The provided broom methods do the following:
augment: Takes the input data and adds additional columns with the
fitted values and residuals.
glance: Extracts the deviance, null deviance, and the number of
observations.`
tidy: Extracts the estimated coefficients and their standard errors.
# S3 method for feglm
augment(x, newdata = NULL, ...)# S3 method for felm
augment(x, newdata = NULL, ...)
# S3 method for feglm
glance(x, ...)
# S3 method for felm
glance(x, ...)
# S3 method for feglm
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)
# S3 method for felm
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)
A tibble with the respective information for the augment, glance,
and tidy methods.
A fitted model object.
Optional argument to use data different from the data used to fit the model.
Additional arguments passed to the method.
Logical indicating whether to include the confidence interval.
The confidence level for the confidence interval.
set.seed(123)
trade_2006 <- trade_panel[trade_panel$year == 2006, ]
trade_2006 <- trade_2006[sample(nrow(trade_2006), 500), ]
mod <- fepoisson(
trade ~ log_dist + lang + cntg + clny | exp_year + imp_year,
trade_2006
)
broom::augment(mod)
broom::glance(mod)
broom::tidy(mod)
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