broom: Broom Integration

In capybara: Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

Broom Integration

Description

The provided broom methods do the following:

1. augment: Takes the input data and adds additional columns with the fitted values and residuals.

2. glance: Extracts the deviance, null deviance, and the number of observations.'

3. tidy: Extracts the estimated coefficients and their standard errors.

Usage

## S3 method for class 'feglm'
augment(x, newdata = NULL, ...)

## S3 method for class 'felm'
augment(x, newdata = NULL, ...)

## S3 method for class 'feglm'
glance(x, ...)

## S3 method for class 'felm'
glance(x, ...)

## S3 method for class 'feglm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)

## S3 method for class 'felm'
tidy(x, conf_int = FALSE, conf_level = 0.95, ...)

Arguments

x	A fitted model object.
newdata	Optional argument to use data different from the data used to fit the model.
...	Additional arguments passed to the method.
conf_int	Logical indicating whether to include the confidence interval.
conf_level	The confidence level for the confidence interval.

Value

A tibble with the respective information for the augment, glance, and tidy methods.

Examples

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fepoisson(ltrade ~ ldist, ross2004_subset,
  control = fit_control(keep_data = TRUE)
)

broom::augment(fit)
broom::glance(fit)
broom::tidy(fit)

capybara documentation built on June 29, 2026, 5:07 p.m.