ~ csw(x.[, 1:3]), base) # Same for ..(, "regex") feols(y ~ csw(..(,"x")), base)# # Basic estimation # res = feols(Sepal.Length ~ Sepal.Width + Petal.Length, iris) # You can specify clustered standard-errors in summary: summary(res, cluster = ~Species) # # Just one set of fixed-effects: # res = feols(Sepal.Length ~ Sepal.Width + Petal.Length | Species, iris) # By default, the SEs are clustered according to the first fixed-effect summary(res) # # Varying slopes: # res = feols(Sepal.Length ~ Petal.Length | Species[Sepal.Width], iris) summary(res) # # Combining the FEs: # base = iris base$fe_2 = rep(1:10, 15) res_comb = feols(Sepal.Length ~ Petal.Length | Species^fe_2, base) summary(res_comb) fixef(res_comb)[[1]] # # Using leads/lags: # data(base_did) # We need to set up the panel with the arg. panel.id est1 = feols(y ~ l(x1, 0:1), base_did, panel.id = ~id+period) est2 = feols(f(y) ~ l(x1, -1:1), base_did, panel.id = ~id+period) etable(est1, est2, order = "f", drop="Int") # # Using interactions: # data(base_did) # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id+period, base_did) # Now we can plot the result of the interaction with coefplot coefplot(est_did) # You have many more example in coefplot help # # Instrumental variables # # To estimate Two stage least squares, # insert a formula describing the endo. vars./instr. relation after a pipe: base = iris names(base) = c("y", "x1", "x2", "x3", "fe1") base$x_inst1 = 0.2 * base$x1 + 0.7 * base$x2 + rpois(150, 2) base$x_inst2 = 0.2 * base$x2 + 0.7 * base$x3 + rpois(150, 3) base$x_endo1 = 0.5 * base$y + 0.5 * base$x3 + rnorm(150, sd = 2) base$x_endo2 = 1.5 * base$y + 0.5 * base$x3 + 3 * base$x_inst1 + rnorm(150, sd = 5) # Using 2 controls, 1 endogenous var. and 1 instrument res_iv = feols(y ~ x1 + x2 | x_endo1 ~ x_inst1, base) # The second stage is the default summary(res_iv) # To show the first stage: summary(res_iv, stage = 1) # To show both the first and second stages: summary(res_iv, stage = 1:2) # Adding a fixed-effect => IV formula always last! res_iv_fe = feols(y ~ x1 + x2 | fe1 | x_endo1 ~ x_inst1, base) # With two endogenous regressors res_iv2 = feols(y ~ x1 + x2 | x_endo1 + x_endo2 ~ x_inst1 + x_inst2, base) # Now there's two first stages => a fixest_multi object is returned sum_res_iv2 = summary(res_iv2, stage = 1) # You can navigate through it by subsetting: sum_res_iv2[iv = 1] # The stage argument also works in etable: etable(res_iv, res_iv_fe, res_iv2, order = "endo") etable(res_iv, res_iv_fe, res_iv2, stage = 1:2, order = c("endo", "inst"), group = list(control = "!endo|inst")) # # Multiple estimations: # # 6 estimations est_mult = feols(c(Ozone, Solar.R) ~ Wind + Temp + csw0(Wind:Temp, Day), airquality) # We can display the results for the first lhs: etable(est_mult[lhs = 1]) # And now the second (access can be made by name) etable(est_mult[lhs = "Solar.R"]) # Now we focus on the two last right hand sides # (note that .N can be used to specify the last item) etable(est_mult[rhs = 2:.N]) # Combining with split est_split = feols(c(Ozone, Solar.R) ~ sw(poly(Wind, 2), poly(Temp, 2)), airquality, split = ~ Month) # You can display everything at once with the print method est_split # Different way of displaying the results with "compact" summary(est_split, "compact") # You can still select which sample/LHS/RHS to display est_split[sample = 1:2, lhs = 1, rhs = 1] # # Split sample estimations # base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ x.[1:3], base, split = ~species) etable(est) # You can select specific values with the %keep% and %drop% operators # By default, partial matching is enabled. It should refer to a single variable. est = feols(y ~ x.[1:3], base, split = ~species %keep% c("set", "vers")) etable(est) # You can supply regular expression by using an @ first. # regex can match several values. est = feols(y ~ x.[1:3], base, split = ~species %keep% c("@set|vers")) etable(est) # # Argument sliding # # When the data set is set up globally, you can use the vcov argument implicitly base = setNames(iris, c("y", "x1", "x2", "x3", "species")) no_sliding = feols(y ~ x1 + x2, base, ~species) # With sliding setFixest_estimation(data = base) # ~species is implicitly deduced to be equal to 'vcov' sliding = feols(y ~ x1 + x2, ~species) etable(no_sliding, sliding) # Resetting the global options setFixest_estimation(data = NULL) # # Formula expansions # # By default, the features of the xpd function are enabled in # all fixest estimations # Here's a few examples base = setNames(iris, c("y", "x1", "x2", "x3", "species")) # dot square bracket operator feols(y ~ x.[1:3], base) # fetching variables via regular expressions: ..("regex") feols(y ~ ..("1|2"), base) # NOTA: it also works for multiple LHS mult1 = feols(x.[1:2] ~ y + species, base) mult2 = feols(..("y|3") ~ x.[1:2] + species, base) etable(mult1, mult2) # Use .[, stuff] to include variables in functions: feols(y ~ csw(x.[, 1:3]), base) # Same for ..(, "regex") feols(y ~ csw(..(,"x")), base)
Computes various fit statistics for fixest estimations.
fitstat( x, type, simplify = FALSE, verbose = TRUE, show_types = FALSE, frame = parent.frame(), ... )fitstat( x, type, simplify = FALSE, verbose = TRUE, show_types = FALSE, frame = parent.frame(), ... )
x |
A |
type |
Character vector or one sided formula. The type of fit statistic to be computed.
The classic ones are: n, rmse, r2, pr2, f, wald, ivf, ivwald. You have the full list in
the details section or use |
simplify |
Logical, default is |
verbose |
Logical, default is |
show_types |
Logical, default is |
frame |
An environment in which to evaluate variables, default is |
... |
Other elements to be passed to other methods and may be used to compute
the statistics (for example you can pass on arguments to compute the VCOV when using
|
By default an object of class fixest_fitstat is returned. Using verbose = FALSE
returns a simple a list. Finally, if only one type is selected, simplify = TRUE
leads to the selected type to be returned.
You can register custom fit statistics with the function fitstat_register.
The types are case sensitive, please use lower case only. The types available are:
n, ll, aic, bic, rmse: The number of observations, the log-likelihood, the AIC, the BIC and the root mean squared error, respectively.
my: Mean of the dependent variable.
g: The degrees of freedom used to compute the t-test (it influences the p-values of the coefficients). When the VCOV is clustered, this value is equal to the minimum cluster size, otherwise, it is equal to the sample size minus the number of variables.
r2, ar2, wr2, awr2, pr2, apr2, wpr2, awpr2: All r2 that can be
obtained with the function r2. The a stands for 'adjusted', the w for 'within' and
the p for 'pseudo'. Note that the order of the letters a, w and p does not matter.
The pseudo R2s are McFadden's R2s (ratios of log-likelihoods).
theta: The over-dispersion parameter in Negative Binomial models. Low values mean high overdispersion.
f, wf: The F-tests of nullity of the coefficients. The w stands for
'within'. These types return the following values: stat, p, df1 and df2.
If you want to display only one of these, use their name after a dot: e.g. f.stat
will give the statistic of the F-test, or wf.p will give the p-values of the F-test
on the projected model (i.e. projected onto the fixed-effects).
wald: Wald test of joint nullity of the coefficients. This test always excludes
the intercept and the fixed-effects. These type returns the following values:
stat, p, df1, df2 and vcov. The element vcov reports the way the VCOV
matrix was computed since it directly influences this statistic.
ivf, ivf1, ivf2, ivfall: These statistics are specific to IV estimations.
They report either the IV F-test (namely the Cragg-Donald F statistic in the presence
of only one endogenous regressor) of the first stage (ivf or ivf1), of the
second stage (ivf2) or of both (ivfall). The F-test of the first stage is
commonly named weak instrument test. The value of ivfall is only useful in etable
when both the 1st and 2nd stages are displayed (it leads to the 1st stage F-test(s)
to be displayed on the 1st stage estimation(s), and the 2nd stage one on the
2nd stage estimation – otherwise, ivf1 would also be displayed on the 2nd stage
estimation). These types return the following values: stat, p, df1 and df2.
ivwald, ivwald1, ivwald2, ivwaldall: These statistics are specific to IV
estimations. They report either the IV Wald-test of the first stage (ivwald or ivwald1),
of the second stage (ivwald2) or of both (ivwaldall). The Wald-test of the first stage
is commonly named weak instrument test. Note that if the estimation was done with a robust
VCOV and there is only one endogenous regressor, this is equivalent to the
Kleibergen-Paap statistic. The value of ivwaldall is only useful in etable when both
the 1st and 2nd stages are displayed (it leads to the 1st stage Wald-test(s) to be displayed
on the 1st stage estimation(s), and the 2nd stage one on the 2nd stage estimation –
otherwise, ivwald1 would also be displayed on the 2nd stage estimation). These types
return the following values: stat, p, df1, df2, and vcov.
cd: The Cragg-Donald test for weak instruments.
kpr: The Kleibergen-Paap test for weak instruments.
wh: This statistic is specific to IV estimations. Wu-Hausman endogeneity test.
H0 is the absence of endogeneity of the instrumented variables. It returns the following
values: stat, p, df1, df2.
sargan: Sargan test of overidentifying restrictions. H0: the instruments are
not correlated with the second stage residuals. It returns the
following values: stat, p, df.
lr, wlr: Likelihood ratio and within likelihood ratio tests. It returns
the following elements: stat, p, df. Concerning the within-LR test, note that,
contrary to estimations with femlm or feNmlm, estimations with feglm/fepois
need to estimate the model with fixed-effects only which may prove time-consuming
(depending on your model). Bottom line, if you really need the within-LR and estimate a
Poisson model, use femlm instead of fepois (the former uses direct ML maximization for
which the only FEs model is a by product).
data(trade) gravity = feols(log(Euros) ~ log(dist_km) | Destination + Origin, trade) # Extracting the 'working' number of observations used to compute the pvalues fitstat(gravity, "g", simplify = TRUE) # Some fit statistics fitstat(gravity, ~ rmse + r2 + wald + wf) # You can use them in etable etable(gravity, fitstat = ~ rmse + r2 + wald + wf) # For wald and wf, you could show the pvalue instead: etable(gravity, fitstat = ~ rmse + r2 + wald.p + wf.p) # Now let's display some statistics that are not built-in # => we use fitstat_register to create them # We need: a) type name, b) the function to be applied # c) (optional) an alias fitstat_register("tstand", function(x) tstat(x, se = "stand")[1], "t-stat (regular)") fitstat_register("thc", function(x) tstat(x, se = "heter")[1], "t-stat (HC1)") fitstat_register("t1w", function(x) tstat(x, se = "clus")[1], "t-stat (clustered)") fitstat_register("t2w", function(x) tstat(x, se = "twow")[1], "t-stat (2-way)") # Now we can use these keywords in fitstat: etable(gravity, fitstat = ~ . + tstand + thc + t1w + t2w) # Note that the custom stats we created are can easily lead # to errors, but that's another story!data(trade) gravity = feols(log(Euros) ~ log(dist_km) | Destination + Origin, trade) # Extracting the 'working' number of observations used to compute the pvalues fitstat(gravity, "g", simplify = TRUE) # Some fit statistics fitstat(gravity, ~ rmse + r2 + wald + wf) # You can use them in etable etable(gravity, fitstat = ~ rmse + r2 + wald + wf) # For wald and wf, you could show the pvalue instead: etable(gravity, fitstat = ~ rmse + r2 + wald.p + wf.p) # Now let's display some statistics that are not built-in # => we use fitstat_register to create them # We need: a) type name, b) the function to be applied # c) (optional) an alias fitstat_register("tstand", function(x) tstat(x, se = "stand")[1], "t-stat (regular)") fitstat_register("thc", function(x) tstat(x, se = "heter")[1], "t-stat (HC1)") fitstat_register("t1w", function(x) tstat(x, se = "clus")[1], "t-stat (clustered)") fitstat_register("t2w", function(x) tstat(x, se = "twow")[1], "t-stat (2-way)") # Now we can use these keywords in fitstat: etable(gravity, fitstat = ~ . + tstand + thc + t1w + t2w) # Note that the custom stats we created are can easily lead # to errors, but that's another story!
Enables the registration of custom fi statistics that can be easily summoned with the function fitstat.
fitstat_register(type, fun, alias = NULL, subtypes = NULL)fitstat_register(type, fun, alias = NULL, subtypes = NULL)
type |
A character scalar giving the type-name. |
fun |
A function to be applied to a |
alias |
A (named) character vector. An alias to be used in lieu of the type name in
the display methods (ie when used in |
subtypes |
A character vector giving the name of each element returned by the
function |
If there are several components to the computed statistics (i.e. the function returns
several elements), then using the argument subtypes, giving the names of each of
these components, is mandatory. This is to ensure that the statistic can be used as any
other built-in statistic (and there are too many edge cases impeding automatic deduction).
Laurent Berge
# An estimation base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ x1 + x2 | species, base) # # single valued tests # # say you want to add the coefficient of variation of the dependent variable cv = function(est){ y = model.matrix(est, type = "lhs") sd(y)/mean(y) } # Now we register the routine fitstat_register("cvy", cv, "Coef. of Variation (dep. var.)") # now we can summon the registered routine with its type ("cvy") fitstat(est, "cvy") # # Multi valued tests # # Let's say you want a Wald test with an heteroskedasticiy robust variance # First we create the function hc_wald = function(est){ w = wald(est, keep = "!Intercept", print = FALSE, se = "hetero") head(w, 4) } # This test returns a vector of 4 elements: stat, p, df1 and df2 # Now we register the routine fitstat_register("hc_wald", hc_wald, "Wald (HC1)", "test2") # You can access the statistic, as before fitstat(est, "hc_wald") # But you can also access the sub elements fitstat(est, "hc_wald.p")# An estimation base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ x1 + x2 | species, base) # # single valued tests # # say you want to add the coefficient of variation of the dependent variable cv = function(est){ y = model.matrix(est, type = "lhs") sd(y)/mean(y) } # Now we register the routine fitstat_register("cvy", cv, "Coef. of Variation (dep. var.)") # now we can summon the registered routine with its type ("cvy") fitstat(est, "cvy") # # Multi valued tests # # Let's say you want a Wald test with an heteroskedasticiy robust variance # First we create the function hc_wald = function(est){ w = wald(est, keep = "!Intercept", print = FALSE, se = "hetero") head(w, 4) } # This test returns a vector of 4 elements: stat, p, df1 and df2 # Now we register the routine fitstat_register("hc_wald", hc_wald, "Wald (HC1)", "test2") # You can access the statistic, as before fitstat(est, "hc_wald") # But you can also access the sub elements fitstat(est, "hc_wald.p")
fixest fitThis function extracts the fitted values from a model estimated with femlm,
feols or feglm. The fitted values that are returned are the expected predictor.
## S3 method for class 'fixest' fitted(object, type = c("response", "link"), na.rm = TRUE, ...) ## S3 method for class 'fixest' fitted.values(object, type = c("response", "link"), na.rm = TRUE, ...)## S3 method for class 'fixest' fitted(object, type = c("response", "link"), na.rm = TRUE, ...) ## S3 method for class 'fixest' fitted.values(object, type = c("response", "link"), na.rm = TRUE, ...)
object |
A |
type |
Character either equal to |
na.rm |
Logical, default is |
... |
Not currently used. |
This function returns the expected predictor of a fixest fit. The likelihood functions
are detailed in femlm help page.
It returns a numeric vector of length the number of observations used to estimate the model.
If type = "response", the value returned is the expected predictor, i.e. the
expected value of the dependent variable for the fitted model: .
If type = "link", the value returned is the linear predictor of the fitted model,
that is (remind that ).
Laurent Berge
See also the main estimation functions femlm, feols or feglm.
resid.fixest, predict.fixest, summary.fixest, vcov.fixest, fixef.fixest.
# simple estimation on iris data, using "Species" fixed-effects res_poisson = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # we extract the fitted values y_fitted_poisson = fitted(res_poisson) # Same estimation but in OLS (Gaussian family) res_gaussian = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris, family = "gaussian") y_fitted_gaussian = fitted(res_gaussian) # comparison of the fit for the two families plot(iris$Sepal.Length, y_fitted_poisson) points(iris$Sepal.Length, y_fitted_gaussian, col = 2, pch = 2)# simple estimation on iris data, using "Species" fixed-effects res_poisson = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # we extract the fitted values y_fitted_poisson = fitted(res_poisson) # Same estimation but in OLS (Gaussian family) res_gaussian = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris, family = "gaussian") y_fitted_gaussian = fitted(res_gaussian) # comparison of the fit for the two families plot(iris$Sepal.Length, y_fitted_poisson) points(iris$Sepal.Length, y_fitted_gaussian, col = 2, pch = 2)
fixest estimation.This function retrieves the fixed effects from a fixest estimation. It is useful only
when there are one or more fixed-effect dimensions.
## S3 method for class 'fixest' fixef( object, notes = getFixest_notes(), sorted = TRUE, nthreads = getFixest_nthreads(), fixef.tol = 1e-05, fixef.iter = 10000, ... )## S3 method for class 'fixest' fixef( object, notes = getFixest_notes(), sorted = TRUE, nthreads = getFixest_nthreads(), fixef.tol = 1e-05, fixef.iter = 10000, ... )
object |
|
notes |
Logical. Whether to display a note when the fixed-effects coefficients are not regular. |
sorted |
Logical, default is |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
... |
Not currently used. |
If the fixed-effect coefficients are not regular, then several reference points need to be set: this means that the fixed-effects coefficients cannot be directly interpreted. If this is the case, then a warning is raised.
A list containing the vectors of the fixed effects.
If there is more than 1 fixed-effect, then the attribute “references” is created. This is a vector of length the number of fixed-effects, each element contains the number of coefficients set as references. By construction, the elements of the first fixed-effect dimension are never set as references. In the presence of regular fixed-effects, there should be Q-1 references (with Q the number of fixed-effects).
Laurent Berge
plot.fixest.fixef. See also the main estimation functions femlm, feols
or feglm. Use summary.fixest to see the results with the appropriate
standard-errors, fixef.fixest to extract the fixed-effect coefficients, and
the function etable to visualize the results of multiple estimations.
data(trade) # We estimate the effect of distance on trade => we account for 3 fixed-effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # Obtaining the fixed-effects coefficients: fe_trade = fixef(est_pois) # The fixed-effects of the first fixed-effect dimension: head(fe_trade$Origin) # Summary information: summary(fe_trade) # Plotting them: plot(fe_trade)data(trade) # We estimate the effect of distance on trade => we account for 3 fixed-effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # Obtaining the fixed-effects coefficients: fe_trade = fixef(est_pois) # The fixed-effects of the first fixed-effect dimension: head(fe_trade$Origin) # Summary information: summary(fe_trade) # Plotting them: plot(fe_trade)
fixest estimationRetrieves the original data set used to estimate a fixest or fixest_multi model.
Note that this is the original data set and not the data used for the estimation (i.e. it can have more rows).
fixest_data(x, sample = "original")fixest_data(x, sample = "original")
x |
An object of class |
sample |
Either "original" (default) or "estimation". If equal to "original", it matches the original data set. If equal to "estimation", the rows of the data set returned matches the observations used for the estimation. |
It returns a data.frame equal to the original data set used for the estimation, when the function was called.
If sample = "estimation", only the lines used for the estimation are returned.
In case of a fixest_multi object, it returns the data set of the first estimation object.
So in that case it does not make sense to use sample = "estimation" since
the samples may be inconsistent across the different estimations.
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) base$y[1:5] = NA est = feols(y ~ x1 + x2, base) # the original data set head(fixest_data(est)) # the data set, with only the lines used for the estimation head(fixest_data(est, sample = "est"))base = setNames(iris, c("y", "x1", "x2", "x3", "species")) base$y[1:5] = NA est = feols(y ~ x1 + x2, base) # the original data set head(fixest_data(est)) # the data set, with only the lines used for the estimation head(fixest_data(est, sample = "est"))
Package startup messages can be very annoying, although sometimes they can be necessary.
Use this function to prevent fixest's package startup message from popping when loading.
This will be specific to your current project.
fixest_startup_msg(x)fixest_startup_msg(x)
x |
Logical, no default. If |
Note that this function is introduced to cope with the first fixest startup message
(in version 0.9.0).
This function works only with R >= 4.0.0. There are no startup messages for R < 4.0.0.
fixest fitThis function extracts the formula from a fixest estimation (obtained with femlm,
feols or feglm). If the estimation was done with fixed-effects, they are added
in the formula after a pipe (“|”). If the estimation was done with a non
linear in parameters part, then this will be added in the formula in between I().
## S3 method for class 'fixest' formula(x, type = c("full", "linear", "iv", "NL"), ...)## S3 method for class 'fixest' formula(x, type = c("full", "linear", "iv", "NL"), ...)
x |
An object of class |
type |
A character scalar. Default is |
... |
Not currently used. |
It returns a formula.
Laurent Berge
See also the main estimation functions femlm, feols or feglm.
model.matrix.fixest, update.fixest, summary.fixest, vcov.fixest.
# simple estimation on iris data, using "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # formula with the fixed-effect variable formula(res) # linear part without the fixed-effects formula(res, "linear")# simple estimation on iris data, using "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # formula with the fixed-effect variable formula(res) # linear part without the fixed-effects formula(res, "linear")
fixest objectsComputes the hat values for feols or feglm estimations. Only works when
there are no fixed-effects.
## S3 method for class 'fixest' hatvalues(model, ...)## S3 method for class 'fixest' hatvalues(model, ...)
model |
A fixest object. For instance from feols or feglm. |
... |
Not currently used. |
Hat values are not available for fenegbin, femlm
and feNmlm estimations.
When there are fixed-effects, the hat values of the reduced form are different from the hat values of the full model. And we cannot get costlessly the hat values of the full model from the reduced form. It would require to reestimate the model with the fixed-effects as regular variables.
Returns a vector of the same length as the number of observations used in the estimation.
est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) head(hatvalues(est))est = feols(Petal.Length ~ Petal.Width + Sepal.Width, iris) head(hatvalues(est))
Treat a variable as a factor, or interacts a variable with a factor. Values to
be dropped/kept from the factor can be easily set. Note that to interact
fixed-effects, this function should not be used: instead use directly the syntax fe1^fe2.
i(factor_var, var, ref, keep, bin, ref2, keep2, bin2, ...)i(factor_var, var, ref, keep, bin, ref2, keep2, bin2, ...)
factor_var |
A vector (of any type) that will be treated as a factor.
