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Fixed effect counts in modelsummary

Time:01-26

I have a modelsummary of three fixed effects regressions like so:

remotes::install_github("lrberge/fixest")
remotes::install_github("vincentarelbundock/modelsummary")

library(fixest)
library(modelsummary)

mod1 <- feols(mpg ~ hp | cyl, data = mtcars)
mod2 <- feols(mpg ~ wt | cyl, data = mtcars)
mod3 <- feols(mpg ~ drat | cyl, data = mtcars)

modelsummary(list(mod1, mod2, mod3), output = "markdown")
Model 1 Model 2 Model 3
hp -0.024
(0.015)
wt -3.206
(1.188)
drat 1.793
(1.564)
Num.Obs. 32 32 32
R2 0.754 0.837 0.745
R2 Adj. 0.727 0.820 0.718
R2 Within 0.080 0.392 0.048
R2 Within Adj. 0.047 0.371 0.014
AIC 167.9 154.6 169.0
BIC 173.8 160.5 174.9
RMSE 2.94 2.39 2.99
Std.Errors by: cyl by: cyl by: cyl
FE: cyl X X X

Instead of having the table show merely whether certain fixed effects were present, is it possible to show the number of fixed effects that were estimated instead?

The raw models do contain this information:

> mod1
OLS estimation, Dep. Var.: mpg
Observations: 32 
Fixed-effects: cyl: 3
Standard-errors: Clustered (cyl) 
    Estimate Std. Error  t value Pr(>|t|) 
hp -0.024039   0.015344 -1.56664  0.25771 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
RMSE: 2.94304     Adj. R2: 0.727485
                Within R2: 0.07998 

CodePudding user response:

Yes, you’ll need to define a glance_custom.fixest() method. See this section of the docs for detailed instructions and many examples:

https://vincentarelbundock.github.io/modelsummary/articles/modelsummary.html#customizing-existing-models-part-i

And here’s an example with fixest:

library(fixest)
library(tibble)
library(modelsummary)

models <- list(
    feols(mpg ~ hp | cyl, data = mtcars),
    feols(mpg ~ hp | am, data = mtcars),
    feols(mpg ~ hp | cyl   am, data = mtcars)
)

glance_custom.fixest <- function(x, ...) {
    tibble::tibble(`# FE` = paste(x$fixef_sizes, collapse = "   "))
}

modelsummary(models, gof_map = c("nobs", "# FE"))
(1) (2) (3)
hp -0.024 -0.059 -0.044
(0.015) (0.000) (0.016)
Num.Obs. 32 32 32
# FE 3 2 3 2
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