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There appears to be a bug where even with only one clustering variable the conditional fstat changes depending on which cmethod argument is used in felm. For example:
library(lfe)
#> Loading required package: Matrix
library(tidyverse)
set.seed(6)
fake_data<-matrix(rnorm(1000*5, 0, 1),
nrow=1000,
ncol=5) %>%
as_tibble() %>%
mutate(group=factor(rep(1:5, 200)))
#> Warning: `as_tibble.matrix()` requires a matrix with column names or a `.name_repair` argument. Using compatibility `.name_repair`.#> This warning is displayed once per session.lfe_fit_cgm<- felm(formula=V1~1|group| (V2|V3~V4+V5) |group,
data=fake_data,
cmethod="cgm")
lfe_fit_cgm2<- felm(formula=V1~1|group| (V2|V3~V4+V5) |group,
data=fake_data,
cmethod="cgm2")
condfstat(lfe_fit_cgm)
#> V2 V3#> cluster F 1.945907 2.995397#> attr(,"df1")#> [1] 1
condfstat(lfe_fit_cgm2)
#> V2 V3#> cluster F 1.945907 0.0120056#> attr(,"df1")#> [1] 1
My understanding is that with only one clustering dimension cgm and cgm2 should return identical results?
The text was updated successfully, but these errors were encountered:
Hi - big fan of the package!
There appears to be a bug where even with only one clustering variable the conditional fstat changes depending on which
cmethod
argument is used infelm
. For example:My understanding is that with only one clustering dimension
cgm
andcgm2
should return identical results?The text was updated successfully, but these errors were encountered: