Output regression table for an lm() regression in "tidy" format. This function
is a wrapper function for broom::tidy() and includes confidence
intervals in the output table by default.
Usage
get_regression_table(
model,
conf.level = 0.95,
digits = 3,
print = FALSE,
default_categorical_levels = FALSE
)Arguments
- model
an
lm()model object- conf.level
The confidence level to use for the confidence interval if
conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.- digits
number of digits precision in output table
If TRUE, return in print format suitable for R Markdown
- default_categorical_levels
If TRUE, do not change the non-baseline categorical variables in the term column. Otherwise non-baseline categorical variables will be displayed in the format "categorical_variable_name: level_name"
Value
A tibble-formatted regression table along with lower and upper end
points of all confidence intervals for all parameters lower_ci and
upper_ci; the confidence levels default to 95\
Examples
library(moderndive)
# Fit lm() regression:
mpg_model <- lm(mpg ~ cyl, data = mtcars)
# Get regression table:
get_regression_table(mpg_model)
#> # A tibble: 2 × 7
#> term estimate std_error statistic p_value lower_ci upper_ci
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 intercept 37.9 2.07 18.3 0 33.6 42.1
#> 2 cyl -2.88 0.322 -8.92 0 -3.53 -2.22
# Vary confidence level of confidence intervals
get_regression_table(mpg_model, conf.level = 0.99)
#> # A tibble: 2 × 7
#> term estimate std_error statistic p_value lower_ci upper_ci
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 intercept 37.9 2.07 18.3 0 32.2 43.6
#> 2 cyl -2.88 0.322 -8.92 0 -3.76 -1.99