Regression model summary

This function takes a regression model object and returns a formatted table that is publication-ready. The function is customizable allowing the user to create bespoke regression model summary tables. Review the tbl_regression() vignette for detailed examples.

Usage

tbl_regression(x, ...)

# Default S3 method
tbl_regression(
  x,
  label = NULL,
  exponentiate = FALSE,
  include = everything(),
  show_single_row = NULL,
  conf.level = 0.95,
  intercept = FALSE,
  estimate_fun = ifelse(exponentiate, label_style_ratio(), label_style_sigfig()),
  pvalue_fun = label_style_pvalue(digits = 1),
  tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
  add_estimate_to_reference_rows = FALSE,
  conf.int = TRUE,
  ...
)

Arguments

x

(regression model)
Regression model object

...

Additional arguments passed to broom.helpers::tidy_plus_plus().

label

(formula-list-selector)
Used to change variables labels, e.g. list(age = "Age", stage = "Path T Stage")

exponentiate

(scalar logical)
Logical indicating whether to exponentiate the coefficient estimates. Default is FALSE.

include

(tidy-select)
Variables to include in output. Default is everything().

show_single_row

(tidy-select)
By default categorical variables are printed on multiple rows. If a variable is dichotomous (e.g. Yes/No) and you wish to print the regression coefficient on a single row, include the variable name(s) here.

conf.level

(scalar real)
Confidence level for confidence interval/credible interval. Defaults to 0.95.

intercept

(scalar logical)
Indicates whether to include the intercept in the output. Default is FALSE

estimate_fun

(function)
Function to round and format coefficient estimates. Default is label_style_sigfig() when the coefficients are not transformed, and label_style_ratio() when the coefficients have been exponentiated.

pvalue_fun

(function)
Function to round and format p-values. Default is label_style_pvalue().

tidy_fun

(function)
Tidier function for the model. Default is to use broom::tidy(). If an error occurs, the tidying of the model is attempted with parameters::model_parameters(), if installed.

add_estimate_to_reference_rows

(scalar logical)
Add a reference value. Default is FALSE.

conf.int

(scalar logical)
Logical indicating whether or not to include a confidence interval in the output. Default is TRUE.

Value

A tbl_regression object

Methods

The default method for tbl_regression() model summary uses broom::tidy(x) to perform the initial tidying of the model object. There are, however, a few models that use modifications.

• "parsnip/workflows": If the model was prepared using parsnip/workflows, the original model fit is extracted and the original x= argument is replaced with the model fit. This will typically go unnoticed; however,if you've provided a custom tidier in tidy_fun= the tidier will be applied to the model fit object and not the parsnip/workflows object.

• "survreg": The scale parameter is removed, broom::tidy(x) %>% dplyr::filter(term != "Log(scale)")

• "multinom": This multinomial outcome is complex, with one line per covariate per outcome (less the reference group)

• "gam": Uses the internal tidier tidy_gam() to print both parametric and smooth terms.

• "lmerMod", "glmerMod", "glmmTMB", "glmmadmb", "stanreg", "brmsfit": These mixed effects models use broom.mixed::tidy(x, effects = "fixed"). Specify tidy_fun = broom.mixed::tidy to print the random components.

Author

Daniel D. Sjoberg

Examples

# Example 1 ----------------------------------
glm(response ~ age + grade, trial, family = binomial()) |>
  tbl_regression(exponentiate = TRUE)


  
Characteristic
      
OR
      
95% CI
      
p-value
    
Age
1.02
1.00, 1.04
0.10
Grade





    I
—
—

    II
0.85
0.39, 1.85
0.7
    III
1.01
0.47, 2.16
>0.9
Abbreviations: CI = Confidence Interval, OR = Odds Ratio