R2s of fixest models
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 |
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.