Reports different R2s for fixest estimations (e.g. feglm or feols).

## Usage

r2(x, type = "all", full_names = FALSE)

## Arguments

x

A fixest object, e.g. obtained with function feglm or feols.

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 type = "r2", the within adjusted R2: use type = "war2", the pseudo R2: use type = "pr2", etc. Use "cor2" for the squared correlation. By default, all R2s are computed.

full_names

Logical scalar, default is FALSE. If TRUE then names of the vector in output will have full names instead of keywords (e.g. Squared Correlation instead of cor2, etc).

## Value

Returns a named vector.

## Details

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.

Laurent Berge

## Examples



# 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")
#>      cor2
#> 0.7040186

# "regular" r2:
r2(est, "r2")
#>        r2
#> 0.7040186

# pseudo r2 (equivalent to GLM with Gaussian family)
r2(est, "pr2")
#>       pr2
#> 0.2353827

r2(est, "war2")
#>      war2
#> 0.2182526

# all four at once
r2(est, c("cor2", "r2", "pr2", "war2"))
#>      cor2        r2       pr2      war2
#> 0.7040186 0.7040186 0.2353827 0.2182526

# same with full names instead of codes
r2(est, c("cor2", "r2", "pr2", "war2"), full_names = TRUE)
#> Squared Correlation                  R2           Pseudo R2  Adjusted Within R2
#>           0.7040186           0.7040186           0.2353827           0.2182526