You can set references (i.e. exclude values for which to create dummies) with
the |
var |
A variable of the same length as |
ref |
A vector of values to be taken as references from |
keep |
A vector of values to be kept from |
bin |
A list of values to be grouped, a vector, a formula, or the special
values |
ref2 |
A vector of values to be dropped from |
keep2 |
A vector of values to be kept from |
bin2 |
A list or vector defining the binning of the second variable.
See help for the argument |
... |
Not currently used. |
To interact fixed-effects, this function should not be used: instead use directly the syntax
fe1^fe2 in the fixed-effects part of the formula. Please see the details and
examples in the help page of feols.
It returns a matrix with number of rows the length of factor_var. If there is no interacted
variable or it is interacted with a numeric variable, the number of columns is equal to the
number of cases contained in factor_var minus the reference(s). If the interacted variable is
a factor, the number of columns is the number of combined cases between factor_var and var.
Laurent Berge
iplot to plot interactions or factors created with i(), feols for
OLS estimation with multiple fixed-effects.
See the function bin for binning variables.
# # Simple illustration # x = rep(letters[1:4], 3)[1:10] y = rep(1:4, c(1, 2, 3, 4)) # interaction data.frame(x, y, i(x, y, ref = TRUE)) # without interaction data.frame(x, i(x, "b")) # you can interact factors too z = rep(c("e", "f", "g"), c(5, 3, 2)) data.frame(x, z, i(x, z)) # to force a numeric variable to be treated as a factor: use i. data.frame(x, y, i(x, i.y)) # Binning data.frame(x, i(x, bin = list(ab = c("a", "b")))) # Same as before but using .() for list() and a regular expression # note that to trigger a regex, you need to use an @ first data.frame(x, i(x, bin = .(ab = "@a|b"))) # # In fixest estimations # data(base_did) # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id + period, base_did) # => plot only interactions with iplot iplot(est_did) # Using i() for factors est_bis = feols(y ~ x1 + i(period, keep = 3:6) + i(period, treat, 5) | id, base_did) # we plot the second set of variables created with i() # => we need to use keep (otherwise only the first one is represented) coefplot(est_bis, keep = "trea") # => special treatment in etable etable(est_bis, dict = c("6" = "six")) # # Interact two factors # # We use the i. prefix to consider week as a factor data(airquality) aq = airquality aq$week = aq$Day %/% 7 + 1 # Interacting Month and week: res_2F = feols(Ozone ~ Solar.R + i(Month, i.week), aq) # Same but dropping the 5th Month and 1st week res_2F_bis = feols(Ozone ~ Solar.R + i(Month, i.week, ref = 5, ref2 = 1), aq) etable(res_2F, res_2F_bis) # # Binning # data(airquality) feols(Ozone ~ i(Month, bin = "bin::2"), airquality) feols(Ozone ~ i(Month, bin = list(summer = 7:9)), airquality)# # Simple illustration # x = rep(letters[1:4], 3)[1:10] y = rep(1:4, c(1, 2, 3, 4)) # interaction data.frame(x, y, i(x, y, ref = TRUE)) # without interaction data.frame(x, i(x, "b")) # you can interact factors too z = rep(c("e", "f", "g"), c(5, 3, 2)) data.frame(x, z, i(x, z)) # to force a numeric variable to be treated as a factor: use i. data.frame(x, y, i(x, i.y)) # Binning data.frame(x, i(x, bin = list(ab = c("a", "b")))) # Same as before but using .() for list() and a regular expression # note that to trigger a regex, you need to use an @ first data.frame(x, i(x, bin = .(ab = "@a|b"))) # # In fixest estimations # data(base_did) # We interact the variable 'period' with the variable 'treat' est_did = feols(y ~ x1 + i(period, treat, 5) | id + period, base_did) # => plot only interactions with iplot iplot(est_did) # Using i() for factors est_bis = feols(y ~ x1 + i(period, keep = 3:6) + i(period, treat, 5) | id, base_did) # we plot the second set of variables created with i() # => we need to use keep (otherwise only the first one is represented) coefplot(est_bis, keep = "trea") # => special treatment in etable etable(est_bis, dict = c("6" = "six")) # # Interact two factors # # We use the i. prefix to consider week as a factor data(airquality) aq = airquality aq$week = aq$Day %/% 7 + 1 # Interacting Month and week: res_2F = feols(Ozone ~ Solar.R + i(Month, i.week), aq) # Same but dropping the 5th Month and 1st week res_2F_bis = feols(Ozone ~ Solar.R + i(Month, i.week, ref = 5, ref2 = 1), aq) etable(res_2F, res_2F_bis) # # Binning # data(airquality) feols(Ozone ~ i(Month, bin = "bin::2"), airquality) feols(Ozone ~ i(Month, bin = list(summer = 7:9)), airquality)
Lags a variable using panel id + time identifiers in a formula.
## S3 method for class 'formula' lag( x, k = 1, data, time.step = NULL, fill = NA, duplicate.method = c("none", "first"), ... ) lag_fml( x, k = 1, data, time.step = NULL, fill = NA, duplicate.method = c("none", "first"), ... )## S3 method for class 'formula' lag( x, k = 1, data, time.step = NULL, fill = NA, duplicate.method = c("none", "first"), ... ) lag_fml( x, k = 1, data, time.step = NULL, fill = NA, duplicate.method = c("none", "first"), ... )
x |
A formula of the type |
k |
An integer giving the number of lags. Default is 1. For leads, just use a negative number. |
data |
Optional, the data.frame in which to evaluate the formula. If not provided, variables will be fetched in the current environment. |
time.step |
The method to compute the lags, default is |
fill |
Scalar. How to fill the observations without defined lead/lag values.
Default is |
duplicate.method |
If several observations have the same id and time values,
then the notion of lag is not defined for them. If |
... |
Not currently used. |
It returns a vector of the same type and length as the variable to be lagged in the formula.
lag_fml(): Lags a variable using a formula syntax
Laurent Berge
Alternatively, the function panel changes a data.frame into a panel from which
the functions l and f (creating leads and lags) can be called. Otherwise you can set
the panel 'live' during the estimation using the argument panel.id (see for example in
the function feols).
# simple example with an unbalanced panel base = data.frame(id = rep(1:2, each = 4), time = c(1, 2, 3, 4, 1, 4, 6, 9), x = 1:8) base$lag1 = lag(x~id+time, 1, base) # lag 1 base$lead1 = lag(x~id+time, -1, base) # lead 1 base$lag2_fill0 = lag(x~id+time, 2, base, fill = 0) # with time.step = "consecutive" base$lag1_consecutive = lag(x~id+time, 1, base, time.step = "consecutive") # => works for indiv. 2 because 9 (resp. 6) is consecutive to 6 (resp. 4) base$lag1_within.consecutive = lag(x~id+time, 1, base, time.step = "within") # => now two consecutive years within each indiv is one lag print(base) # Argument time.step = "consecutive" is # mostly useful when the time variable is not a number: # e.g. c("1991q1", "1991q2", "1991q3") etc # with duplicates base_dup = data.frame(id = rep(1:2, each = 4), time = c(1, 1, 1, 2, 1, 2, 2, 3), x = 1:8) # Error because of duplicate values for (id, time) try(lag(x~id+time, 1, base_dup)) # Error is bypassed, lag corresponds to first occurence of (id, time) lag(x~id+time, 1, base_dup, duplicate.method = "first") # Playing with time steps base = data.frame(id = rep(1:2, each = 4), time = c(1, 2, 3, 4, 1, 4, 6, 9), x = 1:8) # time step: 0.5 (here equivalent to lag of 1) lag(x~id+time, 2, base, time.step = 0.5) # Error: wrong time step try(lag(x~id+time, 2, base, time.step = 7)) # Adding NAs + unsorted IDs base = data.frame(id = rep(1:2, each = 4), time = c(4, NA, 3, 1, 2, NA, 1, 3), x = 1:8) base$lag1 = lag(x~id+time, 1, base) base$lag1_within = lag(x~id+time, 1, base, time.step = "w") base_bis = base[order(base$id, base$time),] print(base_bis) # You can create variables without specifying the data within data.table: if(require("data.table")){ base = data.table(id = rep(1:2, each = 3), year = 1990 + rep(1:3, 2), x = 1:6) base[, x.l1 := lag(x~id+year, 1)] }# simple example with an unbalanced panel base = data.frame(id = rep(1:2, each = 4), time = c(1, 2, 3, 4, 1, 4, 6, 9), x = 1:8) base$lag1 = lag(x~id+time, 1, base) # lag 1 base$lead1 = lag(x~id+time, -1, base) # lead 1 base$lag2_fill0 = lag(x~id+time, 2, base, fill = 0) # with time.step = "consecutive" base$lag1_consecutive = lag(x~id+time, 1, base, time.step = "consecutive") # => works for indiv. 2 because 9 (resp. 6) is consecutive to 6 (resp. 4) base$lag1_within.consecutive = lag(x~id+time, 1, base, time.step = "within") # => now two consecutive years within each indiv is one lag print(base) # Argument time.step = "consecutive" is # mostly useful when the time variable is not a number: # e.g. c("1991q1", "1991q2", "1991q3") etc # with duplicates base_dup = data.frame(id = rep(1:2, each = 4), time = c(1, 1, 1, 2, 1, 2, 2, 3), x = 1:8) # Error because of duplicate values for (id, time) try(lag(x~id+time, 1, base_dup)) # Error is bypassed, lag corresponds to first occurence of (id, time) lag(x~id+time, 1, base_dup, duplicate.method = "first") # Playing with time steps base = data.frame(id = rep(1:2, each = 4), time = c(1, 2, 3, 4, 1, 4, 6, 9), x = 1:8) # time step: 0.5 (here equivalent to lag of 1) lag(x~id+time, 2, base, time.step = 0.5) # Error: wrong time step try(lag(x~id+time, 2, base, time.step = 7)) # Adding NAs + unsorted IDs base = data.frame(id = rep(1:2, each = 4), time = c(4, NA, 3, 1, 2, NA, 1, 3), x = 1:8) base$lag1 = lag(x~id+time, 1, base) base$lag1_within = lag(x~id+time, 1, base, time.step = "w") base_bis = base[order(base$id, base$time),] print(base_bis) # You can create variables without specifying the data within data.table: if(require("data.table")){ base = data.table(id = rep(1:2, each = 3), year = 1990 + rep(1:3, 2), x = 1:6) base[, x.l1 := lag(x~id+year, 1)] }
This function extracts the log-likelihood from a fixest estimation.
## S3 method for class 'fixest' logLik(object, ...)## S3 method for class 'fixest' logLik(object, ...)
object |
A |
... |
Not currently used. |
This function extracts the log-likelihood based on the model fit. You can have more
information on the likelihoods in the details of the function femlm.
It returns a numeric scalar.
Laurent Berge
See also the main estimation functions femlm, feols or feglm. Other
statistics functions: AIC.fixest, BIC.fixest.
# simple estimation on iris data with "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) nobs(res) logLik(res)# simple estimation on iris data with "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) nobs(res) logLik(res)
fixest objectThis function creates the left-hand-side or the right-hand-side(s) of a femlm,
feols or feglm estimation.
## S3 method for class 'fixest' model.matrix( object, data, type = "rhs", na.rm = TRUE, subset = FALSE, as.matrix = FALSE, as.df = FALSE, collin.rm = TRUE, ... )## S3 method for class 'fixest' model.matrix( object, data, type = "rhs", na.rm = TRUE, subset = FALSE, as.matrix = FALSE, as.df = FALSE, collin.rm = TRUE, ... )
object |
A |
data |
If missing (default) then the original data is obtained by evaluating
the |
type |
Character vector or one sided formula, default is "rhs". Contains the type of matrix/data.frame to be returned. Possible values are: "lhs", "rhs", "fixef", "iv.rhs1" (1st stage RHS), "iv.rhs2" (2nd stage RHS), "iv.endo" (endogenous vars.), "iv.exo" (exogenous vars), "iv.inst" (instruments). |
na.rm |
Default is |
subset |
Logical or character vector. Default is |
as.matrix |
Logical scalar, default is |
as.df |
Logical scalar, default is |
collin.rm |
Logical scalar, default is |
... |
Not currently used. |
It returns either a vector, a matrix or a data.frame. It returns a vector for the dependent variable ("lhs"), a data.frame for the fixed-effects ("fixef") and a matrix for any other type.
Laurent Berge
See also the main estimation functions femlm, feols or feglm. formula.fixest, update.fixest, summary.fixest, vcov.fixest.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ poly(x1, 2) + x2, base) head(model.matrix(est)) # Illustration of subset # subset => character vector head(model.matrix(est, subset = "x1")) # subset => TRUE, only works with data argument!! head(model.matrix(est, data = base[, "x1", drop = FALSE], subset = TRUE))base = iris names(base) = c("y", "x1", "x2", "x3", "species") est = feols(y ~ poly(x1, 2) + x2, base) head(model.matrix(est)) # Illustration of subset # subset => character vector head(model.matrix(est, subset = "x1")) # subset => TRUE, only works with data argument!! head(model.matrix(est, data = base[, "x1", drop = FALSE], subset = TRUE))
fixest_multi objectExtracts the meta information on all the models contained in a fixest_multi estimation.
models(x, simplify = FALSE)models(x, simplify = FALSE)
x |
A |
simplify |
Logical, default is |
It returns a data.frame whose first column (named id) is the index of the models and
the other columns contain the information specific to each model (e.g. which sample,
which RHS, which dependent variable, etc).
multiple estimations in feols, n_models
# a multiple estimation base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x.[, 1:3]), base, fsplit = ~species) # All the meta information models(est) # Illustration: Why use simplify est_sub = est[sample = 2] models(est_sub) models(est_sub, simplify = TRUE)# a multiple estimation base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x.[, 1:3]), base, fsplit = ~species) # All the meta information models(est) # Illustration: Why use simplify est_sub = est[sample = 2] models(est_sub) models(est_sub, simplify = TRUE)
fixest_multi objectsOtabin the number of unique models of a fixest_multi object, depending on the
type requested.
n_models( x, lhs = FALSE, rhs = FALSE, sample = FALSE, fixef = FALSE, iv = FALSE )n_models( x, lhs = FALSE, rhs = FALSE, sample = FALSE, fixef = FALSE, iv = FALSE )
x |
A |
lhs |
Logical scalar, default is |
rhs |
Logical scalar, default is |
sample |
Logical scalar, default is |
fixef |
Logical scalar, default is |
iv |
Logical scalar, default is |
It returns an integer scalar. If no argument is provided, the total number of models is returned.
Multiple estimations in feols, models
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x1, x2, x3), base, fsplit = ~species) # there are 3 different RHSs and 4 different samples models(est) # We can obtain these numbers with n_models n_models(est, rhs = TRUE) n_models(est, sample = TRUE)base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x1, x2, x3), base, fsplit = ~species) # there are 3 different RHSs and 4 different samples models(est) # We can obtain these numbers with n_models n_models(est, rhs = TRUE) n_models(est, sample = TRUE)
This utility tool displays the number of unique elements in one or multiple data.frames as well as their number of NA values.
n_unik(x) ## S3 method for class 'vec_n_unik' print(x, ...) ## S3 method for class 'list_n_unik' print(x, ...)n_unik(x) ## S3 method for class 'vec_n_unik' print(x, ...) ## S3 method for class 'list_n_unik' print(x, ...)
x |
A formula, with data set names on the LHS and variables on the RHS,
like |
... |
Not currently used. |
It returns a vector containing the number of unique values per element. If several data sets were provided, a list is returned, as long as the number of data sets, each element being a vector of unique values.
In the formula, you can use the following special values: ".", ".N", ".U", and ".NA".
"."Accesses the default values. If there is only one data set and the
data set is not a data.table, then the default is to display the number of
observations and the number of unique rows. If the data is a data.table, the number
of unique items in the key(s) is displayed instead of the number of unique rows
(if the table has keys of course). If there are two or more data sets, then the
default is to display the unique items for: a) the variables common across all data sets,
if there's less than 4, and b) if no variable is shown in a), the number of variables
common across at least two data sets, provided there are less than 5. If the data sets are
data tables, the keys are also displayed on top of the common variables. In any case, the
number of observations is always displayed.
".N"Displays the number of observations.
".U"Displays the number of unique rows.
".NA"Displays the number of rows with at least one NA.
NA functionThe special function NA is an equivalent to is.na but can handle several variables.
For instance, NA(x, y) is equivalent to is.na(x) | is.na(y). You can add as
many variables as you want as arguments. If no argument is provided, as in NA(),
it is identical to having all the variables of the data set as argument.
Use the "hat", "^", operator to combine several variables. For example id^period
will display the number of unique values of id x period combinations.
Use the "super hat", "%^%", operator to also include the terms on both sides.
For example, instead of writing id + period + id^period, you can simply write id%^%period.
Alternatively, you can use : for ^ and * for %^%.
To show the number of unique values for sub samples, simply use [].
For example, id[x > 10] will display the number of unique id for which x > 10.
Simple square brackets lead to the inclusion of both the variable and its subset.
For example id[x > 10] is equivalent to id + id[x > 10].
To include only the sub selection, use double square brackets, as in id[[x > 10]].
You can add multiple sub selections at once, only separate them with a comma.
For example id[x > 10, NA(y)] is equivalent to id[x > 10] + id[NA(y)].
Use the double negative operator, i.e. !!, to include both a condition and
its opposite at once. For example id[!!x > 10] is equivalent to id[x > 10, !x > 10].
Double negative operators can be chained, like in id[!!cond1 & !!cond2], then the
cardinal product of all double negatived conditions is returned.
Laurent Berge
data = base_did data$x1.L1 = round(lag(x1~id+period, 1, data)) # By default, just the formatted number of observations n_unik(data) # Or the nber of unique elements of a vector n_unik(data$id) # number of unique id values and id x period pairs n_unik(data ~.N + id + id^period) # use the %^% operator to include the terms on the two sides at once # => same as id*period n_unik(data ~.N + id %^% period) # using sub selection with [] n_unik(data ~.N + period[!NA(x1.L1)]) # to show only the sub selection: [[]] n_unik(data ~.N + period[[!NA(x1.L1)]]) # you can have multiple values in [], # just separate them with a comma n_unik(data ~.N + period[!NA(x1.L1), x1 > 7]) # to have both a condition and its opposite, # use the !! operator n_unik(data ~.N[!!NA(x1.L1)]) # the !! operator works within condition chains n_unik(data ~.N[!!NA(x1.L1) & !!x1 > 7]) # Conditions can be distributed n_unik(data ~ (id + period)[x1 > 7]) # # Several data sets # # Typical use case: merging # Let's create two data sets and merge them data(base_did) base_main = base_did base_extra = sample_df(base_main[, c("id", "period")], 100) base_extra$id[1:10] = 111:120 base_extra$period[11:20] = 11:20 base_extra$z = rnorm(100) # You can use db1:db2 to compare the common keys in two data sets n_unik(base_main:base_extra) tmp = merge(base_main, base_extra, all.x = TRUE, by = c("id", "period")) # You can show unique values for any variable, as before n_unik(tmp + base_main + base_extra ~ id[!!NA(z)] + id^period)data = base_did data$x1.L1 = round(lag(x1~id+period, 1, data)) # By default, just the formatted number of observations n_unik(data) # Or the nber of unique elements of a vector n_unik(data$id) # number of unique id values and id x period pairs n_unik(data ~.N + id + id^period) # use the %^% operator to include the terms on the two sides at once # => same as id*period n_unik(data ~.N + id %^% period) # using sub selection with [] n_unik(data ~.N + period[!NA(x1.L1)]) # to show only the sub selection: [[]] n_unik(data ~.N + period[[!NA(x1.L1)]]) # you can have multiple values in [], # just separate them with a comma n_unik(data ~.N + period[!NA(x1.L1), x1 > 7]) # to have both a condition and its opposite, # use the !! operator n_unik(data ~.N[!!NA(x1.L1)]) # the !! operator works within condition chains n_unik(data ~.N[!!NA(x1.L1) & !!x1 > 7]) # Conditions can be distributed n_unik(data ~ (id + period)[x1 > 7]) # # Several data sets # # Typical use case: merging # Let's create two data sets and merge them data(base_did) base_main = base_did base_extra = sample_df(base_main[, c("id", "period")], 100) base_extra$id[1:10] = 111:120 base_extra$period[11:20] = 11:20 base_extra$z = rnorm(100) # You can use db1:db2 to compare the common keys in two data sets n_unik(base_main:base_extra) tmp = merge(base_main, base_extra, all.x = TRUE, by = c("id", "period")) # You can show unique values for any variable, as before n_unik(tmp + base_main + base_extra ~ id[!!NA(z)] + id^period)
fixest objectThis function simply extracts the number of observations form a fixest object,
obtained using the functions femlm, feols or feglm.
## S3 method for class 'fixest' nobs(object, ...)## S3 method for class 'fixest' nobs(object, ...)
object |
A |
... |
Not currently used. |
It returns an interger.
Laurent Berge
See also the main estimation functions femlm, feols or feglm.
Use summary.fixest to see the results with the appropriate standard-errors,
fixef.fixest to extract the fixed-effects coefficients, and the function etable
to visualize the results of multiple estimations.
# simple estimation on iris data with "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) nobs(res) logLik(res)# simple estimation on iris data with "Species" fixed-effects res = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) nobs(res) logLik(res)
This function extracts the observations used in fixest estimation.
obs(x)obs(x)
x |
A |
It returns a simple vector of integers.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$y[1:5] = NA # Split sample estimations est_split = feols(y ~ x1, base, split = ~species) (obs_setosa = obs(est_split[[1]])) (obs_versi = obs(est_split[sample = "versi", drop = TRUE])) est_versi = feols(y ~ x1, base, subset = obs_versi) etable(est_split, est_versi)base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$y[1:5] = NA # Split sample estimations est_split = feols(y ~ x1, base, split = ~species) (obs_setosa = obs(est_split[[1]])) (obs_versi = obs(est_split[sample = "versi", drop = TRUE])) est_versi = feols(y ~ x1, base, subset = obs_versi) etable(est_split, est_versi)
Tools that returns a formatted object size, where the appropriate unit is automatically chosen.
osize(x) ## S3 method for class 'osize' print(x, ...)osize(x) ## S3 method for class 'osize' print(x, ...)
x |
Any R object. |
... |
Not currently used. |
Returns a character scalar.
