This function takes a regression model object and returns a formatted table that is publication-ready. The function is highly customizable allowing the user to obtain a bespoke summary table of the regression model results. Review the tbl_regression vignette for detailed examples.

tbl_regression(x, ...)

# S3 method for default
tbl_regression(
  x,
  label = NULL,
  exponentiate = FALSE,
  include = everything(),
  show_single_row = NULL,
  conf.level = NULL,
  intercept = FALSE,
  estimate_fun = NULL,
  pvalue_fun = NULL,
  tidy_fun = NULL,
  add_estimate_to_reference_rows = FALSE,
  show_yesno = NULL,
  exclude = NULL,
  ...
)

Arguments

x

Regression model object

...

Not used

label

List of formulas specifying variables labels, e.g. list(age ~ "Age", stage ~ "Path T Stage")

exponentiate

Logical indicating whether to exponentiate the coefficient estimates. Default is FALSE.

include

Variables to include in output. Input may be a vector of quoted variable names, unquoted variable names, or tidyselect select helper functions. Default is everything().

show_single_row

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--quoted and unquoted variable name accepted.

conf.level

Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

intercept

Logical argument indicating whether to include the intercept in the output. Default is FALSE

estimate_fun

Function to round and format coefficient estimates. Default is style_sigfig when the coefficients are not transformed, and style_ratio when the coefficients have been exponentiated.

pvalue_fun

Function to round and format p-values. