Title: | Converts `lfe::felm()` To The `fixest::feols()` Equivalents |
---|---|
Description: | What the package does (one paragraph). |
Authors: | Grant McDermott [aut, cre] |
Maintainer: | Grant McDermott <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.0.1 |
Built: | 2024-09-19 03:28:37 UTC |
Source: | https://github.com/grantmcdermott/lfe2fixest |
lfe::felm()
commands into their fixest::feols()
equivalents.Takes an R script with lfe::felm()
commands, converts them
into their fixest::feols
equivalents, and then exports the resulting
script to disk. Conversion is the only thing it does. Neither the input
not output script are run.
lfe2fixest(infile = NULL, outfile = NULL, verbose = FALSE, robust = FALSE) felm2feols(infile = NULL, outfile = NULL, verbose = FALSE, robust = FALSE)
lfe2fixest(infile = NULL, outfile = NULL, verbose = FALSE, robust = FALSE) felm2feols(infile = NULL, outfile = NULL, verbose = FALSE, robust = FALSE)
infile |
An R script containing |
outfile |
File or connection to write the resulting R script (i.e. with
|
verbose |
Logical. Should the result be printed to screen. Defaults to
|
robust |
Logical. By default, iid errors will be used unless cluster
variables have been specified in the |
lfe::felm()
and fixest::feols()
provide "fixed-effects"
estimation routines for high-dimensional data. Both methods are highly
optimised, although feols()
is newer and tends to be quite a bit faster.
The syntax between these two methods is similar, if not quite offering
drop-in replacement. This function aims to automate the conversion process;
ignoring non-relevant arguments and differing options between the two,
while doing its best to ensure that the resulting scripts will produce the
same output.
Note that the conversion only handles (or attempts to handle) the actual
model calls. No attempt is made to convert downstream objects or functions
like regression table construction. Although, you will probably be okay if
you use a modern table-generating package like modelsummary
.
Other limitations include: (1) The function more or less implements a
literal translation of the relevant felm
model. It doesn't support
translation for some of the specialised syntax that feols()
offers, e.g.
multiple estimation and varying slopes. Everything should still work even
if the literal translation doesn't yield all of the additional performance
boosts and tricks that feols()
offers. (2) The function assumes that
users always provide a dataset in their model calls; i.e. regressions with
global variables are not supported. (3) Similarly, models that are
constructed programatically (e.g. with Formula()
) are not supported.
An R script.
## Not run: ## Write a (deliberately messy) lfe script lfe_string = " library(lfe) library(modelsummary) ## Our toy dataset aq = airquality names(aq) = c('y', 'x1', 'x2', 'x3', 'mnth', 'dy') ## Simple OLS (no FEs) mod1 = felm(y ~ x1 + x2, aq) ## Add a FE and cluster variable mod2 = felm(y ~ x1 + x2 | dy | 0 | mnth, aq) ## Add a second cluster variable and some estimation options mod3 = felm(y ~ x1 + x2 | dy | 0 | dy + mnth, cmethod = 'reghdfe', exactDOF = TRUE, aq) ## IV reg with weights mod4 = felm(y ~ 1 | dy | (x1 ~ x3) | mnth, weights = aq$x2, data = aq ) ## Regression table mods = list(mod1, mod2, mod3, mod4) msummary(mods, gof_omit = 'Pseudo|Within|Log|IC', output = 'markdown') " writeLines(lfe_string, 'lfe_script.R') ## Covert to fixest equivalents lfe2fixest('lfe_script.R') ## no output file provided, will print to screen lfe2fixest('lfe_script.R', 'fixest_script.R') ## write converted script to disk ## Check equivalence ## First the lfe version source('lfe_script.R', print.eval = TRUE) ## Then the fixest conversion source('fixest_script.R', print.eval = TRUE) ## Clean up file.remove(c('lfe_script.R', 'fixest_script.R')) ## End(Not run) ## Not run: ## For people that like options, there's the felm2feols() alias... felm2fixest('another_felm_script.R') ## End(Not run)
## Not run: ## Write a (deliberately messy) lfe script lfe_string = " library(lfe) library(modelsummary) ## Our toy dataset aq = airquality names(aq) = c('y', 'x1', 'x2', 'x3', 'mnth', 'dy') ## Simple OLS (no FEs) mod1 = felm(y ~ x1 + x2, aq) ## Add a FE and cluster variable mod2 = felm(y ~ x1 + x2 | dy | 0 | mnth, aq) ## Add a second cluster variable and some estimation options mod3 = felm(y ~ x1 + x2 | dy | 0 | dy + mnth, cmethod = 'reghdfe', exactDOF = TRUE, aq) ## IV reg with weights mod4 = felm(y ~ 1 | dy | (x1 ~ x3) | mnth, weights = aq$x2, data = aq ) ## Regression table mods = list(mod1, mod2, mod3, mod4) msummary(mods, gof_omit = 'Pseudo|Within|Log|IC', output = 'markdown') " writeLines(lfe_string, 'lfe_script.R') ## Covert to fixest equivalents lfe2fixest('lfe_script.R') ## no output file provided, will print to screen lfe2fixest('lfe_script.R', 'fixest_script.R') ## write converted script to disk ## Check equivalence ## First the lfe version source('lfe_script.R', print.eval = TRUE) ## Then the fixest conversion source('fixest_script.R', print.eval = TRUE) ## Clean up file.remove(c('lfe_script.R', 'fixest_script.R')) ## End(Not run) ## Not run: ## For people that like options, there's the felm2feols() alias... felm2fixest('another_felm_script.R') ## End(Not run)