Laurent Berge
osize(iris) data(trade) osize(trade)osize(iris) data(trade) osize(trade)
fixest panel data baseConstructs a fixest panel data base out of a data.frame which allows to use leads and lags
in fixest estimations and to create new variables from leads and lags if the data.frame
was also a data.table::data.table.
panel(data, panel.id, time.step = NULL, duplicate.method = c("none", "first"))panel(data, panel.id, time.step = NULL, duplicate.method = c("none", "first"))
data |
A data.frame. |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
time.step |
The method to compute the lags, default is |
duplicate.method |
If several observations have the same id and time values,
then the notion of lag is not defined for them. If |
This function allows you to use leads and lags in a fixest estimation without having to
provide the argument panel.id. It also offers more options on how to set the panel
(with the additional arguments 'time.step' and 'duplicate.method').
When the initial data set was also a data.table, not all operations are supported and some may
dissolve the fixest_panel. This is the case when creating subselections of the initial data
with additional attributes (e.g. pdt[x>0, .(x, y, z)] would dissolve the fixest_panel,
meaning only a data.table would be the result of the call).
If the initial data set was also a data.table, then you can create new variables from lags
and leads using the functions l and f. See the example.
It returns a data base identical to the one given in input, but with an additional attribute: “panel_info”. This attribute contains vectors used to efficiently create lags/leads of the data. When the data is subselected, some bookeeping is performed on the attribute “panel_info”.
Laurent Berge
The estimation methods feols, fepois and feglm.
The functions l and f to create lags and leads within fixest_panel objects.
data(base_did) # Setting a data set as a panel... pdat = panel(base_did, ~id+period) # ...then using the functions l and f est1 = feols(y~l(x1, 0:1), pdat) est2 = feols(f(y)~l(x1, -1:1), pdat) est3 = feols(l(y)~l(x1, 0:3), pdat) etable(est1, est2, est3, order = c("f", "^x"), drop="Int") # or using the argument panel.id feols(f(y)~l(x1, -1:1), base_did, panel.id = ~id+period) # You can use panel.id in various ways: pdat = panel(base_did, ~id+period) # is identical to: pdat = panel(base_did, c("id", "period")) # and also to: pdat = panel(base_did, "id,period") # l() and f() can also be used within a data.table: if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Now since pdat_dt is also a data.table # you can create lags/leads directly pdat_dt[, x1_l1 := l(x1)] pdat_dt[, c("x1_l1_fill0", "y_f2") := .(l(x1, fill = 0), f(y, 2))] }data(base_did) # Setting a data set as a panel... pdat = panel(base_did, ~id+period) # ...then using the functions l and f est1 = feols(y~l(x1, 0:1), pdat) est2 = feols(f(y)~l(x1, -1:1), pdat) est3 = feols(l(y)~l(x1, 0:3), pdat) etable(est1, est2, est3, order = c("f", "^x"), drop="Int") # or using the argument panel.id feols(f(y)~l(x1, -1:1), base_did, panel.id = ~id+period) # You can use panel.id in various ways: pdat = panel(base_did, ~id+period) # is identical to: pdat = panel(base_did, c("id", "period")) # and also to: pdat = panel(base_did, "id,period") # l() and f() can also be used within a data.table: if(require("data.table")){ pdat_dt = panel(as.data.table(base_did), ~id+period) # Now since pdat_dt is also a data.table # you can create lags/leads directly pdat_dt[, x1_l1 := l(x1)] pdat_dt[, c("x1_l1_fill0", "y_f2") := .(l(x1, fill = 0), f(y, 2))] }
This function plots the 5 fixed-effects with the highest and lowest values, for
each of the fixed-effect dimension. It takes as an argument the fixed-effects obtained
from the function fixef.fixest after an estimation using femlm, feols or feglm.
## S3 method for class 'fixest.fixef' plot(x, n = 5, ...)## S3 method for class 'fixest.fixef' plot(x, n = 5, ...)
x |
An object obtained from the function |
n |
The number of fixed-effects to be drawn. Defaults to 5. |
... |
Not currently used. Note that the fixed-effect coefficients might NOT be interpretable. This function is useful only for fully regular panels. If the data are not regular in the fixed-effect coefficients, this means that several ‘reference points’ are set to obtain the fixed-effects, thereby impeding their interpretation. In this case a warning is raised. |
Laurent Berge
fixef.fixest to extract clouster coefficients. See also the main
estimation function femlm, feols or feglm. Use summary.fixest to see
the results with the appropriate standard-errors, the function etable to
visualize the results of multiple estimations.
data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # obtaining the fixed-effects coefficients fe_trade = fixef(est_pois) # plotting them plot(fe_trade)data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # obtaining the fixed-effects coefficients fe_trade = fixef(est_pois) # plotting them plot(fe_trade)
fixest fitsThis function obtains prediction from a fitted model estimated with femlm,
feols or feglm.
## S3 method for class 'fixest' predict( object, newdata, type = c("response", "link"), se.fit = FALSE, interval = "none", level = 0.95, fixef = FALSE, vs.coef = FALSE, sample = c("estimation", "original"), vcov = NULL, ssc = NULL, ... )## S3 method for class 'fixest' predict( object, newdata, type = c("response", "link"), se.fit = FALSE, interval = "none", level = 0.95, fixef = FALSE, vs.coef = FALSE, sample = c("estimation", "original"), vcov = NULL, ssc = NULL, ... )
object |
A |
newdata |
A data.frame containing the variables used to make the prediction.
If not provided, the fitted expected (or linear if |
type |
Character either equal to |
se.fit |
Logical, default is |
interval |
Either "none" (default), "confidence" or "prediction". What type of confidence interval to compute. Note that this feature is only available for OLS models not containing fixed-effects (GLM/ML models are not covered). |
level |
A numeric scalar in between 0.5 and 1, defaults to 0.95. Only used when the argument 'interval' is requested, it corresponds to the width of the confidence interval. |
fixef |
Logical scalar, default is |
vs.coef |
Logical scalar, default is |
sample |
Either "estimation" (default) or "original". This argument is only used when arg. 'newdata' is missing, and is ignored otherwise. If equal to "estimation", the vector returned matches the sample used for the estimation. If equal to "original", it matches the original data set (the observations not used for the estimation being filled with NAs). |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
ssc |
An object of class |
... |
Not currently used. |
It returns a numeric vector of length equal to the number of observations in argument newdata.
If newdata is missing, it returns a vector of the same length as the estimation sample,
except if sample = "original", in which case the length of the vector will match the one
of the original data set (which can, but also cannot, be the estimation sample).
If fixef = TRUE, a data.frame is returned.
If se.fit = TRUE or interval != "none", the object returned is a data.frame
with the following columns: fit, se.fit, and, if CIs are requested, ci_low and ci_high.
Laurent Berge
See also the main estimation functions femlm, feols or feglm. update.fixest, summary.fixest, vcov.fixest, fixef.fixest.
# Estimation on iris data res = fepois(Sepal.Length ~ Petal.Length | Species, iris) # what would be the prediction if the data was all setosa? newdata = data.frame(Petal.Length = iris$Petal.Length, Species = "setosa") pred_setosa = predict(res, newdata = newdata) # Let's look at it graphically plot(c(1, 7), c(3, 11), type = "n", xlab = "Petal.Length", ylab = "Sepal.Length") newdata = iris[order(iris$Petal.Length), ] newdata$Species = "setosa" lines(newdata$Petal.Length, predict(res, newdata)) # versicolor newdata$Species = "versicolor" lines(newdata$Petal.Length, predict(res, newdata), col=2) # virginica newdata$Species = "virginica" lines(newdata$Petal.Length, predict(res, newdata), col=3) # The original data points(iris$Petal.Length, iris$Sepal.Length, col = iris$Species, pch = 18) legend("topleft", lty = 1, col = 1:3, legend = levels(iris$Species)) # # Getting the fixed-effect coefficients for each obs. # data(trade) est_trade = fepois(Euros ~ log(dist_km) | Destination^Product + Origin^Product + Year, trade) obs_fe = predict(est_trade, fixef = TRUE) head(obs_fe) # can we check we get the right sum of fixed-effects head(cbind(rowSums(obs_fe), est_trade$sumFE)) # # Standard-error of the prediction # base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ x1 + species, base) head(predict(est, se.fit = TRUE)) # regular confidence interval head(predict(est, interval = "conf")) # adding the residual to the CI head(predict(est, interval = "predi")) # You can change the type of SE on the fly head(predict(est, interval = "conf", vcov = ~species))# Estimation on iris data res = fepois(Sepal.Length ~ Petal.Length | Species, iris) # what would be the prediction if the data was all setosa? newdata = data.frame(Petal.Length = iris$Petal.Length, Species = "setosa") pred_setosa = predict(res, newdata = newdata) # Let's look at it graphically plot(c(1, 7), c(3, 11), type = "n", xlab = "Petal.Length", ylab = "Sepal.Length") newdata = iris[order(iris$Petal.Length), ] newdata$Species = "setosa" lines(newdata$Petal.Length, predict(res, newdata)) # versicolor newdata$Species = "versicolor" lines(newdata$Petal.Length, predict(res, newdata), col=2) # virginica newdata$Species = "virginica" lines(newdata$Petal.Length, predict(res, newdata), col=3) # The original data points(iris$Petal.Length, iris$Sepal.Length, col = iris$Species, pch = 18) legend("topleft", lty = 1, col = 1:3, legend = levels(iris$Species)) # # Getting the fixed-effect coefficients for each obs. # data(trade) est_trade = fepois(Euros ~ log(dist_km) | Destination^Product + Origin^Product + Year, trade) obs_fe = predict(est_trade, fixef = TRUE) head(obs_fe) # can we check we get the right sum of fixed-effects head(cbind(rowSums(obs_fe), est_trade$sumFE)) # # Standard-error of the prediction # base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ x1 + species, base) head(predict(est, se.fit = TRUE)) # regular confidence interval head(predict(est, interval = "conf")) # adding the residual to the CI head(predict(est, interval = "predi")) # You can change the type of SE on the fly head(predict(est, interval = "conf", vcov = ~species))
fixest objects.This function is very similar to usual summary functions as it
provides the table of coefficients along with other information on the fit of
the estimation. The type of output can be customized by the user (using
function setFixest_print).
## S3 method for class 'fixest' print(x, n, type = "table", fitstat = NULL, ...) setFixest_print(type = "table", fitstat = NULL) getFixest_print()## S3 method for class 'fixest' print(x, n, type = "table", fitstat = NULL, ...) setFixest_print(type = "table", fitstat = NULL) getFixest_print()
x |
A |
n |
Integer, number of coefficients to display. By default, only the
first 8 coefficients are displayed if |
type |
Either |
fitstat |
A formula or a character vector representing which fit
statistic to display. The types must be valid types of the function
|
... |
Other arguments to be passed to |
It is possible to set the default values for the arguments
type and fitstat by using the function setFixest_print.
Laurent Berge
See also the main estimation functions femlm,
feols or feglm. Use
summary.fixest to see the results with the appropriate
standard-errors, fixef.fixest to extract the
fixed-effects coefficients, and the function etable to
visualize the results of multiple estimations.
# Load trade data data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects (FEs) est_pois = fepois(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # displaying the results # (by default SEs are clustered if FEs are used) print(est_pois) # By default the coefficient table is displayed. # If the user wished to display only the coefficents, use option type: print(est_pois, type = "coef") # To permanently display coef. only, use setFixest_print: setFixest_print(type = "coef") est_pois # back to default: setFixest_print(type = "table") # # fitstat # # We modify which fit statistic to display print(est_pois, fitstat = ~ . + lr) # We add the LR test to the default (represented by the ".") # to show only the LR stat: print(est_pois, fitstat = ~ . + lr.stat) # To modify the defaults: setFixest_print(fitstat = ~ . + lr.stat + rmse) est_pois # Back to default (NULL == default) setFixest_print(fitstat = NULL)# Load trade data data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects (FEs) est_pois = fepois(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # displaying the results # (by default SEs are clustered if FEs are used) print(est_pois) # By default the coefficient table is displayed. # If the user wished to display only the coefficents, use option type: print(est_pois, type = "coef") # To permanently display coef. only, use setFixest_print: setFixest_print(type = "coef") est_pois # back to default: setFixest_print(type = "table") # # fitstat # # We modify which fit statistic to display print(est_pois, fitstat = ~ . + lr) # We add the LR test to the default (represented by the ".") # to show only the LR stat: print(est_pois, fitstat = ~ . + lr.stat) # To modify the defaults: setFixest_print(fitstat = ~ . + lr.stat + rmse) est_pois # Back to default (NULL == default) setFixest_print(fitstat = NULL)
Displays a brief summary of selected fit statistics from the function fitstat.
## S3 method for class 'fixest_fitstat' print(x, na.rm = FALSE, ...)## S3 method for class 'fixest_fitstat' print(x, na.rm = FALSE, ...)
x |
An object resulting from the |
na.rm |
Logical, default is |
... |
Not currently used. |
data(trade) gravity = feols(log(Euros) ~ log(dist_km) | Destination + Origin, trade) # Extracting the 'working' number of observations used to compute the pvalues fitstat(gravity, "g", simplify = TRUE) # Some fit statistics fitstat(gravity, ~ rmse + r2 + wald + wf) # You can use them in etable etable(gravity, fitstat = ~ rmse + r2 + wald + wf) # For wald and wf, you could show the pvalue instead: etable(gravity, fitstat = ~ rmse + r2 + wald.p + wf.p) # Now let's display some statistics that are not built-in # => we use fitstat_register to create them # We need: a) type name, b) the function to be applied # c) (optional) an alias fitstat_register("tstand", function(x) tstat(x, se = "stand")[1], "t-stat (regular)") fitstat_register("thc", function(x) tstat(x, se = "heter")[1], "t-stat (HC1)") fitstat_register("t1w", function(x) tstat(x, se = "clus")[1], "t-stat (clustered)") fitstat_register("t2w", function(x) tstat(x, se = "twow")[1], "t-stat (2-way)") # Now we can use these keywords in fitstat: etable(gravity, fitstat = ~ . + tstand + thc + t1w + t2w) # Note that the custom stats we created are can easily lead # to errors, but that's another story!data(trade) gravity = feols(log(Euros) ~ log(dist_km) | Destination + Origin, trade) # Extracting the 'working' number of observations used to compute the pvalues fitstat(gravity, "g", simplify = TRUE) # Some fit statistics fitstat(gravity, ~ rmse + r2 + wald + wf) # You can use them in etable etable(gravity, fitstat = ~ rmse + r2 + wald + wf) # For wald and wf, you could show the pvalue instead: etable(gravity, fitstat = ~ rmse + r2 + wald.p + wf.p) # Now let's display some statistics that are not built-in # => we use fitstat_register to create them # We need: a) type name, b) the function to be applied # c) (optional) an alias fitstat_register("tstand", function(x) tstat(x, se = "stand")[1], "t-stat (regular)") fitstat_register("thc", function(x) tstat(x, se = "heter")[1], "t-stat (HC1)") fitstat_register("t1w", function(x) tstat(x, se = "clus")[1], "t-stat (clustered)") fitstat_register("t2w", function(x) tstat(x, se = "twow")[1], "t-stat (2-way)") # Now we can use these keywords in fitstat: etable(gravity, fitstat = ~ . + tstand + thc + t1w + t2w) # Note that the custom stats we created are can easily lead # to errors, but that's another story!
Displays summary information on fixest_multi objects in the R console.
## S3 method for class 'fixest_multi' print(x, ...)## S3 method for class 'fixest_multi' print(x, ...)
x |
A |
... |
Other arguments to be passed to |
The main fixest estimation functions: feols, fepois,
fenegbin, feglm, feNmlm. Tools for mutliple fixest
estimations: summary.fixest_multi, print.fixest_multi, as.list.fixest_multi,
sub-sub-.fixest_multi, sub-.fixest_multi.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # Let's print all that resbase = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # Let's print all that res
fixest modelsReports different R2s for fixest estimations (e.g. feglm or feols).
r2(x, type = "all", full_names = FALSE)r2(x, type = "all", full_names = FALSE)
x |
A |
type |
A character vector representing the R2 to compute. The R2 codes are of the form:
"wapr2" with letters "w" (within), "a" (adjusted) and "p" (pseudo) possibly missing.
E.g. to get the regular R2: use |
full_names |
Logical scalar, default is |
The pseudo R2s are the McFaddens R2s, that is the ratio of log-likelihoods.
For R2s with no theoretical justification, like e.g. regular R2s for maximum likelihood models – or within R2s for models without fixed-effects, NA is returned. The single measure to possibly compare all kinds of models is the squared correlation between the dependent variable and the expected predictor.
The pseudo-R2 is also returned in the OLS case, it corresponds to the pseudo-R2 of the equivalent GLM model with a Gaussian family.
For the adjusted within-R2s, the adjustment factor is (n - nb_fe) / (n - nb_fe - K)
with n the number of observations, nb_fe the number of fixed-effects and K
the number of variables.
Returns a named vector.
Laurent Berge
# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est = feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product, trade) # Squared correlation: r2(est, "cor2") # "regular" r2: r2(est, "r2") # pseudo r2 (equivalent to GLM with Gaussian family) r2(est, "pr2") # adjusted within r2 r2(est, "war2") # all four at once r2(est, c("cor2", "r2", "pr2", "war2")) # same with full names instead of codes r2(est, c("cor2", "r2", "pr2", "war2"), full_names = TRUE)# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est = feols(log(Euros) ~ log(dist_km) | Origin + Destination + Product, trade) # Squared correlation: r2(est, "cor2") # "regular" r2: r2(est, "r2") # pseudo r2 (equivalent to GLM with Gaussian family) r2(est, "pr2") # adjusted within r2 r2(est, "war2") # all four at once r2(est, c("cor2", "r2", "pr2", "war2")) # same with full names instead of codes r2(est, c("cor2", "r2", "pr2", "war2"), full_names = TRUE)
Takes a variables of any types, transforms it into a factors, and modifies the values of the factors. Useful in estimations when you want to set some value of a vector as a reference.
ref(x, ref)ref(x, ref)
x |
A vector of any type (must be atomic though). |
ref |
A vector or a list, or special binning values (explained later). If a vector,
it must correspond to (partially matched) values of the vector |
It returns a factor of the same length as x, where levels have been modified according
to the argument ref.
Numeric vectors can be cut easily into: a) equal parts, b) user-specified bins.
Use "cut::n" to cut the vector into n (roughly) equal parts. Percentiles are
used to partition the data, hence some data distributions can lead to create less
than n parts (for example if P0 is the same as P50).
The user can specify custom bins with the following syntax: "cut::a]b]c]". Here
the numbers a, b, c, etc, are a sequence of increasing numbers, each followed
by an open or closed square bracket. The numbers can be specified as either
plain numbers (e.g. "cut::5]12[32["), quartiles (e.g. "cut::q1]q3["),
or percentiles (e.g. "cut::p10]p15]p90]"). Values of different types can be mixed:
"cut::5]q2[p80[" is valid provided the median (q2) is indeed greater
than 5, otherwise an error is thrown.
The square bracket right of each number tells whether the numbers should be included
or excluded from the current bin. For example, say x ranges from 0 to 100,
then "cut::5]" will create two bins: one from 0 to 5 and a second from 6 to 100.
With "cut::5[" the bins would have been 0-4 and 5-100.
A factor is always returned. The labels always report the min and max values in each bin.
To have user-specified bin labels, just add them in the character vector
following 'cut::values'. You don't need to provide all of them, and NA values
fall back to the default label. For example, bin = c("cut::4", "Q1", NA, "Q3")
will modify only the first and third label that will be displayed as "Q1" and "Q3".
bin vs ref
The functions bin and ref are able to do the same thing, then why use one
instead of the other? Here are the differences:
ref always returns a factor. This is in contrast with bin which returns,
when possible, a vector of the same type as the vector in input.
ref always places the values modified in the first place of the factor levels.
On the other hand, bin tries to not modify the ordering of the levels. It is possible
to make bin mimic the behavior of ref by adding an "@" as the first element of
the list in the argument bin.
when a vector (and not a list) is given in input, ref will place each element of
the vector in the first place of the factor levels. The behavior of bin is
totally different, bin will transform all the values in the vector into a single
value in x (i.e. it's binning).
Laurent Berge
To bin the values of a vector: bin.
data(airquality) # A vector of months month_num = airquality$Month month_lab = c("may", "june", "july", "august", "september") month_fact = factor(month_num, labels = month_lab) table(month_num) table(month_fact) # # Main use # # Without argument: equivalent to as.factor ref(month_num) # Main usage: to set a level first: # (Note that partial matching is enabled.) table(ref(month_fact, "aug")) # You can rename the level on-the-fly # (Northern hemisphere specific!) table(ref(month_fact, .("Hot month"="aug", "Late summer" = "sept"))) # Main use is in estimations: a = feols(Petal.Width ~ Petal.Length + Species, iris) # We change the reference b = feols(Petal.Width ~ Petal.Length + ref(Species, "vers"), iris) etable(a, b) # # Binning # # You can also bin factor values on the fly # Using @ first means a regular expression will be used to match the values. # Note that the value created is placed first. # To avoid that behavior => use the function "bin" table(ref(month_fact, .(summer = "@jul|aug|sep"))) # Please refer to the example in the bin help page for more example. # The syntax is the same. # # Precise relocation # # You can place a factor at the location you want # by adding "@digit" in the name first: table(ref(month_num, .("@5"=5))) # Same with renaming table(ref(month_num, .("@5 five"=5)))data(airquality) # A vector of months month_num = airquality$Month month_lab = c("may", "june", "july", "august", "september") month_fact = factor(month_num, labels = month_lab) table(month_num) table(month_fact) # # Main use # # Without argument: equivalent to as.factor ref(month_num) # Main usage: to set a level first: # (Note that partial matching is enabled.) table(ref(month_fact, "aug")) # You can rename the level on-the-fly # (Northern hemisphere specific!) table(ref(month_fact, .("Hot month"="aug", "Late summer" = "sept"))) # Main use is in estimations: a = feols(Petal.Width ~ Petal.Length + Species, iris) # We change the reference b = feols(Petal.Width ~ Petal.Length + ref(Species, "vers"), iris) etable(a, b) # # Binning # # You can also bin factor values on the fly # Using @ first means a regular expression will be used to match the values. # Note that the value created is placed first. # To avoid that behavior => use the function "bin" table(ref(month_fact, .(summer = "@jul|aug|sep"))) # Please refer to the example in the bin help page for more example. # The syntax is the same. # # Precise relocation # # You can place a factor at the location you want # by adding "@digit" in the name first: table(ref(month_num, .("@5"=5))) # Same with renaming table(ref(month_num, .("@5 five"=5)))
fixest objectsSimple function that replicates fixest objects while (optionally) computing different
standard-errors. Useful mostly in combination with etable or coefplot.
## S3 method for class 'fixest' rep(x, times = 1, each = 1, vcov, ...) ## S3 method for class 'fixest_list' rep(x, times = 1, each = 1, vcov, ...) .l(...)## S3 method for class 'fixest' rep(x, times = 1, each = 1, vcov, ...) ## S3 method for class 'fixest_list' rep(x, times = 1, each = 1, vcov, ...) .l(...)
x |
Either a |
times |
Integer vector giving the number of repetitions of the vector of elements. By
default |
each |
Integer scalar indicating the repetition of each element. Default is 1. |
vcov |
A list containing the types of standard-error to be computed, default is missing. If
not missing, it must be of the same length as |
... |
In |
To apply rep.fixest on a list of fixest objects, it is absolutely necessary to use
.l() and not list().
Returns a list of the appropriate length. Each element of the list is a fixest object.
# Let's show results with different standard-errors est = feols(Ozone ~ Solar.R + Wind + Temp, data = airquality) my_vcov = list(~ Month, ~ Day, ~ Day + Month) etable(rep(est, vcov = my_vcov)) coefplot(rep(est, vcov = my_vcov), drop = "Int") # # To rep multiple objects, you need to use .l() # est_bis = feols(Ozone ~ Solar.R + Wind + Temp | Month, airquality) etable(rep(.l(est, est_bis), vcov = my_vcov)) # using each etable(rep(.l(est, est_bis), each = 3, vcov = my_vcov))# Let's show results with different standard-errors est = feols(Ozone ~ Solar.R + Wind + Temp, data = airquality) my_vcov = list(~ Month, ~ Day, ~ Day + Month) etable(rep(est, vcov = my_vcov)) coefplot(rep(est, vcov = my_vcov), drop = "Int") # # To rep multiple objects, you need to use .l() # est_bis = feols(Ozone ~ Solar.R + Wind + Temp | Month, airquality) etable(rep(.l(est, est_bis), vcov = my_vcov)) # using each etable(rep(.l(est, est_bis), each = 3, vcov = my_vcov))
fixest objectThis function extracts residuals from a fitted model estimated with femlm,
feols or feglm.
## S3 method for class 'fixest' resid( object, type = c("response", "deviance", "pearson", "working"), na.rm = TRUE, ... ) ## S3 method for class 'fixest' residuals( object, type = c("response", "deviance", "pearson", "working"), na.rm = TRUE, ... )## S3 method for class 'fixest' resid( object, type = c("response", "deviance", "pearson", "working"), na.rm = TRUE, ... ) ## S3 method for class 'fixest' residuals( object, type = c("response", "deviance", "pearson", "working"), na.rm = TRUE, ... )
object |
A |
type |
A character scalar, either |
na.rm |
Logical, default is |
... |
Not currently used. |
It returns a numeric vector of the length the number of observations used for the estimation
(if na.rm = TRUE) or of the length of the original data set (if na.rm = FALSE).
Laurent Berge
See also the main estimation functions femlm, feols or feglm. fitted.fixest, predict.fixest, summary.fixest, vcov.fixest, fixef.fixest.
# simple estimation on iris data, using "Species" fixed-effects res_poisson = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # we plot the residuals plot(resid(res_poisson))# simple estimation on iris data, using "Species" fixed-effects res_poisson = femlm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width | Species, iris) # we plot the residuals plot(resid(res_poisson))
fixest_multi objectUtility to extract the residuals from multiple fixest estimations. If possible,
all the residuals are coerced into a matrix.
## S3 method for class 'fixest_multi' resid( object, type = c("response", "deviance", "pearson", "working"), na.rm = FALSE, ... ) ## S3 method for class 'fixest_multi' residuals( object, type = c("response", "deviance", "pearson", "working"), na.rm = FALSE, ... )## S3 method for class 'fixest_multi' resid( object, type = c("response", "deviance", "pearson", "working"), na.rm = FALSE, ... ) ## S3 method for class 'fixest_multi' residuals( object, type = c("response", "deviance", "pearson", "working"), na.rm = FALSE, ... )
object |
A |
type |
A character scalar, either |
na.rm |
Logical, default is |
... |
Not currently used. |
If all the models return residuals of the same length, a matrix is returned. Otherwise,
a list is returned.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # A multiple estimation est = feols(y ~ x1 + csw0(x2, x3), base) # We can get all the residuals at once, # each column is a model head(resid(est)) # We can select/order the model using fixest_multi extraction head(resid(est[rhs = .N:1]))base = iris names(base) = c("y", "x1", "x2", "x3", "species") # A multiple estimation est = feols(y ~ x1 + csw0(x2, x3), base) # We can get all the residuals at once, # each column is a model head(resid(est)) # We can select/order the model using fixest_multi extraction head(resid(est[rhs = .N:1]))
This function is useful to check a data set. It gives a random number of rows of the input data set.
sample_df(x, n = 10, previous = FALSE)sample_df(x, n = 10, previous = FALSE)
x |
A data set: either a vector, a matrix or a data frame. |
n |
The number of random rows/elements to sample randomly. |
previous |
Logical scalar. Whether the results of the previous draw should be returned. |
A data base (resp vector) with n rows (resp elements).
Laurent Berge
sample_df(iris) sample_df(iris, previous = TRUE)sample_df(iris) sample_df(iris, previous = TRUE)
You can set the default values of most arguments of coefplot with this function.
setFixest_coefplot( style, horiz = FALSE, dict = getFixest_dict(), keep, ci.width = "1%", ci_level = 0.95, pt.pch = 20, pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, sep, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL, reset = FALSE ) getFixest_coefplot()setFixest_coefplot( style, horiz = FALSE, dict = getFixest_dict(), keep, ci.width = "1%", ci_level = 0.95, pt.pch = 20, pt.bg = NULL, cex = 1, pt.cex = cex, col = 1:8, pt.col = col, ci.col = col, lwd = 1, pt.lwd = lwd, ci.lwd = lwd, ci.lty = 1, grid = TRUE, grid.par = list(lty = 3, col = "gray"), zero = TRUE, zero.par = list(col = "black", lwd = 1), pt.join = FALSE, pt.join.par = list(col = pt.col, lwd = lwd), ci.join = FALSE, ci.join.par = list(lwd = lwd, col = col, lty = 2), ci.fill = FALSE, ci.fill.par = list(col = "lightgray", alpha = 0.5), ref.line = "auto", ref.line.par = list(col = "black", lty = 2), lab.cex, lab.min.cex = 0.85, lab.max.mar = 0.25, lab.fit = "auto", xlim.add, ylim.add, sep, bg, group = "auto", group.par = list(lwd = 2, line = 3, tcl = 0.75), main = "Effect on __depvar__", value.lab = "Estimate and __ci__ Conf. Int.", ylab = NULL, xlab = NULL, sub = NULL, reset = FALSE ) getFixest_coefplot()
style |
A character scalar giving the style of the plot to be used. You
can set styles with the function |
horiz |
A logical scalar, default is |
dict |
A named character vector or a logical scalar. It changes the original variable names
to the ones contained in the |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
ci.width |
The width of the extremities of the confidence intervals. Default is |
ci_level |
Scalar between 0 and 1: the level of the CI. By default it is equal to 0.95. |
pt.pch |
The patch of the coefficient estimates. Default is 1 (circle). |
pt.bg |
The background color of the point estimate (when the |
cex |
Numeric, default is 1. Expansion factor for the points |
pt.cex |
The size of the coefficient estimates. Default is the other argument |
col |
The color of the points and the confidence intervals. Default is 1
("black"). Note that you can set the colors separately for each of them with |
pt.col |
The color of the coefficient estimates. Default is equal to the other argument |
ci.col |
The color of the confidence intervals. Default is equal to the other argument |
lwd |
General line with. Default is 1. |
pt.lwd |
The line width of the coefficient estimates. Default is equal to
the other argument |
ci.lwd |
The line width of the confidence intervals. Default is equal to
the other argument |
ci.lty |
The line type of the confidence intervals. Default is 1. |
grid |
Logical, default is |
grid.par |
List. Parameters of the grid. The default values are: |
zero |
Logical, default is |
zero.par |
List. Parameters of the zero-line. The default values are
|
pt.join |
Logical, default is |
pt.join.par |
List. Parameters of the line joining the coefficients. The
default values are: |
ci.join |
Logical default to |
ci.join.par |
A list of parameters to be passed to |
ci.fill |
Logical default to |
ci.fill.par |
A list of parameters to be passed to |
ref.line |
Logical or numeric, default is "auto", whose behavior depends
on the situation. It is |
ref.line.par |
List. Parameters of the vertical line on the reference. The
default values are: |
lab.cex |
The size of the labels of the coefficients. Default is missing.
It is automatically set by an internal algorithm which can go as low as |
lab.min.cex |
The minimum size of the coefficients labels, as set by the internal algorithm. Default is 0.85. |
lab.max.mar |
The maximum size the left margin can take when trying to fit
the coefficient labels into it (only when |
lab.fit |
The method to fit the coefficient labels into the plotting region
(only when |
xlim.add |
A numeric vector of length 1 or 2. It represents an extension
factor of xlim, in percentage. Eg: |
ylim.add |
A numeric vector of length 1 or 2. It represents an extension
factor of ylim, in percentage. Eg: |
sep |
The distance between two estimates – only when argument |
bg |
Background color for the plot. By default it is white. |
group |
A list, default is missing. Each element of the list reports the
coefficients to be grouped while the name of the element is the group name. Each
element of the list can be either: i) a character vector of length 1, ii) of
length 2, or ii) a numeric vector. If equal to: i) then it is interpreted as
a pattern: all element fitting the regular expression will be grouped (note that
you can use the special character "^^" to clean the beginning of the names, see
example), if ii) it corresponds to the first and last elements to be grouped,
if iii) it corresponds to the coefficients numbers to be grouped. If equal to
a character vector, you can use a percentage to tell the algorithm to look at
the coefficients before aliasing (e.g. |
group.par |
A list of parameters controlling the display of the group. The
parameters controlling the line are: |
main |
The title of the plot. Default is |
value.lab |
The label to appear on the side of the coefficient values. If
|
ylab |
The label of the y-axis, default is |
xlab |
The label of the x-axis, default is |
sub |
A subtitle, default is |
reset |
Logical, default is |
Doesn't return anything.
# coefplot has many arguments, which makes it highly flexible. # If you don't like the default style of coefplot. No worries, # you can set *your* default by using the function # setFixest_coefplot() # Estimation est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width | Species, iris) # Plot with default style coefplot(est) # Now we permanently change some arguments dict = c("Petal.Length"="Length (Petal)", "Petal.Width"="Width (Petal)", "Sepal.Length"="Length (Sepal)", "Sepal.Width"="Width (Sepal)") setFixest_coefplot(ci.col = 2, pt.col = "darkblue", ci.lwd = 3, pt.cex = 2, pt.pch = 15, ci.width = 0, dict = dict) # Tadaaa! coefplot(est) # To reset to the default settings: setFixest_coefplot("all", reset = TRUE) coefplot(est)# coefplot has many arguments, which makes it highly flexible. # If you don't like the default style of coefplot. No worries, # you can set *your* default by using the function # setFixest_coefplot() # Estimation est = feols(Petal.Length ~ Petal.Width + Sepal.Length + Sepal.Width | Species, iris) # Plot with default style coefplot(est) # Now we permanently change some arguments dict = c("Petal.Length"="Length (Petal)", "Petal.Width"="Width (Petal)", "Sepal.Length"="Length (Sepal)", "Sepal.Width"="Width (Sepal)") setFixest_coefplot(ci.col = 2, pt.col = "darkblue", ci.lwd = 3, pt.cex = 2, pt.pch = 15, ci.width = 0, dict = dict) # Tadaaa! coefplot(est) # To reset to the default settings: setFixest_coefplot("all", reset = TRUE) coefplot(est)
Sets/gets the default dictionary used in the function etable, did_means and
coefplot. The dictionaries are used to relabel variables (usually towards a fancier, more
explicit formatting) when exporting them into a Latex table or displaying in graphs. By setting
the dictionary with setFixest_dict, you can avoid providing the argument dict.
setFixest_dict(dict = NULL, ..., reset = FALSE) getFixest_dict()setFixest_dict(dict = NULL, ..., reset = FALSE) getFixest_dict()
dict |
A named character vector or a character scalar. E.g. to change my variable named "a"
and "b" to (resp.) "$log(a)$" and "$bonus^3$", then use
|
... |
You can add arguments of the form: |
reset |
Logical, default is |
By default the dictionary only grows. This means that successive calls with not erase the
previous definitions unless the argument reset has been set to TRUE.
The default dictionary is equivalent to having setFixest_dict("(Intercept)" = "Constant"). To
change this default, you need to provide a new definition to "(Intercept)" explicitly.
Laurent Berge
data(trade) est = feols(log(Euros) ~ log(dist_km)|Origin+Destination+Product, trade) # we export the result & rename some variables etable(est, dict = c("log(Euros)"="Euros (ln)", Origin="Country of Origin")) # If you export many tables, it can be more convenient to use setFixest_dict: setFixest_dict(c("log(Euros)"="Euros (ln)", Origin="Country of Origin")) etable(est) # variables are properly relabeled # The dictionary only 'grows' # Here you get the previous two variables + the new one that are relabeled # Btw you set the dictionary directly using the argument names: setFixest_dict(Destination = "Country of Destination") etable(est) # Another way to set a dictionary: with a character string: # See the help page of as.dict dict = "log(dist_km): Distance (ln); Product: Type of Good" setFixest_dict(dict) etable(est) # And now we reset: setFixest_dict(reset = TRUE) etable(est)data(trade) est = feols(log(Euros) ~ log(dist_km)|Origin+Destination+Product, trade) # we export the result & rename some variables etable(est, dict = c("log(Euros)"="Euros (ln)", Origin="Country of Origin")) # If you export many tables, it can be more convenient to use setFixest_dict: setFixest_dict(c("log(Euros)"="Euros (ln)", Origin="Country of Origin")) etable(est) # variables are properly relabeled # The dictionary only 'grows' # Here you get the previous two variables + the new one that are relabeled # Btw you set the dictionary directly using the argument names: setFixest_dict(Destination = "Country of Destination") etable(est) # Another way to set a dictionary: with a character string: # See the help page of as.dict dict = "log(dist_km): Distance (ln); Product: Type of Good" setFixest_dict(dict) etable(est) # And now we reset: setFixest_dict(reset = TRUE) etable(est)
This function sets globally the default arguments of fixest estimations.
setFixest_estimation( data = NULL, panel.id = NULL, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, collin.tol = 1e-10, lean = FALSE, verbose = 0, warn = TRUE, combine.quick = NULL, demeaned = FALSE, mem.clean = FALSE, glm.iter = 25, glm.tol = 1e-08, data.save = FALSE, reset = FALSE ) getFixest_estimation()setFixest_estimation( data = NULL, panel.id = NULL, fixef.rm = "perfect", fixef.tol = 1e-06, fixef.iter = 10000, collin.tol = 1e-10, lean = FALSE, verbose = 0, warn = TRUE, combine.quick = NULL, demeaned = FALSE, mem.clean = FALSE, glm.iter = 25, glm.tol = 1e-08, data.save = FALSE, reset = FALSE ) getFixest_estimation()
data |
A data.frame containing the necessary variables to run the model.
The variables of the non-linear right hand side of the formula are identified
with this |
panel.id |
The panel identifiers. Can either be: i) a one sided formula
(e.g. |
fixef.rm |
Can be equal to "perfect" (default), "singleton", "both" or "none". Controls which observations are to be removed. If "perfect", then observations having a fixed-effect with perfect fit (e.g. only 0 outcomes in Poisson estimations) will be removed. If "singleton", all observations for which a fixed-effect appears only once will be removed. Note, importantly, that singletons are removed in just one pass, there is no recursivity implemented. The meaning of "both" and "none" is direct. |
fixef.tol |
Precision used to obtain the fixed-effects. Defaults to |
fixef.iter |
Maximum number of iterations in fixed-effects algorithm (only in use for 2+ fixed-effects). Default is 10000. |
collin.tol |
Numeric scalar, default is |
lean |
Logical, default is |
verbose |
Integer. Higher values give more information. In particular, it can detail the number of iterations in the demeaning algorithm (the first number is the left-hand-side, the other numbers are the right-hand-side variables). |
warn |
Logical, default is |
combine.quick |
Logical. When you combine different variables to transform them
into a single fixed-effects you can do e.g. |
demeaned |
Logical, default is |
mem.clean |
Logical, default is |
glm.iter |
Number of iterations of the glm algorithm. Default is 25. |
glm.tol |
Tolerance level for the glm algorithm. Default is |
data.save |
Logical scalar, default is |
reset |
Logical scalar, default is |
The function getFixest_estimation returns the currently set global defaults.
# # Example: removing singletons is FALSE by default # # => changing this default # Let's create data with singletons base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$fe_singletons = as.character(base$species) base$fe_singletons[1:5] = letters[1:5] res = feols(y ~ x1 + x2 | fe_singletons, base) res_noSingle = feols(y ~ x1 + x2 | fe_singletons, base, fixef.rm = "single") # New defaults setFixest_estimation(fixef.rm = "single") res_newDefault = feols(y ~ x1 + x2 | fe_singletons, base) etable(res, res_noSingle, res_newDefault) # Resetting the defaults setFixest_estimation(reset = TRUE)# # Example: removing singletons is FALSE by default # # => changing this default # Let's create data with singletons base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$fe_singletons = as.character(base$species) base$fe_singletons[1:5] = letters[1:5] res = feols(y ~ x1 + x2 | fe_singletons, base) res_noSingle = feols(y ~ x1 + x2 | fe_singletons, base, fixef.rm = "single") # New defaults setFixest_estimation(fixef.rm = "single") res_newDefault = feols(y ~ x1 + x2 | fe_singletons, base) etable(res, res_noSingle, res_newDefault) # Resetting the defaults setFixest_estimation(reset = TRUE)
You can set formula macros globally with setFixest_fml. These macros can then be used in fixest estimations or when using the function xpd.
setFixest_fml(..., reset = FALSE) getFixest_fml()setFixest_fml(..., reset = FALSE) getFixest_fml()
... |
Definition of the macro variables. Each argument name corresponds to the name of the
macro variable. It is required that each macro variable name starts with two dots
(e.g. |
reset |
A logical scalar, defaults to |
In xpd, the default macro variables are taken from getFixest_fml.
Any value in the ... argument of xpd will replace these default values.
The definitions of the macro variables will replace in verbatim the macro variables.
Therefore, you can include multipart formulas if you wish but then beware of the order the
macros variable in the formula. For example, using the airquality data, say you want to set as
controls the variable Temp and Day fixed-effects, you can do
setFixest_fml(..ctrl = ~Temp | Day), but then
feols(Ozone ~ Wind + ..ctrl, airquality) will be quite different from
feols(Ozone ~ ..ctrl + Wind, airquality), so beware!
The function getFixest_fml() returns a list of character strings, the names
corresponding to the macro variable names, the character strings corresponding
to their definition.
xpd to make use of formula macros.
# Small examples with airquality data data(airquality) # we set two macro variables setFixest_fml(..ctrl = ~ Temp + Day, ..ctrl_long = ~ poly(Temp, 2) + poly(Day, 2)) # Using the macro in lm with xpd: lm(xpd(Ozone ~ Wind + ..ctrl), airquality) lm(xpd(Ozone ~ Wind + ..ctrl_long), airquality) # You can use the macros without xpd() in fixest estimations a = feols(Ozone ~ Wind + ..ctrl, airquality) b = feols(Ozone ~ Wind + ..ctrl_long, airquality) etable(a, b, keep = "Int|Win") # Using .[] base = setNames(iris, c("y", "x1", "x2", "x3", "species")) i = 2:3 z = "species" lm(xpd(y ~ x.[2:3] + .[z]), base) # No xpd() needed in feols feols(y ~ x.[2:3] + .[z], base) # # Auto completion with '..' suffix # # You can trigger variables autocompletion with the '..' suffix # You need to provide the argument data base = setNames(iris, c("y", "x1", "x2", "x3", "species")) xpd(y ~ x.., data = base) # In fixest estimations, this is automatically taken care of feols(y ~ x.., data = base) # # You can use xpd for stepwise estimations # # Note that for stepwise estimations in fixest, you can use # the stepwise functions: sw, sw0, csw, csw0 # -> see help in feols or in the dedicated vignette # we want to look at the effect of x1 on y # controlling for different variables base = iris names(base) = c("y", "x1", "x2", "x3", "species") # We first create a matrix with all possible combinations of variables my_args = lapply(names(base)[-(1:2)], function(x) c("", x)) (all_combs = as.matrix(do.call("expand.grid", my_args))) res_all = list() for(i in 1:nrow(all_combs)){ res_all[[i]] = feols(xpd(y ~ x1 + ..v, ..v = all_combs[i, ]), base) } etable(res_all) coefplot(res_all, group = list(Species = "^^species")) # # You can use macros to grep variables in your data set # # Example 1: setting a macro variable globally data(longley) setFixest_fml(..many_vars = grep("GNP|ployed", names(longley), value = TRUE)) feols(Armed.Forces ~ Population + ..many_vars, longley) # Example 2: using ..("regex") or regex("regex") to grep the variables "live" feols(Armed.Forces ~ Population + ..("GNP|ployed"), longley) # Example 3: same as Ex.2 but without using a fixest estimation # Here we need to use xpd(): lm(xpd(Armed.Forces ~ Population + regex("GNP|ployed"), data = longley), longley) # Stepwise estimation with regex: use a comma after the parenthesis feols(Armed.Forces ~ Population + sw(regex(,"GNP|ployed")), longley) # Multiple LHS etable(feols(..("GNP|ployed") ~ Population, longley)) # # lhs and rhs arguments # # to create a one sided formula from a character vector vars = letters[1:5] xpd(rhs = vars) # Alternatively, to replace the RHS xpd(y ~ 1, rhs = vars) # To create a two sided formula xpd(lhs = "y", rhs = vars) # # argument 'add' # xpd(~x1, add = ~ x2 + x3) # also works with character vectors xpd(~x1, add = c("x2", "x3")) # only adds to the RHS xpd(y ~ x, add = ~bon + jour) # # Dot square bracket operator # # The basic use is to add variables in the formula x = c("x1", "x2") xpd(y ~ .[x]) # Alternatively, one-sided formulas can be used and their content will be inserted verbatim x = ~x1 + x2 xpd(y ~ .[x]) # You can create multiple variables at once xpd(y ~ x.[1:5] + z.[2:3]) # You can summon variables from the environment to complete variables names var = "a" xpd(y ~ x.[var]) # ... the variables can be multiple vars = LETTERS[1:3] xpd(y ~ x.[vars]) # You can have "complex" variable names but they must be nested in character form xpd(y ~ .["x.[vars]_sq"]) # DSB can be used within regular expressions re = c("GNP", "Pop") xpd(Unemployed ~ regex(".[re]"), data = longley) # => equivalent to regex("GNP|Pop") # Use .[,var] (NOTE THE COMMA!) to expand with commas # !! can break the formula if missused vars = c("wage", "unemp") xpd(c(y.[,1:3]) ~ csw(.[,vars])) # Example of use of .[] within a loop res_all = list() for(p in 1:3){ res_all[[p]] = feols(Ozone ~ Wind + poly(Temp, .[p]), airquality) } etable(res_all) # The former can be compactly estimated with: res_compact = feols(Ozone ~ Wind + sw(.[, "poly(Temp, .[1:3])"]), airquality) etable(res_compact) # How does it work? # 1) .[, stuff] evaluates stuff and, if a vector, aggregates it with commas # Comma aggregation is done thanks to the comma placed after the square bracket # If .[stuff], then aggregation is with sums. # 2) stuff is evaluated, and if it is a character string, it is evaluated with # the function dsb which expands values in .[] # # Wrapping up: # 2) evaluation of dsb("poly(Temp, .[1:3])") leads to the vector: # c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)") # 1) .[, c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)")] leads to # poly(Temp, 1), poly(Temp, 2), poly(Temp, 3) # # Hence sw(.[, "poly(Temp, .[1:3])"]) becomes: # sw(poly(Temp, 1), poly(Temp, 2), poly(Temp, 3)) # # In non-fixest functions: guessing the data allows to use regex # # When used in non-fixest functions, the algorithm tries to "guess" the data # so that ..("regex") can be directly evaluated without passing the argument 'data' data(longley) lm(xpd(Armed.Forces ~ Population + ..("GNP|ployed")), longley) # same for the auto completion with '..' lm(xpd(Armed.Forces ~ Population + GN..), longley)# Small examples with airquality data data(airquality) # we set two macro variables setFixest_fml(..ctrl = ~ Temp + Day, ..ctrl_long = ~ poly(Temp, 2) + poly(Day, 2)) # Using the macro in lm with xpd: lm(xpd(Ozone ~ Wind + ..ctrl), airquality) lm(xpd(Ozone ~ Wind + ..ctrl_long), airquality) # You can use the macros without xpd() in fixest estimations a = feols(Ozone ~ Wind + ..ctrl, airquality) b = feols(Ozone ~ Wind + ..ctrl_long, airquality) etable(a, b, keep = "Int|Win") # Using .[] base = setNames(iris, c("y", "x1", "x2", "x3", "species")) i = 2:3 z = "species" lm(xpd(y ~ x.[2:3] + .[z]), base) # No xpd() needed in feols feols(y ~ x.[2:3] + .[z], base) # # Auto completion with '..' suffix # # You can trigger variables autocompletion with the '..' suffix # You need to provide the argument data base = setNames(iris, c("y", "x1", "x2", "x3", "species")) xpd(y ~ x.., data = base) # In fixest estimations, this is automatically taken care of feols(y ~ x.., data = base) # # You can use xpd for stepwise estimations # # Note that for stepwise estimations in fixest, you can use # the stepwise functions: sw, sw0, csw, csw0 # -> see help in feols or in the dedicated vignette # we want to look at the effect of x1 on y # controlling for different variables base = iris names(base) = c("y", "x1", "x2", "x3", "species") # We first create a matrix with all possible combinations of variables my_args = lapply(names(base)[-(1:2)], function(x) c("", x)) (all_combs = as.matrix(do.call("expand.grid", my_args))) res_all = list() for(i in 1:nrow(all_combs)){ res_all[[i]] = feols(xpd(y ~ x1 + ..v, ..v = all_combs[i, ]), base) } etable(res_all) coefplot(res_all, group = list(Species = "^^species")) # # You can use macros to grep variables in your data set # # Example 1: setting a macro variable globally data(longley) setFixest_fml(..many_vars = grep("GNP|ployed", names(longley), value = TRUE)) feols(Armed.Forces ~ Population + ..many_vars, longley) # Example 2: using ..("regex") or regex("regex") to grep the variables "live" feols(Armed.Forces ~ Population + ..("GNP|ployed"), longley) # Example 3: same as Ex.2 but without using a fixest estimation # Here we need to use xpd(): lm(xpd(Armed.Forces ~ Population + regex("GNP|ployed"), data = longley), longley) # Stepwise estimation with regex: use a comma after the parenthesis feols(Armed.Forces ~ Population + sw(regex(,"GNP|ployed")), longley) # Multiple LHS etable(feols(..("GNP|ployed") ~ Population, longley)) # # lhs and rhs arguments # # to create a one sided formula from a character vector vars = letters[1:5] xpd(rhs = vars) # Alternatively, to replace the RHS xpd(y ~ 1, rhs = vars) # To create a two sided formula xpd(lhs = "y", rhs = vars) # # argument 'add' # xpd(~x1, add = ~ x2 + x3) # also works with character vectors xpd(~x1, add = c("x2", "x3")) # only adds to the RHS xpd(y ~ x, add = ~bon + jour) # # Dot square bracket operator # # The basic use is to add variables in the formula x = c("x1", "x2") xpd(y ~ .[x]) # Alternatively, one-sided formulas can be used and their content will be inserted verbatim x = ~x1 + x2 xpd(y ~ .[x]) # You can create multiple variables at once xpd(y ~ x.[1:5] + z.[2:3]) # You can summon variables from the environment to complete variables names var = "a" xpd(y ~ x.[var]) # ... the variables can be multiple vars = LETTERS[1:3] xpd(y ~ x.[vars]) # You can have "complex" variable names but they must be nested in character form xpd(y ~ .["x.[vars]_sq"]) # DSB can be used within regular expressions re = c("GNP", "Pop") xpd(Unemployed ~ regex(".[re]"), data = longley) # => equivalent to regex("GNP|Pop") # Use .[,var] (NOTE THE COMMA!) to expand with commas # !! can break the formula if missused vars = c("wage", "unemp") xpd(c(y.[,1:3]) ~ csw(.[,vars])) # Example of use of .[] within a loop res_all = list() for(p in 1:3){ res_all[[p]] = feols(Ozone ~ Wind + poly(Temp, .[p]), airquality) } etable(res_all) # The former can be compactly estimated with: res_compact = feols(Ozone ~ Wind + sw(.[, "poly(Temp, .[1:3])"]), airquality) etable(res_compact) # How does it work? # 1) .[, stuff] evaluates stuff and, if a vector, aggregates it with commas # Comma aggregation is done thanks to the comma placed after the square bracket # If .[stuff], then aggregation is with sums. # 2) stuff is evaluated, and if it is a character string, it is evaluated with # the function dsb which expands values in .[] # # Wrapping up: # 2) evaluation of dsb("poly(Temp, .[1:3])") leads to the vector: # c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)") # 1) .[, c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)")] leads to # poly(Temp, 1), poly(Temp, 2), poly(Temp, 3) # # Hence sw(.[, "poly(Temp, .[1:3])"]) becomes: # sw(poly(Temp, 1), poly(Temp, 2), poly(Temp, 3)) # # In non-fixest functions: guessing the data allows to use regex # # When used in non-fixest functions, the algorithm tries to "guess" the data # so that ..("regex") can be directly evaluated without passing the argument 'data' data(longley) lm(xpd(Armed.Forces ~ Population + ..("GNP|ployed")), longley) # same for the auto completion with '..' lm(xpd(Armed.Forces ~ Population + GN..), longley)
fixest_multi objectsUse this function to change the default behavior of fixest_multi objects.
setFixest_multi(drop = FALSE) getFixest_multi()setFixest_multi(drop = FALSE) getFixest_multi()
drop |
Logical scalar, default is |
The function getFixest_multi() returns the list of settings.
# 1) let's run a multiple estimation base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x1, x2, x3), base) # 2) let's pick a single estimation => by default we have a `fixest_multi` object class(est[rhs = 2]) # `drop = TRUE` would have led to a `fixest` object class(est[rhs = 2, drop = TRUE]) # 3) change the default behavior setFixest_multi(drop = TRUE) class(est[rhs = 2])# 1) let's run a multiple estimation base = setNames(iris, c("y", "x1", "x2", "x3", "species")) est = feols(y ~ csw(x1, x2, x3), base) # 2) let's pick a single estimation => by default we have a `fixest_multi` object class(est[rhs = 2]) # `drop = TRUE` would have led to a `fixest` object class(est[rhs = 2, drop = TRUE]) # 3) change the default behavior setFixest_multi(drop = TRUE) class(est[rhs = 2])
fixest estimation functionsSets/gets the default values of whether notes (informing for NA and observations removed) should be displayed in fixest estimation functions.
setFixest_notes(x) getFixest_notes()setFixest_notes(x) getFixest_notes()
x |
A logical. If |
Laurent Berge
# Change default with setFixest_notes(FALSE) feols(Ozone ~ Solar.R, airquality) # Back to default which is TRUE setFixest_notes(TRUE) feols(Ozone ~ Solar.R, airquality)# Change default with setFixest_notes(FALSE) feols(Ozone ~ Solar.R, airquality) # Back to default which is TRUE setFixest_notes(TRUE) feols(Ozone ~ Solar.R, airquality)
fixest functionsSets/gets the default number of threads to used in fixest estimation functions. The default is the maximum number of threads minus two.
setFixest_nthreads(nthreads, save = FALSE) getFixest_nthreads()setFixest_nthreads(nthreads, save = FALSE) getFixest_nthreads()
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. If missing, the default is to use 50% of all threads. |
save |
Either a logical or equal to |
Laurent Berge
# Gets the current number of threads (nthreads_origin = getFixest_nthreads()) # To set multi-threading off: setFixest_nthreads(1) # To set it back to default at startup: setFixest_nthreads() # And back to the original value setFixest_nthreads(nthreads_origin)# Gets the current number of threads (nthreads_origin = getFixest_nthreads()) # To set multi-threading off: setFixest_nthreads(1) # To set it back to default at startup: setFixest_nthreads() # And back to the original value setFixest_nthreads(nthreads_origin)
This functions defines or extracts the default type of standard-errors to computed in
fixest summary, and vcov.
setFixest_vcov( no_FE = "iid", one_FE = "cluster", two_FE = "cluster", panel = "cluster", all = NULL, reset = FALSE ) getFixest_vcov()setFixest_vcov( no_FE = "iid", one_FE = "cluster", two_FE = "cluster", panel = "cluster", all = NULL, reset = FALSE ) getFixest_vcov()
no_FE |
Character scalar equal to either: |
one_FE |
Character scalar equal to either: |
two_FE |
Character scalar equal to either: |
panel |
Character scalar equal to either: |
all |
Character scalar equal to either: |
reset |
Logical, default is |
The function getFixest_vcov() returns a list with three elements containing the default for
estimations i) without, ii) with one, or iii) with two or more fixed-effects.
# By default: # - no fixed-effect (FE): standard # - one or more FEs: cluster # - panel: cluster on panel id data(base_did) est_no_FE = feols(y ~ x1, base_did) est_one_FE = feols(y ~ x1 | id, base_did) est_two_FE = feols(y ~ x1 | id + period, base_did) est_panel = feols(y ~ x1 | id + period, base_did, panel.id = ~id + period) etable(est_no_FE, est_one_FE, est_two_FE) # Changing the default standard-errors setFixest_vcov(no_FE = "hetero", one_FE = "iid", two_FE = "twoway", panel = "drisc") etable(est_no_FE, est_one_FE, est_two_FE, est_panel) # Resetting the defaults setFixest_vcov(reset = TRUE)# By default: # - no fixed-effect (FE): standard # - one or more FEs: cluster # - panel: cluster on panel id data(base_did) est_no_FE = feols(y ~ x1, base_did) est_one_FE = feols(y ~ x1 | id, base_did) est_two_FE = feols(y ~ x1 | id + period, base_did) est_panel = feols(y ~ x1 | id + period, base_did, panel.id = ~id + period) etable(est_no_FE, est_one_FE, est_two_FE) # Changing the default standard-errors setFixest_vcov(no_FE = "hetero", one_FE = "iid", two_FE = "twoway", panel = "drisc") etable(est_no_FE, est_one_FE, est_two_FE, est_panel) # Resetting the defaults setFixest_vcov(reset = TRUE)
fixest estimationsExtract the estimated standard deviation of the errors from fixest estimations.
## S3 method for class 'fixest' sigma(object, ...)## S3 method for class 'fixest' sigma(object, ...)
object |
A |
... |
Not currently used. |
Returns a numeric scalar.
feols, fepois, feglm, fenegbin, feNmlm.
est = feols(Petal.Length ~ Petal.Width, iris) sigma(est)est = feols(Petal.Length ~ Petal.Width, iris) sigma(est)
fixest VCOVsProvides how the small sample correction should be calculated in vcov.fixest/summary.fixest.
ssc( adj = TRUE, fixef.K = "nested", cluster.adj = TRUE, cluster.df = "min", t.df = "min", fixef.force_exact = FALSE ) dof( adj = TRUE, fixef.K = "nested", cluster.adj = TRUE, cluster.df = "min", t.df = "min", fixef.force_exact = FALSE ) setFixest_ssc(ssc.type = ssc()) getFixest_ssc()ssc( adj = TRUE, fixef.K = "nested", cluster.adj = TRUE, cluster.df = "min", t.df = "min", fixef.force_exact = FALSE ) dof( adj = TRUE, fixef.K = "nested", cluster.adj = TRUE, cluster.df = "min", t.df = "min", fixef.force_exact = FALSE ) setFixest_ssc(ssc.type = ssc()) getFixest_ssc()
adj |
Logical scalar, defaults to |
fixef.K |
Character scalar equal to |
cluster.adj |
Logical scalar, default is |
cluster.df |
Either "conventional" or "min" (default). Only relevant when the
variance-covariance matrix is two-way clustered (or higher). It governs how the small
sample adjustment for the clusters is to be performed. [Sorry for the jargon that follows.]
By default a unique adjustment is made, of the form G_min/(G_min-1) with G_min the
smallest G_i. If |
t.df |
Either "conventional", "min" (default) or an integer scalar. Only relevant when
the variance-covariance matrix is clustered. It governs how the p-values should be computed.
By default, the degrees of freedom of the Student t distribution is equal to the minimum size
of the clusters with which the VCOV has been clustered minus one. If |
fixef.force_exact |
Logical, default is |
ssc.type |
An object of class |
The following vignette: On standard-errors,
describes in details how the standard-errors are computed in fixest and how you can
replicate standard-errors from other software.
It returns a ssc.type object.
dof(): This function is deprecated and will be removed at some point (in 6 months from August 2021). Exactly the same as ssc.
Laurent Berge
# # Equivalence with lm/glm standard-errors # # LM # In the absence of fixed-effects, # by default, the standard-errors are computed in the same way res = feols(Petal.Length ~ Petal.Width + Species, iris) res_lm = lm(Petal.Length ~ Petal.Width + Species, iris) vcov(res) / vcov(res_lm) # GLM # By default, there is no small sample adjustment in glm, as opposed to feglm. # To get the same SEs, we need to use ssc(adj = FALSE) res_pois = fepois(round(Petal.Length) ~ Petal.Width + Species, iris) res_glm = glm(round(Petal.Length) ~ Petal.Width + Species, iris, family = poisson()) vcov(res_pois, ssc = ssc(adj = FALSE)) / vcov(res_glm) # Same example with the Gamma res_gamma = feglm(round(Petal.Length) ~ Petal.Width + Species, iris, family = Gamma()) res_glm_gamma = glm(round(Petal.Length) ~ Petal.Width + Species, iris, family = Gamma()) vcov(res_gamma, ssc = ssc(adj = FALSE)) / vcov(res_glm_gamma) # # Fixed-effects corrections # # We create "irregular" FEs base = data.frame(x = rnorm(10)) base$y = base$x + rnorm(10) base$fe1 = rep(1:3, c(4, 3, 3)) base$fe2 = rep(1:5, each = 2) est = feols(y ~ x | fe1 + fe2, base) # fe1: 3 FEs # fe2: 5 FEs # # Clustered standard-errors: by fe1 # # Default: fixef.K = "nested" # => adjustment K = 1 + 5 (i.e. x + fe2) summary(est) attributes(vcov(est, attr = TRUE))[c("ssc", "dof.K")] # fixef.K = FALSE # => adjustment K = 1 (i.e. only x) summary(est, ssc = ssc(fixef.K = "none")) attr(vcov(est, ssc = ssc(fixef.K = "none"), attr = TRUE), "dof.K") # fixef.K = TRUE # => adjustment K = 1 + 3 + 5 - 1 (i.e. x + fe1 + fe2 - 1 restriction) summary(est, ssc = ssc(fixef.K = "full")) attr(vcov(est, ssc = ssc(fixef.K = "full"), attr = TRUE), "dof.K") # fixef.K = TRUE & fixef.force_exact = TRUE # => adjustment K = 1 + 3 + 5 - 2 (i.e. x + fe1 + fe2 - 2 restrictions) summary(est, ssc = ssc(fixef.K = "full", fixef.force_exact = TRUE)) attr(vcov(est, ssc = ssc(fixef.K = "full", fixef.force_exact = TRUE), attr = TRUE), "dof.K") # There are two restrictions: attr(fixef(est), "references") # # To permanently set the default ssc: # # eg no small sample adjustment: setFixest_ssc(ssc(adj = FALSE)) # Factory default setFixest_ssc()# # Equivalence with lm/glm standard-errors # # LM # In the absence of fixed-effects, # by default, the standard-errors are computed in the same way res = feols(Petal.Length ~ Petal.Width + Species, iris) res_lm = lm(Petal.Length ~ Petal.Width + Species, iris) vcov(res) / vcov(res_lm) # GLM # By default, there is no small sample adjustment in glm, as opposed to feglm. # To get the same SEs, we need to use ssc(adj = FALSE) res_pois = fepois(round(Petal.Length) ~ Petal.Width + Species, iris) res_glm = glm(round(Petal.Length) ~ Petal.Width + Species, iris, family = poisson()) vcov(res_pois, ssc = ssc(adj = FALSE)) / vcov(res_glm) # Same example with the Gamma res_gamma = feglm(round(Petal.Length) ~ Petal.Width + Species, iris, family = Gamma()) res_glm_gamma = glm(round(Petal.Length) ~ Petal.Width + Species, iris, family = Gamma()) vcov(res_gamma, ssc = ssc(adj = FALSE)) / vcov(res_glm_gamma) # # Fixed-effects corrections # # We create "irregular" FEs base = data.frame(x = rnorm(10)) base$y = base$x + rnorm(10) base$fe1 = rep(1:3, c(4, 3, 3)) base$fe2 = rep(1:5, each = 2) est = feols(y ~ x | fe1 + fe2, base) # fe1: 3 FEs # fe2: 5 FEs # # Clustered standard-errors: by fe1 # # Default: fixef.K = "nested" # => adjustment K = 1 + 5 (i.e. x + fe2) summary(est) attributes(vcov(est, attr = TRUE))[c("ssc", "dof.K")] # fixef.K = FALSE # => adjustment K = 1 (i.e. only x) summary(est, ssc = ssc(fixef.K = "none")) attr(vcov(est, ssc = ssc(fixef.K = "none"), attr = TRUE), "dof.K") # fixef.K = TRUE # => adjustment K = 1 + 3 + 5 - 1 (i.e. x + fe1 + fe2 - 1 restriction) summary(est, ssc = ssc(fixef.K = "full")) attr(vcov(est, ssc = ssc(fixef.K = "full"), attr = TRUE), "dof.K") # fixef.K = TRUE & fixef.force_exact = TRUE # => adjustment K = 1 + 3 + 5 - 2 (i.e. x + fe1 + fe2 - 2 restrictions) summary(est, ssc = ssc(fixef.K = "full", fixef.force_exact = TRUE)) attr(vcov(est, ssc = ssc(fixef.K = "full", fixef.force_exact = TRUE), attr = TRUE), "dof.K") # There are two restrictions: attr(fixef(est), "references") # # To permanently set the default ssc: # # eg no small sample adjustment: setFixest_ssc(ssc(adj = FALSE)) # Factory default setFixest_ssc()
Functions to perform stepwise estimations in fixest models.
sw(...) csw(...) sw0(...) csw0(...) mvsw(...)sw(...) csw(...) sw0(...) csw0(...) mvsw(...)
... |
Represents formula variables to be added in a stepwise fashion to an estimation. |
To include multiple independent variables, you need to use the stepwise functions.
There are 5 stepwise functions: sw, sw0, csw, csw0 and mvsw. Let's explain that.
Assume you have the following formula: fml = y ~ x1 + sw(x2, x3). The stepwise
function sw will estimate the following two models: y ~ x1 + x2 and y ~ x1 + x3.
That is, each element in sw() is sequentially, and separately, added to the formula.
Would have you used sw0 in lieu of sw, then the model y ~ x1 would also have
been estimated. The 0 in the name implies that the model without any stepwise
element will also be estimated.
Finally, the prefix c means cumulative: each stepwise element is added to the next.
That is, fml = y ~ x1 + csw(x2, x3) would lead to the following models y ~ x1 + x2
and y ~ x1 + x2 + x3. The 0 has the same meaning and would also lead to the model
without the stepwise elements to be estimated: in other words,
fml = y ~ x1 + csw0(x2, x3) leads to the following three models: y ~ x1,
y ~ x1 + x2 and y ~ x1 + x2 + x3.
The last stepwise function, mvsw, refers to 'multiverse' stepwise. It will estimate
as many models as there are unique combinations of stepwise variables. For example
fml = y ~ x1 + mvsw(x2, x3) will estimate y ~ x1, y ~ x1 + x2, y ~ x1 + x3,
y ~ x1 + x2 + x3. Beware that the number of estimations grows pretty fast (2^n,
with n the number of stewise variables)!
base = setNames(iris, c("y", "x1", "x2", "x3", "species")) # Regular stepwise feols(y ~ sw(x1, x2, x3), base) # Cumulative stepwise feols(y ~ csw(x1, x2, x3), base) # Using the 0 feols(y ~ x1 + x2 + sw0(x3), base) # Multiverse stepwise feols(y ~ x1 + mvsw(x2, x3), base)base = setNames(iris, c("y", "x1", "x2", "x3", "species")) # Regular stepwise feols(y ~ sw(x1, x2, x3), base) # Cumulative stepwise feols(y ~ csw(x1, x2, x3), base) # Using the 0 feols(y ~ x1 + x2 + sw0(x3), base) # Multiverse stepwise feols(y ~ x1 + mvsw(x2, x3), base)
This function describes the style of data.frames created with the function etable.
style.df( depvar.title = "Dependent Var.:", fixef.title = "Fixed-Effects:", fixef.line = "-", fixef.prefix = "", fixef.suffix = "", slopes.title = "Varying Slopes:", slopes.line = "-", slopes.format = "__var__ (__slope__)", stats.title = "_", stats.line = "_", yesNo = c("Yes", "No"), headers.sep = TRUE, signif.code = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1), interaction.combine = " x ", i.equal = " = ", default = FALSE )style.df( depvar.title = "Dependent Var.:", fixef.title = "Fixed-Effects:", fixef.line = "-", fixef.prefix = "", fixef.suffix = "", slopes.title = "Varying Slopes:", slopes.line = "-", slopes.format = "__var__ (__slope__)", stats.title = "_", stats.line = "_", yesNo = c("Yes", "No"), headers.sep = TRUE, signif.code = c(`***` = 0.001, `**` = 0.01, `*` = 0.05, . = 0.1), interaction.combine = " x ", i.equal = " = ", default = FALSE )
depvar.title |
Character scalar. Default is |
fixef.title |
Character scalar. Default is |
fixef.line |
A single character. Default is |
fixef.prefix |
Character scalar. Default is |
fixef.suffix |
Character scalar. Default is |
slopes.title |
Character scalar. Default is |
slopes.line |
Character scalar. Default is |
slopes.format |
Character scalar. Default is |
stats.title |
Character scalar. Default is |
stats.line |
Character scalar. Default is |
yesNo |
Character vector of length 1 or 2. Default is |
headers.sep |
Logical, default is |
signif.code |
Named numeric vector, used to provide the significance codes with respect to
the p-value of the coefficients. Default is |
interaction.combine |
Character scalar, defaults to |
i.equal |
Character scalar, defaults to |
default |
Logical, default is |
@inheritParams etable
The title elements (depvar.title, fixef.title, slopes.title and stats.title) will be the
row names of the returned data.frame. Therefore keep in mind that any two of them should not be
identical (since identical row names are forbidden in data.frames).
It returns an object of class fixest_style_df.
# Multiple estimations => see details in feols aq = airquality est = feols(c(Ozone, Solar.R) ~ Wind + csw(Temp, Temp^2, Temp^3) | Month + Day, data = aq) # Default result etable(est) # Playing a bit with the styles etable(est, style.df = style.df(fixef.title = "", fixef.suffix = " FE", stats.line = " ", yesNo = "yes"))# Multiple estimations => see details in feols aq = airquality est = feols(c(Ozone, Solar.R) ~ Wind + csw(Temp, Temp^2, Temp^3) | Month + Day, data = aq) # Default result etable(est) # Playing a bit with the styles etable(est, style.df = style.df(fixef.title = "", fixef.suffix = " FE", stats.line = " ", yesNo = "yes"))
This function describes the style of Latex tables to be exported with the function etable.
style.tex( main = "base", depvar.title, model.title, model.format, line.top, line.bottom, var.title, fixef.title, fixef.prefix, fixef.suffix, fixef.where, slopes.title, slopes.format, fixef_sizes.prefix, fixef_sizes.suffix, stats.title, notes.intro, notes.tpt.intro, tablefoot, tablefoot.value, yesNo, tabular = "normal", depvar.style, no_border, caption.after, rules_width, signif.code, tpt, arraystretch, adjustbox = NULL, fontsize, interaction.combine = " $\\times$ ", i.equal = " $=$ " )style.tex( main = "base", depvar.title, model.title, model.format, line.top, line.bottom, var.title, fixef.title, fixef.prefix, fixef.suffix, fixef.where, slopes.title, slopes.format, fixef_sizes.prefix, fixef_sizes.suffix, stats.title, notes.intro, notes.tpt.intro, tablefoot, tablefoot.value, yesNo, tabular = "normal", depvar.style, no_border, caption.after, rules_width, signif.code, tpt, arraystretch, adjustbox = NULL, fontsize, interaction.combine = " $\\times$ ", i.equal = " $=$ " )
main |
Either "base", "aer" or "qje". Defines the basic style to start from. The styles "aer" and "qje" are almost identical and only differ on the top/bottom lines. |
depvar.title |
A character scalar. The title of the line of the dependent variables
(defaults to |
model.title |
A character scalar. The title of the line of the models (defaults to
|
model.format |
A character scalar. The value to appear on top of each column. It defaults
to |
line.top |
A character scalar equal to |
line.bottom |
A character scalar equal to |
var.title |
A character scalar. The title line appearing before the variables (defaults to
|
fixef.title |
A character scalar. The title line appearing before the fixed-effects
(defaults to |
fixef.prefix |
A prefix to add to the fixed-effects names. Defaults to |
fixef.suffix |
A suffix to add to the fixed-effects names. Defaults to |
fixef.where |
Either "var" or "stats". Where to place the fixed-effects lines?
Defaults to |
slopes.title |
A character scalar. The title line appearing before the variables with
varying slopes (defaults to |
slopes.format |
Character scalar representing the format of the slope variable name.
There are two special characters: "var" and "slope", placeholers for the variable
and slope names. Defaults to |
fixef_sizes.prefix |
A prefix to add to the fixed-effects names. Defaults to |
fixef_sizes.suffix |
A suffix to add to the fixed-effects names. Defaults
to |
stats.title |
A character scalar. The title line appearing before the statistics
(defaults to |
notes.intro |
A character scalar. Some tex code appearing just before the notes,
defaults to |
notes.tpt.intro |
Character scalar. Only used if |
tablefoot |
A logical scalar. Whether or not to display a footer within the table.
Defaults to |
tablefoot.value |
A character scalar. The notes to be displayed in the footer.
Defaults to |
yesNo |
A character vector of length 1 or 2. Defaults to |
tabular |
(Tex only.) Character scalar equal to "normal" (default), |
depvar.style |
Character scalar equal to either |
no_border |
Logical, default is |
caption.after |
Character scalar. Tex code that will be placed right after the caption.
Defaults to |
rules_width |
Character vector of length 1 or 2. This vector gives the width of the
|
signif.code |
Named numeric vector, used to provide the significance codes with respect to
the p-value of the coefficients. Default is |
tpt |
(Tex only.) Logical scalar, default is FALSE. Whether to use the |
arraystretch |
(Tex only.) A numeric scalar, default is |
adjustbox |
(Tex only.) A logical, numeric or character scalar, default is |
fontsize |
(Tex only.) A character scalar, default is |
interaction.combine |
Character scalar, defaults to |
i.equal |
Character scalar, defaults to |
The \\checkmark command, used in the "aer" style (in argument yesNo), is in the
amssymb package.
The commands \\toprule, \\midrule and \\bottomrule are in the booktabs package.
You can set the width of the top/bottom rules with \\setlength\\heavyrulewidth\{wd\},
and of the midrule with \\setlength\\lightrulewidth\{wd\}.
Note that all titles (depvar.title, depvar.title, etc) are not escaped, so they
must be valid Latex expressions.
Returns a list containing the style parameters.
# Multiple estimations => see details in feols aq = airquality est = feols(c(Ozone, Solar.R) ~ Wind + csw(Temp, Temp^2, Temp^3) | Month + Day, data = aq) # Playing a bit with the styles etable(est, tex = TRUE) etable(est, tex = TRUE, style.tex = style.tex("aer")) etable(est, tex = TRUE, style.tex = style.tex("aer", var.title = "\\emph{Expl. Vars.}", model.format = "[i]", yesNo = "x", tabular = "*"))# Multiple estimations => see details in feols aq = airquality est = feols(c(Ozone, Solar.R) ~ Wind + csw(Temp, Temp^2, Temp^3) | Month + Day, data = aq) # Playing a bit with the styles etable(est, tex = TRUE) etable(est, tex = TRUE, style.tex = style.tex("aer")) etable(est, tex = TRUE, style.tex = style.tex("aer", var.title = "\\emph{Expl. Vars.}", model.format = "[i]", yesNo = "x", tabular = "*"))
fixest object. Computes different types of standard errors.This function is similar to print.fixest. It provides the table of coefficients along with
other information on the fit of the estimation. It can compute different types of standard
errors. The new variance covariance matrix is an object returned.
## S3 method for class 'fixest' summary( object, vcov = NULL, cluster = NULL, ssc = NULL, .vcov = NULL, stage = NULL, lean = FALSE, agg = NULL, forceCovariance = FALSE, se = NULL, keepBounded = FALSE, n = 1000, vcov_fix = TRUE, nthreads = getFixest_nthreads(), ... ) ## S3 method for class 'fixest_list' summary( object, se, cluster, ssc = getFixest_ssc(), .vcov, stage = 2, lean = FALSE, n, ... )## S3 method for class 'fixest' summary( object, vcov = NULL, cluster = NULL, ssc = NULL, .vcov = NULL, stage = NULL, lean = FALSE, agg = NULL, forceCovariance = FALSE, se = NULL, keepBounded = FALSE, n = 1000, vcov_fix = TRUE, nthreads = getFixest_nthreads(), ... ) ## S3 method for class 'fixest_list' summary( object, se, cluster, ssc = getFixest_ssc(), .vcov, stage = 2, lean = FALSE, n, ... )
object |
A |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
.vcov |
A user provided covariance matrix or a function computing this matrix. If a matrix, it must be a square matrix of the same number of rows as the number of variables estimated. If a function, it must return the previously mentioned matrix. |
stage |
Can be equal to |
lean |
Logical, default is |
agg |
A character scalar describing the variable names to be aggregated,
it is pattern-based. For |
forceCovariance |
(Advanced users.) Logical, default is |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
keepBounded |
(Advanced users – |
n |
Integer, default is 1000. Number of coefficients to display when the print method is used. |
vcov_fix |
Logical scalar, default is |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
... |
Only used if the argument |
It returns a fixest object with:
cov.scaled |
The new variance-covariance matrix (computed according to the argument |
se |
The new standard-errors (computed according to the argument |
coeftable |
The table of coefficients with the new standard errors. |
The VCOVs from sandwich can be used with feols, feglm and fepois estimations.
If you want to have a sandwich VCOV when using summary.fixest, you can use
the argument vcov to specify the VCOV function to use (see examples).
Note that if you do so and you use a formula in the cluster argument, an innocuous
warning can pop up if you used several non-numeric fixed-effects in the estimation
(this is due to the function expand.model.frame used in sandwich).
Laurent Berge
See also the main estimation functions femlm, feols or feglm.
Use fixef.fixest to extract the fixed-effects coefficients, and the function etable
to visualize the results of multiple estimations.
# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = fepois(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # Comparing different types of standard errors sum_standard = summary(est_pois, vcov = "iid") sum_hetero = summary(est_pois, vcov = "hetero") sum_oneway = summary(est_pois, vcov = "cluster") sum_twoway = summary(est_pois, vcov = "twoway") etable(sum_standard, sum_hetero, sum_oneway, sum_twoway) # Alternative ways to cluster the SE: summary(est_pois, vcov = cluster ~ Product + Origin) summary(est_pois, vcov = ~Product + Origin) summary(est_pois, cluster = ~Product + Origin) # You can interact the clustering variables "live" using the var1 ^ var2 syntax.#' summary(est_pois, vcov = ~Destination^Product) # # Newey-West and Driscoll-Kraay SEs # data(base_did) # Simple estimation on a panel est = feols(y ~ x1, base_did) # -- # Newey-West # Use the syntax NW ~ unit + time summary(est, NW ~ id + period) # Now take a lag of 3: summary(est, NW(3) ~ id + period) # -- # Driscoll-Kraay # Use the syntax DK ~ time summary(est, DK ~ period) # Now take a lag of 3: summary(est, DK(3) ~ period) #-- # Implicit deductions # When the estimation is done with a panel.id, you don't need to # specify these values. est_panel = feols(y ~ x1, base_did, panel.id = ~id + period) # Both methods, NM and DK, now work automatically summary(est_panel, "NW") summary(est_panel, "DK") # # VCOVs robust to spatial correlation # data(quakes) est_geo = feols(depth ~ mag, quakes) # -- # Conley # Use the syntax: conley(cutoff) ~ lat + lon # with lat/lon the latitude/longitude variable names in the data set summary(est_geo, conley(100) ~ lat + long) # Change the cutoff, and how the distance is computed summary(est_geo, conley(200, distance = "spherical") ~ lat + long) # -- # Implicit deduction # By default the latitude and longitude are directly fetched in the data based # on pattern matching. So you don't have to specify them. # Further an automatic cutoff is computed by default. # The following works summary(est_geo, "conley") # # Compatibility with sandwich # # You can use the VCOVs from sandwich by using the argument .vcov: library(sandwich) summary(est_pois, .vcov = vcovCL, cluster = trade[, c("Destination", "Product")])# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = fepois(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # Comparing different types of standard errors sum_standard = summary(est_pois, vcov = "iid") sum_hetero = summary(est_pois, vcov = "hetero") sum_oneway = summary(est_pois, vcov = "cluster") sum_twoway = summary(est_pois, vcov = "twoway") etable(sum_standard, sum_hetero, sum_oneway, sum_twoway) # Alternative ways to cluster the SE: summary(est_pois, vcov = cluster ~ Product + Origin) summary(est_pois, vcov = ~Product + Origin) summary(est_pois, cluster = ~Product + Origin) # You can interact the clustering variables "live" using the var1 ^ var2 syntax.#' summary(est_pois, vcov = ~Destination^Product) # # Newey-West and Driscoll-Kraay SEs # data(base_did) # Simple estimation on a panel est = feols(y ~ x1, base_did) # -- # Newey-West # Use the syntax NW ~ unit + time summary(est, NW ~ id + period) # Now take a lag of 3: summary(est, NW(3) ~ id + period) # -- # Driscoll-Kraay # Use the syntax DK ~ time summary(est, DK ~ period) # Now take a lag of 3: summary(est, DK(3) ~ period) #-- # Implicit deductions # When the estimation is done with a panel.id, you don't need to # specify these values. est_panel = feols(y ~ x1, base_did, panel.id = ~id + period) # Both methods, NM and DK, now work automatically summary(est_panel, "NW") summary(est_panel, "DK") # # VCOVs robust to spatial correlation # data(quakes) est_geo = feols(depth ~ mag, quakes) # -- # Conley # Use the syntax: conley(cutoff) ~ lat + lon # with lat/lon the latitude/longitude variable names in the data set summary(est_geo, conley(100) ~ lat + long) # Change the cutoff, and how the distance is computed summary(est_geo, conley(200, distance = "spherical") ~ lat + long) # -- # Implicit deduction # By default the latitude and longitude are directly fetched in the data based # on pattern matching. So you don't have to specify them. # Further an automatic cutoff is computed by default. # The following works summary(est_geo, "conley") # # Compatibility with sandwich # # You can use the VCOVs from sandwich by using the argument .vcov: library(sandwich) summary(est_pois, .vcov = vcovCL, cluster = trade[, c("Destination", "Product")])
Summary information for fixest_multi objects. In particular, this is used to specify the type of standard-errors to be computed.
## S3 method for class 'fixest_multi' summary( object, type = "short", vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, .vcov = NULL, stage = 2, lean = FALSE, n = 1000, ... )## S3 method for class 'fixest_multi' summary( object, type = "short", vcov = NULL, se = NULL, cluster = NULL, ssc = NULL, .vcov = NULL, stage = 2, lean = FALSE, n = 1000, ... )
object |
A |
type |
A character either equal to |
vcov, .vcov
|
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
stage |
Can be equal to |
lean |
Logical, default is |
n |
Integer, default is 1000. Number of coefficients to display when the print method is used. |
... |
Not currently used. |
It returns either an object of class fixest_multi (if type equals short or long),
either a data.frame (if type equals compact or se_compact).
The main fixest estimation functions: feols, fepois,
fenegbin, feglm, feNmlm. Tools for mutliple fixest
estimations: summary.fixest_multi, print.fixest_multi, as.list.fixest_multi,
sub-sub-.fixest_multi, sub-.fixest_multi.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # By default, the type is "short" # You can still use the arguments from summary.fixest summary(res, se = "hetero") summary(res, type = "long") summary(res, type = "compact") summary(res, type = "se_compact") summary(res, type = "se_long")base = iris names(base) = c("y", "x1", "x2", "x3", "species") # Multiple estimation res = feols(y ~ csw(x1, x2, x3), base, split = ~species) # By default, the type is "short" # You can still use the arguments from summary.fixest summary(res, se = "hetero") summary(res, type = "long") summary(res, type = "compact") summary(res, type = "se_compact") summary(res, type = "se_long")
This function summarizes the main characteristics of the fixed-effects coefficients. It shows the number of fixed-effects that have been set as references and the first elements of the fixed-effects.
## S3 method for class 'fixest.fixef' summary(object, n = 5, ...)## S3 method for class 'fixest.fixef' summary(object, n = 5, ...)
object |
An object returned by the function |
n |
Positive integer, defaults to 5. The |
... |
Not currently used. |
It prints the number of fixed-effect coefficients per fixed-effect dimension, as well as
the number of fixed-effects used as references for each dimension, and the mean and variance
of the fixed-effect coefficients. Finally, it reports the first 5 (arg. n) elements of
each fixed-effect.
Laurent Berge
femlm, fixef.fixest, plot.fixest.fixef.
data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # obtaining the fixed-effects coefficients fe_trade = fixef(est_pois) # printing some summary information on the fixed-effects coefficients: summary(fe_trade)data(trade) # We estimate the effect of distance on trade # => we account for 3 fixed-effects effects est_pois = femlm(Euros ~ log(dist_km)|Origin+Destination+Product, trade) # obtaining the fixed-effects coefficients fe_trade = fixef(est_pois) # printing some summary information on the fixed-effects coefficients: summary(fe_trade)
User-level method to implement staggered difference-in-difference estimations a la Sun and Abraham (Journal of Econometrics, 2021).
sunab( cohort, period, ref.c = NULL, ref.p = -1, bin, bin.rel, bin.c, bin.p, att = FALSE, no_agg = FALSE ) sunab_att(cohort, period, ref.c = NULL, ref.p = -1)sunab( cohort, period, ref.c = NULL, ref.p = -1, bin, bin.rel, bin.c, bin.p, att = FALSE, no_agg = FALSE ) sunab_att(cohort, period, ref.c = NULL, ref.p = -1)
cohort |
A vector representing the cohort. It should represent the period at which the treatment has been received (and thus be fixed for each unit). |
period |
A vector representing the period. It can be either a relative time period (with negative values representing the before the treatment and positive values after the treatment), or a regular time period. In the latter case, the relative time period will be created from the cohort information (which represents the time at which the treatment has been received). |
ref.c |
A vector of references for the cohort. By default the never treated cohorts are taken as reference and the always treated are excluded from the estimation. You can add more references with this argument, which means that dummies will not be created for them (but they will remain in the estimation). |
ref.p |
A vector of references for the (relative!) period. By default the
first relative period (RP) before the treatment, i.e. -1, is taken as reference.
You can instead use your own references (i.e. RPs for which dummies will not be
created – but these observations remain in the sample). Please note that you will
need at least two references. You can use the special variables |
bin |
A list of values to be grouped, a vector, or the special value |
bin.rel |
A list or a vector defining which values to bin. Only applies to the
relative periods and not the cohorts. Please refer to the help of the argument
|
bin.c |
A list or a vector defining which values to bin. Only applies to the cohort.
Please refer to the help of the argument |
bin.p |
A list or a vector defining which values to bin. Only applies to the period.
Please refer to the help of the argument |
att |
Logical, default is |
no_agg |
Logical, default is |
This function creates a matrix of cohort x relative_period interactions, and if used within
a fixest estimation, the coefficients will automatically be aggregated to obtain the ATT
for each relative period. In practice, the coefficients are aggregated with the
aggregate.fixest function whose argument agg is automatically set to the appropriate
value.
The SA method requires relative periods (negative/positive for before/after the treatment). Either the user can compute the RP (relative periods) by his/her own, either the RPs are computed on the fly from the periods and the cohorts (which then should represent the treatment period).
The never treated, which are the cohorts displaying only negative RPs are used as references (i.e. no dummy will be constructed for them). On the other hand, the always treated are removed from the estimation, by means of adding NAs for each of their observations.
If the RPs have to be constructed on the fly, any cohort that is not present in the
period is considered as never treated. This means that if the period ranges from
1995 to 2005, cohort = 1994 will be considered as never treated, although it
should be considered as always treated: so be careful.
If you construct your own relative periods, the controls cohorts should have only negative RPs.
If not used within a fixest estimation, this function will return a matrix of
interacted coefficients.
You can bin periods with the arguments bin, bin.c, bin.p and/or bin.rel.
The argument bin applies both to the original periods and cohorts (the cohorts will also
be binned!). This argument only works when the period represent "calendar" periods
(not relative ones!).
Alternatively you can bin the periods with bin.p (either "calendar" or relative); or
the cohorts with bin.c.
The argument bin.rel applies only to the relative periods (hence not to the cohorts) once
they have been created.
To understand how binning works, please have a look at the help and examples of the
function bin.
Binning can be done in many different ways: just remember that it is not because it is possible that it does makes sense!
Laurent Berge
# Simple DiD example data(base_stagg) head(base_stagg) # Note that the year_treated is set to 1000 for the never treated table(base_stagg$year_treated) table(base_stagg$time_to_treatment) # The DiD estimation res_sunab = feols(y ~ x1 + sunab(year_treated, year) | id + year, base_stagg) etable(res_sunab) # By default the reference periods are the first year and the year before the treatment # i.e. ref.p = c(-1, .F); where .F is a shortcut for the first period. # Say you want to set as references the first three periods on top of -1 res_sunab_3ref = feols(y ~ x1 + sunab(year_treated, year, ref.p = c(.F + 0:2, -1)) | id + year, base_stagg) # Display the two results iplot(list(res_sunab, res_sunab_3ref)) # ... + show all refs iplot(list(res_sunab, res_sunab_3ref), ref = "all") # # ATT # # To get the total ATT, you can use summary with the agg argument: summary(res_sunab, agg = "ATT") # You can also look at the total effect per cohort summary(res_sunab, agg = "cohort") # # Binning # # Binning can be done in many different ways # binning the cohort est_bin.c = feols(y ~ x1 + sunab(year_treated, year, bin.c = 3:2) | id + year, base_stagg) # binning the period est_bin.p = feols(y ~ x1 + sunab(year_treated, year, bin.p = 3:1) | id + year, base_stagg) # binning both the cohort and the period est_bin = feols(y ~ x1 + sunab(year_treated, year, bin = 3:1) | id + year, base_stagg) # binning the relative period, grouping every two years est_bin.rel = feols(y ~ x1 + sunab(year_treated, year, bin.rel = "bin::2") | id + year, base_stagg) etable(est_bin.c, est_bin.p, est_bin, est_bin.rel, keep = "year")# Simple DiD example data(base_stagg) head(base_stagg) # Note that the year_treated is set to 1000 for the never treated table(base_stagg$year_treated) table(base_stagg$time_to_treatment) # The DiD estimation res_sunab = feols(y ~ x1 + sunab(year_treated, year) | id + year, base_stagg) etable(res_sunab) # By default the reference periods are the first year and the year before the treatment # i.e. ref.p = c(-1, .F); where .F is a shortcut for the first period. # Say you want to set as references the first three periods on top of -1 res_sunab_3ref = feols(y ~ x1 + sunab(year_treated, year, ref.p = c(.F + 0:2, -1)) | id + year, base_stagg) # Display the two results iplot(list(res_sunab, res_sunab_3ref)) # ... + show all refs iplot(list(res_sunab, res_sunab_3ref), ref = "all") # # ATT # # To get the total ATT, you can use summary with the agg argument: summary(res_sunab, agg = "ATT") # You can also look at the total effect per cohort summary(res_sunab, agg = "cohort") # # Binning # # Binning can be done in many different ways # binning the cohort est_bin.c = feols(y ~ x1 + sunab(year_treated, year, bin.c = 3:2) | id + year, base_stagg) # binning the period est_bin.p = feols(y ~ x1 + sunab(year_treated, year, bin.p = 3:1) | id + year, base_stagg) # binning both the cohort and the period est_bin = feols(y ~ x1 + sunab(year_treated, year, bin = 3:1) | id + year, base_stagg) # binning the relative period, grouping every two years est_bin.rel = feols(y ~ x1 + sunab(year_treated, year, bin.rel = "bin::2") | id + year, base_stagg) etable(est_bin.c, est_bin.p, est_bin, est_bin.rel, keep = "year")
This function extracts the terms of a fixest estimation, excluding the fixed-effects part.
