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Benchmarking Firth's estimators
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# install crdata package to get weisiger2014 data set | |
remotes::install_github("carlislerainey/crdata") | |
# load packages | |
library(tidyverse) | |
library(brglm2) | |
library(logistf) | |
library(microbenchmark) | |
# load data | |
weis <- crdata::weisiger2014 | |
# rescale weisiger2014 explanatory variables using arm::rescale() | |
rs_weis <- weis %>% | |
mutate(across(polity_conq:coord, arm::rescale)) | |
# create functions to fit models | |
f <- resist ~ polity_conq + lndist + terrain + soldperterr + gdppc2 + coord | |
f1 <- function() { | |
glm(f, data = rs_weis, family = "binomial") | |
} | |
f2 <- function() { | |
glm(f, data = rs_weis, family = "binomial", method = brglmFit) | |
} | |
f3 <- function() { | |
logistf(f, data = rs_weis) | |
} | |
f4 <- function() { | |
logistf(f, data = rs_weis, pl = FALSE) | |
} | |
# results don't exactly match (different convergence criteria?) | |
cbind(coef(f2()), coef(f4())) | |
# do benchmarking | |
bm <- microbenchmark("regular glm()" = f1(), | |
"brglm2" = f2(), | |
"logistf (default)" = f3(), | |
"logistf (w/ pl = FALSE)" = f4(), | |
times = 100) | |
print(bm) | |
# plot times | |
bm %>% | |
group_by(expr) %>% | |
summarize(avg_time = mean(time)*10e-5) %>% # convert to milliseconds | |
ggplot(aes(x = fct_rev(expr), y = avg_time)) + | |
geom_col() + | |
labs(x = "Method", | |
y = "Avg. Time (in milliseconds)") + | |
coord_flip() |
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