## S3 method for class 'fixest' terms(x, ...)## S3 method for class 'fixest' terms(x, ...)
x |
A |
... |
Not currently used. |
An object of class c("terms", "formula") which contains the terms representation of a
symbolic model.
# simple estimation on iris data, using "Species" fixed-effects res = feols(Sepal.Length ~ Sepal.Width*Petal.Length + Petal.Width | Species, iris) # Terms of the linear part terms(res)# simple estimation on iris data, using "Species" fixed-effects res = feols(Sepal.Length ~ Sepal.Width*Petal.Length + Petal.Width | Species, iris) # Terms of the linear part terms(res)
Tool to transform any type of vector, or even combination of vectors, into an integer vector ranging from 1 to the number of unique values. This actually creates an unique identifier vector.
to_integer( ..., sorted = FALSE, add_items = FALSE, items.list = FALSE, multi.df = FALSE, multi.join = "_", internal = FALSE )to_integer( ..., sorted = FALSE, add_items = FALSE, items.list = FALSE, multi.df = FALSE, multi.join = "_", internal = FALSE )
... |
Vectors of any type, to be transformed in integer. |
sorted |
Logical, default is |
add_items |
Logical, default is |
items.list |
Logical, default is |
multi.df |
Logical, default is |
multi.join |
Character scalar used to join the items of multiple vectors.
The default is |
internal |
Logical, default is |
Reruns a vector of the same length as the input vectors.
If add_items=TRUE and items.list=TRUE, a list of two elements is returned: x
being the integer vector and items being the unique values to which the values
in x make reference.
Laurent Berge
x1 = iris$Species x2 = as.integer(iris$Sepal.Length) # transforms the species vector into integers to_integer(x1) # To obtain the "items": to_integer(x1, add_items = TRUE) # same but in list form to_integer(x1, add_items = TRUE, items.list = TRUE) # transforms x2 into an integer vector from 1 to 4 to_integer(x2, add_items = TRUE) # To have the sorted items: to_integer(x2, add_items = TRUE, sorted = TRUE) # The result can safely be used as an index res = to_integer(x2, add_items = TRUE, sorted = TRUE, items.list = TRUE) all(res$items[res$x] == x2) # # Multiple vectors # to_integer(x1, x2, add_items = TRUE) # You can use multi.join to handle the join of the items: to_integer(x1, x2, add_items = TRUE, multi.join = "; ")x1 = iris$Species x2 = as.integer(iris$Sepal.Length) # transforms the species vector into integers to_integer(x1) # To obtain the "items": to_integer(x1, add_items = TRUE) # same but in list form to_integer(x1, add_items = TRUE, items.list = TRUE) # transforms x2 into an integer vector from 1 to 4 to_integer(x2, add_items = TRUE) # To have the sorted items: to_integer(x2, add_items = TRUE, sorted = TRUE) # The result can safely be used as an index res = to_integer(x2, add_items = TRUE, sorted = TRUE, items.list = TRUE) all(res$items[res$x] == x2) # # Multiple vectors # to_integer(x1, x2, add_items = TRUE) # You can use multi.join to handle the join of the items: to_integer(x1, x2, add_items = TRUE, multi.join = "; ")
This data reports trade information between countries of the European Union (EU15).
data(trade)data(trade)
trade is a data frame with 38,325 observations and 6 variables named Destination, Origin, Product, Year, dist_km and Euros.
Origin: 2-digits codes of the countries of origin of the trade flow.
Destination: 2-digits codes of the countries of destination of the trade flow.
Products: Number representing the product categories (from 1 to 20).
Year: Years from 2007 to 2016
dist_km: Geographic distance in km between the centers of the countries of origin and destination.
Euros: The total amount in euros of the trade flow for the specific year/product category/origin-destination country pair.
This data has been extrated from Eurostat on October 2017.
fixest panelTransforms a fixest_panel object into a regular data.frame.
unpanel(x)unpanel(x)
x |
A |
Returns a data set of the exact same dimension. Only the attribute 'panel_info' is erased.
Laurent Berge
Alternatively, the function panel changes a data.frame into a panel from which the
functions l and f (creating leads and lags) can be called. Otherwise you can set the panel
'live' during the estimation using the argument panel.id (see for example in the function
feols).
data(base_did) # Setting a data set as a panel pdat = panel(base_did, ~id+period) # ... allows you to use leads and lags in estimations feols(y~l(x1, 0:1), pdat) # Now unpanel => returns the initial data set class(pdat) ; dim(pdat) new_base = unpanel(pdat) class(new_base) ; dim(new_base)data(base_did) # Setting a data set as a panel pdat = panel(base_did, ~id+period) # ... allows you to use leads and lags in estimations feols(y~l(x1, 0:1), pdat) # Now unpanel => returns the initial data set class(pdat) ; dim(pdat) new_base = unpanel(pdat) class(new_base) ; dim(new_base)
fixest estimationUpdates and re-estimates a fixest model (estimated with femlm, feols or feglm).
This function updates the formulas and use previous starting values to estimate a new
fixest model. The data is obtained from the original call.
## S3 method for class 'fixest' update(object, fml.update, nframes = 1, evaluate = TRUE, ...) ## S3 method for class 'fixest_multi' update(object, fml.update, nframes = 1, evaluate = TRUE, ...)## S3 method for class 'fixest' update(object, fml.update, nframes = 1, evaluate = TRUE, ...) ## S3 method for class 'fixest_multi' update(object, fml.update, nframes = 1, evaluate = TRUE, ...)
object |
A |
fml.update |
Changes to be made to the original argument |
nframes |
(Advanced users.) Defaults to 1. Number of frames up the stack where to perform the evaluation of the updated call. By default, this is the parent frame. |
evaluate |
Logical, default is |
... |
Other arguments to be passed to the functions |
It returns a fixest object (see details in femlm, feols or feglm).
Laurent Berge
See also the main estimation functions femlm, feols or feglm. predict.fixest, summary.fixest, vcov.fixest, fixef.fixest.
# Example using trade data data(trade) # main estimation est_pois = fepois(Euros ~ log(dist_km) | Origin + Destination, trade) # we add the variable log(Year) est_2 = update(est_pois, . ~ . + log(Year)) # we add another fixed-effect: "Product" est_3 = update(est_2, . ~ . | . + Product) # we remove the fixed-effect "Origin" and the variable log(dist_km) est_4 = update(est_3, . ~ . - log(dist_km) | . - Origin) # Quick look at the 4 estimations etable(est_pois, est_2, est_3, est_4)# Example using trade data data(trade) # main estimation est_pois = fepois(Euros ~ log(dist_km) | Origin + Destination, trade) # we add the variable log(Year) est_2 = update(est_pois, . ~ . + log(Year)) # we add another fixed-effect: "Product" est_3 = update(est_2, . ~ . | . + Product) # we remove the fixed-effect "Origin" and the variable log(dist_km) est_4 = update(est_3, . ~ . - log(dist_km) | . - Origin) # Quick look at the 4 estimations etable(est_pois, est_2, est_3, est_4)
Computes the clustered VCOV of fixest objects.
vcov_cluster(x, cluster = NULL, ssc = NULL, vcov_fix = TRUE)vcov_cluster(x, cluster = NULL, ssc = NULL, vcov_fix = TRUE)
x |
A |
cluster |
Either i) a character vector giving the names of the variables onto which to cluster, or ii) a formula giving those names, or iii) a vector/list/data.frame giving the hard values of the clusters. Note that in cases i) and ii) the variables are fetched directly in the data set used for the estimation. |
ssc |
An object returned by the function |
vcov_fix |
Logical scalar, default is |
If the first argument is a fixest object, then a VCOV is returned (i.e. a symmetric matrix).
If the first argument is not a fixest object, then a) implicitly the arguments are shifted to
the left (i.e. vcov_cluster(~var1 + var2) is equivalent to
vcov_cluster(cluster = ~var1 + var2)) and b) a VCOV-request is returned and NOT a VCOV.
That VCOV-request can then be used in the argument vcov of various fixest
functions (e.g. vcov.fixest or even in the estimation calls).
Laurent Berge
Cameron AC, Gelbach JB, Miller DL (2011). "Robust Inference with Multiway Clustering." Journal of Business & Economic Statistics, 29(2), 238-249. doi:10.1198/jbes.2010.07136.
base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$clu = rep(1:5, 30) est = feols(y ~ x1, base) # VCOV: using a formula giving the name of the clusters vcov_cluster(est, ~species + clu) # works as well with a character vector vcov_cluster(est, c("species", "clu")) # you can also combine the two with '^' vcov_cluster(est, ~species^clu) # # Using VCOV requests # # per se: pretty useless... vcov_cluster(~species) # ...but VCOV-requests can be used at estimation time: # it may be more explicit than... feols(y ~ x1, base, vcov = vcov_cluster("species")) # ...the equivalent, built-in way: feols(y ~ x1, base, vcov = ~species) # The argument vcov does not accept hard values, # so you can feed them with a VCOV-request: feols(y ~ x1, base, vcov = vcov_cluster(rep(1:5, 30)))base = iris names(base) = c("y", "x1", "x2", "x3", "species") base$clu = rep(1:5, 30) est = feols(y ~ x1, base) # VCOV: using a formula giving the name of the clusters vcov_cluster(est, ~species + clu) # works as well with a character vector vcov_cluster(est, c("species", "clu")) # you can also combine the two with '^' vcov_cluster(est, ~species^clu) # # Using VCOV requests # # per se: pretty useless... vcov_cluster(~species) # ...but VCOV-requests can be used at estimation time: # it may be more explicit than... feols(y ~ x1, base, vcov = vcov_cluster("species")) # ...the equivalent, built-in way: feols(y ~ x1, base, vcov = ~species) # The argument vcov does not accept hard values, # so you can feed them with a VCOV-request: feols(y ~ x1, base, vcov = vcov_cluster(rep(1:5, 30)))
Compute VCOVs robust to spatial correlation, a la Conley (1999).
vcov_conley( x, lat = NULL, lon = NULL, cutoff = NULL, pixel = 0, distance = "triangular", ssc = NULL, vcov_fix = TRUE ) conley(cutoff = NULL, pixel = NULL, distance = NULL)vcov_conley( x, lat = NULL, lon = NULL, cutoff = NULL, pixel = 0, distance = "triangular", ssc = NULL, vcov_fix = TRUE ) conley(cutoff = NULL, pixel = NULL, distance = NULL)
x |
A |
lat |
A character scalar or a one sided formula giving the name of the variable representing the latitude. The latitude must lie in [-90, 90], [0, 180] or [-180, 0]. |
lon |
A character scalar or a one sided formula giving the name of the variable representing the longitude. The longitude must be in [-180, 180], [0, 360] or [-360, 0]. |
cutoff |
The distance cutoff, in km. You can express the cutoff in miles by writing the number in character form and adding "mi" as a suffix: cutoff = "100mi" would be 100 miles. If missing, a rule of thumb is used to deduce the cutoff. |
pixel |
A positive numeric scalar, default is 0. If a positive number, the coordinates of
each observation are pooled into |
distance |
How to compute the distance between points. It can be equal to "triangular" (default) or "spherical". The latter case corresponds to the great circle distance and is more precise than triangular but is a bit more intensive computationally. |
ssc |
An object returned by the function |
vcov_fix |
Logical scalar, default is |
This function computes VCOVs that are robust to spatial correlations by assuming a correlation between the units that are at a geographic distance lower than a given cutoff.
The kernel is uniform.
If the cutoff is not provided, an estimation of it is given. This cutoff ensures that a minimum of units lie within it and is robust to sub-sampling. This automatic cutoff is only here for convenience, the most appropriate cutoff shall depend on the application and shall be provided by the user.
The function conley does not compute VCOVs directly but is meant to be used in the argument
vcov of fixest functions (e.g. in vcov.fixest or even in the estimation calls).
If the first argument is a fixest object, then a VCOV is returned (i.e. a symmetric matrix).
If the first argument is not a fixest object, then a) implicitly the arguments are shifted to
the left (i.e. vcov_conley("lat", "long") is equivalent to
vcov_conley(lat = "lat", lon = "long")) and b) a VCOV-request is returned and NOT a VCOV.
That VCOV-request can then be used in the argument vcov of various fixest functions
(e.g. vcov.fixest or even in the estimation calls).
Conley TG (1999). "GMM Estimation with Cross Sectional Dependence", Journal of Econometrics, 92, 1-45.
data(quakes) # We use conley() in the vcov argument of the estimation feols(depth ~ mag, quakes, conley(100)) # Post estimation est = feols(depth ~ mag, quakes) vcov_conley(est, cutoff = 100)data(quakes) # We use conley() in the vcov argument of the estimation feols(depth ~ mag, quakes, conley(100)) # Post estimation est = feols(depth ~ mag, quakes) vcov_conley(est, cutoff = 100)
Set of functions to compute the VCOVs robust to different forms correlation in panel or time series settings.
vcov_DK(x, time = NULL, lag = NULL, ssc = NULL, vcov_fix = TRUE) vcov_NW(x, unit = NULL, time = NULL, lag = NULL, ssc = NULL, vcov_fix = TRUE) NW(lag = NULL) newey_west(lag = NULL) DK(lag = NULL) driscoll_kraay(lag = NULL)vcov_DK(x, time = NULL, lag = NULL, ssc = NULL, vcov_fix = TRUE) vcov_NW(x, unit = NULL, time = NULL, lag = NULL, ssc = NULL, vcov_fix = TRUE) NW(lag = NULL) newey_west(lag = NULL) DK(lag = NULL) driscoll_kraay(lag = NULL)
x |
A |
time |
A character scalar or a one sided formula giving the name of the variable representing the time. |
lag |
An integer scalar, default is |
ssc |
An object returned by the function |
vcov_fix |
Logical scalar, default is |
unit |
A character scalar or a one sided formula giving the name of the variable representing the units of the panel. |
There are currently three VCOV types: Newey-West applied to time series, Newey-West applied to a panel setting (when the argument 'unit' is not missing), and Driscoll-Kraay.
The functions on this page without the prefix "vcov_" do not compute VCOVs directly but
are meant to be used in the argument vcov of fixest functions (e.g. in vcov.fixest
or even in the estimation calls).
Note that for Driscoll-Kraay VCOVs, to ensure its properties the number of periods should be long enough (a minimum of 20 periods or so).
If the first argument is a fixest object, then a VCOV is returned (i.e. a symmetric matrix).
If the first argument is not a fixest object, then a) implicitly the arguments are shifted to
the left (i.e. vcov_DK(~year) is equivalent to vcov_DK(time = ~year)) and b) a
VCOV-request is returned and NOT a VCOV. That VCOV-request can then be used in the argument
vcov of various fixest functions (e.g. vcov.fixest or even in the estimation calls).
The default lag selection depends on whether the VCOV applies to a panel or a time series.
For panels, i.e. panel Newey-West or Driscoll-Kraay VCOV, the default lag is n_t^0.25 with
n_t the number of time periods. This is based on Newey and West 1987.
For time series Newey-West, the default lag is found thanks to the
bwNeweyWest function from the sandwich package. It is based on
Newey and West 1994.
Newey WK, West KD (1987). "A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix." Econometrica, 55(3), 703-708. doi:10.2307/1913610.
Driscoll JC, Kraay AC (1998). "Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data." The Review of Economics and Statistics, 80(4), 549-560. doi:10.1162/003465398557825.
Millo G (2017). "Robust Standard Error Estimators for Panel Models: A Unifying Approach" Journal of Statistical Software, 82(3). doi:10.18637/jss.v082.i03.
data(base_did) # # During the estimation # # Panel Newey-West, lag = 2 feols(y ~ x1, base_did, NW(2) ~ id + period) # Driscoll-Kraay feols(y ~ x1, base_did, DK ~ period) # If the estimation is made with a panel.id, the dimensions are # automatically deduced: est = feols(y ~ x1, base_did, "NW", panel.id = ~id + period) est # # Post estimation # # If missing, the unit and time are automatically deduced from # the panel.id used in the estimation vcov_NW(est, lag = 2)data(base_did) # # During the estimation # # Panel Newey-West, lag = 2 feols(y ~ x1, base_did, NW(2) ~ id + period) # Driscoll-Kraay feols(y ~ x1, base_did, DK ~ period) # If the estimation is made with a panel.id, the dimensions are # automatically deduced: est = feols(y ~ x1, base_did, "NW", panel.id = ~id + period) est # # Post estimation # # If missing, the unit and time are automatically deduced from # the panel.id used in the estimation vcov_NW(est, lag = 2)
fixest objectThis function extracts the variance-covariance of estimated parameters from a model
estimated with femlm, feols or feglm.
## S3 method for class 'fixest' vcov( object, vcov = NULL, se = NULL, cluster, ssc = NULL, attr = FALSE, forceCovariance = FALSE, keepBounded = FALSE, nthreads = getFixest_nthreads(), vcov_fix = TRUE, ... )## S3 method for class 'fixest' vcov( object, vcov = NULL, se = NULL, cluster, ssc = NULL, attr = FALSE, forceCovariance = FALSE, keepBounded = FALSE, nthreads = getFixest_nthreads(), vcov_fix = TRUE, ... )
object |
A |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
ssc |
An object of class |
attr |
Logical, defaults to |
forceCovariance |
(Advanced users.) Logical, default is |
keepBounded |
(Advanced users – |
nthreads |
The number of threads. Can be: a) an integer lower than, or equal to,
the maximum number of threads; b) 0: meaning all available threads will be used;
c) a number strictly between 0 and 1 which represents the fraction of all threads to use.
The default is to use 50% of all threads. You can set permanently the number
of threads used within this package using the function |
vcov_fix |
Logical scalar, default is |
... |
Other arguments to be passed to The computation of the VCOV matrix is first done in |
For an explanation on how the standard-errors are computed and what is the exact meaning of the arguments, please have a look at the dedicated vignette: On standard-errors.
It returns a square matrix where is the number of variables
of the fitted model.
If attr = TRUE, this matrix has an attribute “type” specifying how this
variance/covariance matrix has been computed.
Laurent Berge
Ding, Peng, 2021, "The Frisch–Waugh–Lovell theorem for standard errors." Statistics & Probability Letters 168.
You can also compute VCOVs with the following functions: vcov_cluster,
vcov_hac, vcov_conley.
See also the main estimation functions femlm, feols or feglm.
summary.fixest, confint.fixest, resid.fixest, predict.fixest, fixef.fixest.
# Load panel data data(base_did) # Simple estimation on a panel est = feols(y ~ x1, base_did) # ======== # # IID VCOV # # ======== # # By default the VCOV assumes iid errors: se(vcov(est)) # You can make the call for an iid VCOV explicitly: se(vcov(est, "iid")) # # Heteroskedasticity-robust VCOV # # By default the VCOV assumes iid errors: se(vcov(est, "hetero")) # => note that it also accepts vcov = "White" and vcov = "HC1" as aliases. # =============== # # Clustered VCOVs # # =============== # # To cluster the VCOV, you can use a formula of the form cluster ~ var1 + var2 etc # Let's cluster by the panel ID: se(vcov(est, cluster ~ id)) # Alternative ways: # -> cluster is implicitly assumed when a one-sided formula is provided se(vcov(est, ~ id)) # -> using the argument cluster instead of vcov se(vcov(est, cluster = ~ id)) # For two-/three- way clustering, just add more variables: se(vcov(est, ~ id + period)) # -------------------| # Implicit deduction | # -------------------| # When the estimation contains FEs, the dimension on which to cluster # is directly inferred from the FEs used in the estimation, so you don't need # to explicitly add them. est_fe = feols(y ~ x1 | id + period, base_did) # Clustered along "id" se(vcov(est_fe, "cluster")) # Clustered along "id" and "period" se(vcov(est_fe, "twoway")) # =========== # # Panel VCOVs # # =========== # # ---------------------| # Newey West (NW) VCOV | # ---------------------| # To obtain NW VCOVs, use a formula of the form NW ~ id + period se(vcov(est, NW ~ id + period)) # If you want to change the lag: se(vcov(est, NW(3) ~ id + period)) # Alternative way: # -> using the vcov_NW function se(vcov(est, vcov_NW(unit = "id", time = "period", lag = 3))) # -------------------------| # Driscoll-Kraay (DK) VCOV | # -------------------------| # To obtain DK VCOVs, use a formula of the form DK ~ period se(vcov(est, DK ~ period)) # If you want to change the lag: se(vcov(est, DK(3) ~ period)) # Alternative way: # -> using the vcov_DK function se(vcov(est, vcov_DK(time = "period", lag = 3))) # -------------------| # Implicit deduction | # -------------------| # When the estimation contains a panel identifier, you don't need # to re-write them later on est_panel = feols(y ~ x1, base_did, panel.id = ~id + period) # Both methods, NM and DK, now work automatically se(vcov(est_panel, "NW")) se(vcov(est_panel, "DK")) # =================================== # # VCOVs robust to spatial correlation # # =================================== # data(quakes) est_geo = feols(depth ~ mag, quakes) # ------------| # Conley VCOV | # ------------| # To obtain a Conley VCOV, use a formula of the form conley(cutoff) ~ lat + lon # with lat/lon the latitude/longitude variable names in the data set se(vcov(est_geo, conley(100) ~ lat + long)) # Alternative way: # -> using the vcov_DK function se(vcov(est_geo, vcov_conley(lat = "lat", lon = "long", cutoff = 100))) # -------------------| # Implicit deduction | # -------------------| # By default the latitude and longitude are directly fetched in the data based # on pattern matching. So you don't have to specify them. # Furhter, an automatic cutoff is deduced by default. # The following works: se(vcov(est_geo, "conley")) # ======================== # # Small Sample Corrections # # ======================== # # You can change the way the small sample corrections are done with the argument ssc. # The argument ssc must be created by the ssc function se(vcov(est, ssc = ssc(adj = FALSE))) # You can add directly the call to ssc in the vcov formula. # You need to add it like a variable: se(vcov(est, iid ~ ssc(adj = FALSE))) se(vcov(est, DK ~ period + ssc(adj = FALSE)))# Load panel data data(base_did) # Simple estimation on a panel est = feols(y ~ x1, base_did) # ======== # # IID VCOV # # ======== # # By default the VCOV assumes iid errors: se(vcov(est)) # You can make the call for an iid VCOV explicitly: se(vcov(est, "iid")) # # Heteroskedasticity-robust VCOV # # By default the VCOV assumes iid errors: se(vcov(est, "hetero")) # => note that it also accepts vcov = "White" and vcov = "HC1" as aliases. # =============== # # Clustered VCOVs # # =============== # # To cluster the VCOV, you can use a formula of the form cluster ~ var1 + var2 etc # Let's cluster by the panel ID: se(vcov(est, cluster ~ id)) # Alternative ways: # -> cluster is implicitly assumed when a one-sided formula is provided se(vcov(est, ~ id)) # -> using the argument cluster instead of vcov se(vcov(est, cluster = ~ id)) # For two-/three- way clustering, just add more variables: se(vcov(est, ~ id + period)) # -------------------| # Implicit deduction | # -------------------| # When the estimation contains FEs, the dimension on which to cluster # is directly inferred from the FEs used in the estimation, so you don't need # to explicitly add them. est_fe = feols(y ~ x1 | id + period, base_did) # Clustered along "id" se(vcov(est_fe, "cluster")) # Clustered along "id" and "period" se(vcov(est_fe, "twoway")) # =========== # # Panel VCOVs # # =========== # # ---------------------| # Newey West (NW) VCOV | # ---------------------| # To obtain NW VCOVs, use a formula of the form NW ~ id + period se(vcov(est, NW ~ id + period)) # If you want to change the lag: se(vcov(est, NW(3) ~ id + period)) # Alternative way: # -> using the vcov_NW function se(vcov(est, vcov_NW(unit = "id", time = "period", lag = 3))) # -------------------------| # Driscoll-Kraay (DK) VCOV | # -------------------------| # To obtain DK VCOVs, use a formula of the form DK ~ period se(vcov(est, DK ~ period)) # If you want to change the lag: se(vcov(est, DK(3) ~ period)) # Alternative way: # -> using the vcov_DK function se(vcov(est, vcov_DK(time = "period", lag = 3))) # -------------------| # Implicit deduction | # -------------------| # When the estimation contains a panel identifier, you don't need # to re-write them later on est_panel = feols(y ~ x1, base_did, panel.id = ~id + period) # Both methods, NM and DK, now work automatically se(vcov(est_panel, "NW")) se(vcov(est_panel, "DK")) # =================================== # # VCOVs robust to spatial correlation # # =================================== # data(quakes) est_geo = feols(depth ~ mag, quakes) # ------------| # Conley VCOV | # ------------| # To obtain a Conley VCOV, use a formula of the form conley(cutoff) ~ lat + lon # with lat/lon the latitude/longitude variable names in the data set se(vcov(est_geo, conley(100) ~ lat + long)) # Alternative way: # -> using the vcov_DK function se(vcov(est_geo, vcov_conley(lat = "lat", lon = "long", cutoff = 100))) # -------------------| # Implicit deduction | # -------------------| # By default the latitude and longitude are directly fetched in the data based # on pattern matching. So you don't have to specify them. # Furhter, an automatic cutoff is deduced by default. # The following works: se(vcov(est_geo, "conley")) # ======================== # # Small Sample Corrections # # ======================== # # You can change the way the small sample corrections are done with the argument ssc. # The argument ssc must be created by the ssc function se(vcov(est, ssc = ssc(adj = FALSE))) # You can add directly the call to ssc in the vcov formula. # You need to add it like a variable: se(vcov(est, iid ~ ssc(adj = FALSE))) se(vcov(est, DK ~ period + ssc(adj = FALSE)))
Wald test used to test the joint nullity of a set of coefficients.
wald(x, keep = NULL, drop = NULL, print = TRUE, vcov, se, cluster, ...)wald(x, keep = NULL, drop = NULL, print = TRUE, vcov, se, cluster, ...)
x |
A |
keep |
Character vector. This element is used to display only a subset of variables. This
should be a vector of regular expressions (see |
drop |
Character vector. This element is used if some variables are not to be displayed.
This should be a vector of regular expressions (see |
print |
Logical, default is |
vcov |
Versatile argument to specify the VCOV. In general, it is either a character
scalar equal to a VCOV type, either a formula of the form: |
se |
Character scalar. Which kind of standard error should be computed:
“standard”, “hetero”, “cluster”, “twoway”, “threeway”
or “fourway”? By default if there are clusters in the estimation:
|
cluster |
Tells how to cluster the standard-errors (if clustering is requested).
Can be either a list of vectors, a character vector of variable names, a formula or
an integer vector. Assume we want to perform 2-way clustering over |
... |
Any other element to be passed to |
The type of VCOV matrix plays a crucial role in this test. Use the arguments se and
cluster to change the type of VCOV for the test.
A named vector containing the following elements is returned: stat, p, df1,
and df2. They correspond to the test statistic, the p-value, the first and
second degrees of freedoms.
If no valid coefficient is found, the value NA is returned.
data(airquality) est = feols(Ozone ~ Solar.R + Wind + poly(Temp, 3), airquality) # Testing the joint nullity of the Temp polynomial wald(est, "poly") # Same but with clustered SEs wald(est, "poly", cluster = "Month") # Now: all vars but the polynomial and the intercept wald(est, drop = "Inte|poly") # # Toy example: testing pre-trends # data(base_did) est_did = feols(y ~ x1 + i(period, treat, 5) | id + period, base_did) # The graph of the coefficients coefplot(est_did) # The pre-trend test wald(est_did, "period::[1234]$") # If "period::[1234]$" looks weird to you, check out # regular expressions: e.g. see ?regex. # Learn it, you won't regret it!data(airquality) est = feols(Ozone ~ Solar.R + Wind + poly(Temp, 3), airquality) # Testing the joint nullity of the Temp polynomial wald(est, "poly") # Same but with clustered SEs wald(est, "poly", cluster = "Month") # Now: all vars but the polynomial and the intercept wald(est, drop = "Inte|poly") # # Toy example: testing pre-trends # data(base_did) est_did = feols(y ~ x1 + i(period, treat, 5) | id + period, base_did) # The graph of the coefficients coefplot(est_did) # The pre-trend test wald(est_did, "period::[1234]$") # If "period::[1234]$" looks weird to you, check out # regular expressions: e.g. see ?regex. # Learn it, you won't regret it!
fixest objectSimply extracts the weights used to estimate a fixest model.
## S3 method for class 'fixest' weights(object, ...)## S3 method for class 'fixest' weights(object, ...)
object |
A |
... |
Not currently used. |
Returns a vector of the same length as the number of observations in the original data set. Ignored observations due to NA or perfect fit are re-introduced and their weights set to NA.
feols, fepois, feglm, fenegbin, feNmlm.
est = feols(Petal.Length ~ Petal.Width, iris, weights = ~as.integer(Sepal.Length) - 3.99) weights(est)est = feols(Petal.Length ~ Petal.Width, iris, weights = ~as.integer(Sepal.Length) - 3.99) weights(est)
Create macros within formulas and expand them with character vectors or other formulas.
xpd(fml, ..., add = NULL, lhs, rhs, data = NULL, frame = parent.frame())xpd(fml, ..., add = NULL, lhs, rhs, data = NULL, frame = parent.frame())
fml |
A formula containing macros variables. Each macro variable must start with two dots.
The macro variables can be set globally using |
... |
Definition of the macro variables. Each argument name corresponds to the name of the
macro variable. It is required that each macro variable name starts with two dots
(e.g. |
add |
Either a character scalar or a one-sided formula. The elements will be added to the right-hand-side of the formula, before any macro expansion is applied. |
lhs |
If present then a formula will be constructed with |
rhs |
If present, then a formula will be constructed with |
data |
Either a character vector or a data.frame. This argument will only be used if a
macro of the type |
frame |
The environment containing the values to be expanded with the dot square bracket
operator. Default is |
In xpd, the default macro variables are taken from getFixest_fml. Any value in the ...
argument of xpd will replace these default values.
The definitions of the macro variables will replace in verbatim the macro variables. Therefore,
you can include multi-part formulas if you wish but then beware of the order of the macros
variable in the formula. For example, using the airquality data, say you want to set as
controls the variable Temp and Day fixed-effects, you can do
setFixest_fml(..ctrl = ~Temp | Day), but then feols(Ozone ~ Wind + ..ctrl, airquality)
will be quite different from feols(Ozone ~ ..ctrl + Wind, airquality), so beware!
It returns a formula where all macros have been expanded.
In a formula, the dot square bracket (DSB) operator can: i) create manifold variables at once, or ii) capture values from the current environment and put them verbatim in the formula.
Say you want to include the variables x1 to x3 in your formula. You can use
xpd(y ~ x.[1:3]) and you'll get y ~ x1 + x2 + x3.
To summon values from the environment, simply put the variable in square brackets. For example:
for(i in 1:3) xpd(y.[i] ~ x) will create the formulas y1 ~ x to y3 ~ x depending on the
value of i.
You can include a full variable from the environment in the same way:
for(y in c("a", "b")) xpd(.[y] ~ x) will create the two formulas a ~ x and b ~ x.
The DSB can even be used within variable names, but then the variable must be nested in
character form. For example y ~ .["x.[1:2]_sq"] will create y ~ x1_sq + x2_sq. Using the
character form is important to avoid a formula parsing error. Double quotes must be used. Note
that the character string that is nested will be parsed with the function dsb, and thus it
will return a vector.
By default, the DSB operator expands vectors into sums. You can add a comma, like in .[, x],
to expand with commas–the content can then be used within functions. For instance:
c(x.[, 1:2]) will create c(x1, x2) (and not c(x1 + x2)).
In all fixest estimations, this special parsing is enabled, so you don't need to use xpd.
One-sided formulas can be expanded with the DSB operator: let x = ~sepal + petal, then
xpd(y ~ .[x]) leads to color ~ sepal + petal.
You can even use multiple square brackets within a single variable, but then the use of nesting
is required. For example, the following xpd(y ~ .[".[letters[1:2]]_.[1:2]"]) will create
y ~ a_1 + b_2. Remember that the nested character string is parsed with dsb,
which explains this behavior.
When the element to be expanded i) is equal to the empty string or, ii) is of length 0, it is
replaced with a neutral element, namely 1. For example, x = "" ; xpd(y ~ .[x]) leads to
y ~ 1.
You can catch several variable names at once by using regular expressions. To use regular
expressions, you need to enclose it in the dot-dot or the regex function: ..("regex") or
regex("regex"). For example, regex("Sepal") will catch both the variables Sepal.Length and
Sepal.Width from the iris data set. In a fixest estimation, the variables names from which
the regex will be applied come from the data set. If you use xpd, you need to provide either a
data set or a vector of names in the argument data.
By default the variables are aggregated with a sum. For example in a data set with the variables
x1 to x10, regex("x(1|2)" will yield x1 + x2 + x10. You can instead ask for "comma"
aggregation by using a comma first, just before the regular expression:
y ~ sw(regex(,"x(1|2)")) would lead to y ~ sw(x1, x2, x10).
Note that the dot square bracket operator (DSB, see before) is applied before the regular
expression is evaluated. This means that regex("x.[3:4]_sq") will lead, after evaluation of
the DSB, to regex("x3_sq|x4_sq"). It is a handy way to insert range of numbers in a regular
expression.
Laurent Berge
setFixest_fml to set formula macros, and dsb to modify character strings with the DSB operator.
# Small examples with airquality data data(airquality) # we set two macro variables setFixest_fml(..ctrl = ~ Temp + Day, ..ctrl_long = ~ poly(Temp, 2) + poly(Day, 2)) # Using the macro in lm with xpd: lm(xpd(Ozone ~ Wind + ..ctrl), airquality) lm(xpd(Ozone ~ Wind + ..ctrl_long), airquality) # You can use the macros without xpd() in fixest estimations a = feols(Ozone ~ Wind + ..ctrl, airquality) b = feols(Ozone ~ Wind + ..ctrl_long, airquality) etable(a, b, keep = "Int|Win") # Using .[] base = setNames(iris, c("y", "x1", "x2", "x3", "species")) i = 2:3 z = "species" lm(xpd(y ~ x.[2:3] + .[z]), base) # No xpd() needed in feols feols(y ~ x.[2:3] + .[z], base) # # Auto completion with '..' suffix # # You can trigger variables autocompletion with the '..' suffix # You need to provide the argument data base = setNames(iris, c("y", "x1", "x2", "x3", "species")) xpd(y ~ x.., data = base) # In fixest estimations, this is automatically taken care of feols(y ~ x.., data = base) # # You can use xpd for stepwise estimations # # Note that for stepwise estimations in fixest, you can use # the stepwise functions: sw, sw0, csw, csw0 # -> see help in feols or in the dedicated vignette # we want to look at the effect of x1 on y # controlling for different variables base = iris names(base) = c("y", "x1", "x2", "x3", "species") # We first create a matrix with all possible combinations of variables my_args = lapply(names(base)[-(1:2)], function(x) c("", x)) (all_combs = as.matrix(do.call("expand.grid", my_args))) res_all = list() for(i in 1:nrow(all_combs)){ res_all[[i]] = feols(xpd(y ~ x1 + ..v, ..v = all_combs[i, ]), base) } etable(res_all) coefplot(res_all, group = list(Species = "^^species")) # # You can use macros to grep variables in your data set # # Example 1: setting a macro variable globally data(longley) setFixest_fml(..many_vars = grep("GNP|ployed", names(longley), value = TRUE)) feols(Armed.Forces ~ Population + ..many_vars, longley) # Example 2: using ..("regex") or regex("regex") to grep the variables "live" feols(Armed.Forces ~ Population + ..("GNP|ployed"), longley) # Example 3: same as Ex.2 but without using a fixest estimation # Here we need to use xpd(): lm(xpd(Armed.Forces ~ Population + regex("GNP|ployed"), data = longley), longley) # Stepwise estimation with regex: use a comma after the parenthesis feols(Armed.Forces ~ Population + sw(regex(,"GNP|ployed")), longley) # Multiple LHS etable(feols(..("GNP|ployed") ~ Population, longley)) # # lhs and rhs arguments # # to create a one sided formula from a character vector vars = letters[1:5] xpd(rhs = vars) # Alternatively, to replace the RHS xpd(y ~ 1, rhs = vars) # To create a two sided formula xpd(lhs = "y", rhs = vars) # # argument 'add' # xpd(~x1, add = ~ x2 + x3) # also works with character vectors xpd(~x1, add = c("x2", "x3")) # only adds to the RHS xpd(y ~ x, add = ~bon + jour) # # Dot square bracket operator # # The basic use is to add variables in the formula x = c("x1", "x2") xpd(y ~ .[x]) # Alternatively, one-sided formulas can be used and their content will be inserted verbatim x = ~x1 + x2 xpd(y ~ .[x]) # You can create multiple variables at once xpd(y ~ x.[1:5] + z.[2:3]) # You can summon variables from the environment to complete variables names var = "a" xpd(y ~ x.[var]) # ... the variables can be multiple vars = LETTERS[1:3] xpd(y ~ x.[vars]) # You can have "complex" variable names but they must be nested in character form xpd(y ~ .["x.[vars]_sq"]) # DSB can be used within regular expressions re = c("GNP", "Pop") xpd(Unemployed ~ regex(".[re]"), data = longley) # => equivalent to regex("GNP|Pop") # Use .[,var] (NOTE THE COMMA!) to expand with commas # !! can break the formula if missused vars = c("wage", "unemp") xpd(c(y.[,1:3]) ~ csw(.[,vars])) # Example of use of .[] within a loop res_all = list() for(p in 1:3){ res_all[[p]] = feols(Ozone ~ Wind + poly(Temp, .[p]), airquality) } etable(res_all) # The former can be compactly estimated with: res_compact = feols(Ozone ~ Wind + sw(.[, "poly(Temp, .[1:3])"]), airquality) etable(res_compact) # How does it work? # 1) .[, stuff] evaluates stuff and, if a vector, aggregates it with commas # Comma aggregation is done thanks to the comma placed after the square bracket # If .[stuff], then aggregation is with sums. # 2) stuff is evaluated, and if it is a character string, it is evaluated with # the function dsb which expands values in .[] # # Wrapping up: # 2) evaluation of dsb("poly(Temp, .[1:3])") leads to the vector: # c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)") # 1) .[, c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)")] leads to # poly(Temp, 1), poly(Temp, 2), poly(Temp, 3) # # Hence sw(.[, "poly(Temp, .[1:3])"]) becomes: # sw(poly(Temp, 1), poly(Temp, 2), poly(Temp, 3)) # # In non-fixest functions: guessing the data allows to use regex # # When used in non-fixest functions, the algorithm tries to "guess" the data # so that ..("regex") can be directly evaluated without passing the argument 'data' data(longley) lm(xpd(Armed.Forces ~ Population + ..("GNP|ployed")), longley) # same for the auto completion with '..' lm(xpd(Armed.Forces ~ Population + GN..), longley)# Small examples with airquality data data(airquality) # we set two macro variables setFixest_fml(..ctrl = ~ Temp + Day, ..ctrl_long = ~ poly(Temp, 2) + poly(Day, 2)) # Using the macro in lm with xpd: lm(xpd(Ozone ~ Wind + ..ctrl), airquality) lm(xpd(Ozone ~ Wind + ..ctrl_long), airquality) # You can use the macros without xpd() in fixest estimations a = feols(Ozone ~ Wind + ..ctrl, airquality) b = feols(Ozone ~ Wind + ..ctrl_long, airquality) etable(a, b, keep = "Int|Win") # Using .[] base = setNames(iris, c("y", "x1", "x2", "x3", "species")) i = 2:3 z = "species" lm(xpd(y ~ x.[2:3] + .[z]), base) # No xpd() needed in feols feols(y ~ x.[2:3] + .[z], base) # # Auto completion with '..' suffix # # You can trigger variables autocompletion with the '..' suffix # You need to provide the argument data base = setNames(iris, c("y", "x1", "x2", "x3", "species")) xpd(y ~ x.., data = base) # In fixest estimations, this is automatically taken care of feols(y ~ x.., data = base) # # You can use xpd for stepwise estimations # # Note that for stepwise estimations in fixest, you can use # the stepwise functions: sw, sw0, csw, csw0 # -> see help in feols or in the dedicated vignette # we want to look at the effect of x1 on y # controlling for different variables base = iris names(base) = c("y", "x1", "x2", "x3", "species") # We first create a matrix with all possible combinations of variables my_args = lapply(names(base)[-(1:2)], function(x) c("", x)) (all_combs = as.matrix(do.call("expand.grid", my_args))) res_all = list() for(i in 1:nrow(all_combs)){ res_all[[i]] = feols(xpd(y ~ x1 + ..v, ..v = all_combs[i, ]), base) } etable(res_all) coefplot(res_all, group = list(Species = "^^species")) # # You can use macros to grep variables in your data set # # Example 1: setting a macro variable globally data(longley) setFixest_fml(..many_vars = grep("GNP|ployed", names(longley), value = TRUE)) feols(Armed.Forces ~ Population + ..many_vars, longley) # Example 2: using ..("regex") or regex("regex") to grep the variables "live" feols(Armed.Forces ~ Population + ..("GNP|ployed"), longley) # Example 3: same as Ex.2 but without using a fixest estimation # Here we need to use xpd(): lm(xpd(Armed.Forces ~ Population + regex("GNP|ployed"), data = longley), longley) # Stepwise estimation with regex: use a comma after the parenthesis feols(Armed.Forces ~ Population + sw(regex(,"GNP|ployed")), longley) # Multiple LHS etable(feols(..("GNP|ployed") ~ Population, longley)) # # lhs and rhs arguments # # to create a one sided formula from a character vector vars = letters[1:5] xpd(rhs = vars) # Alternatively, to replace the RHS xpd(y ~ 1, rhs = vars) # To create a two sided formula xpd(lhs = "y", rhs = vars) # # argument 'add' # xpd(~x1, add = ~ x2 + x3) # also works with character vectors xpd(~x1, add = c("x2", "x3")) # only adds to the RHS xpd(y ~ x, add = ~bon + jour) # # Dot square bracket operator # # The basic use is to add variables in the formula x = c("x1", "x2") xpd(y ~ .[x]) # Alternatively, one-sided formulas can be used and their content will be inserted verbatim x = ~x1 + x2 xpd(y ~ .[x]) # You can create multiple variables at once xpd(y ~ x.[1:5] + z.[2:3]) # You can summon variables from the environment to complete variables names var = "a" xpd(y ~ x.[var]) # ... the variables can be multiple vars = LETTERS[1:3] xpd(y ~ x.[vars]) # You can have "complex" variable names but they must be nested in character form xpd(y ~ .["x.[vars]_sq"]) # DSB can be used within regular expressions re = c("GNP", "Pop") xpd(Unemployed ~ regex(".[re]"), data = longley) # => equivalent to regex("GNP|Pop") # Use .[,var] (NOTE THE COMMA!) to expand with commas # !! can break the formula if missused vars = c("wage", "unemp") xpd(c(y.[,1:3]) ~ csw(.[,vars])) # Example of use of .[] within a loop res_all = list() for(p in 1:3){ res_all[[p]] = feols(Ozone ~ Wind + poly(Temp, .[p]), airquality) } etable(res_all) # The former can be compactly estimated with: res_compact = feols(Ozone ~ Wind + sw(.[, "poly(Temp, .[1:3])"]), airquality) etable(res_compact) # How does it work? # 1) .[, stuff] evaluates stuff and, if a vector, aggregates it with commas # Comma aggregation is done thanks to the comma placed after the square bracket # If .[stuff], then aggregation is with sums. # 2) stuff is evaluated, and if it is a character string, it is evaluated with # the function dsb which expands values in .[] # # Wrapping up: # 2) evaluation of dsb("poly(Temp, .[1:3])") leads to the vector: # c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)") # 1) .[, c("poly(Temp, 1)", "poly(Temp, 2)", "poly(Temp, 3)")] leads to # poly(Temp, 1), poly(Temp, 2), poly(Temp, 3) # # Hence sw(.[, "poly(Temp, .[1:3])"]) becomes: # sw(poly(Temp, 1), poly(Temp, 2), poly(Temp, 3)) # # In non-fixest functions: guessing the data allows to use regex # # When used in non-fixest functions, the algorithm tries to "guess" the data # so that ..("regex") can be directly evaluated without passing the argument 'data' data(longley) lm(xpd(Armed.Forces ~ Population + ..("GNP|ployed")), longley) # same for the auto completion with '..' lm(xpd(Armed.Forces ~ Population + GN..), longley)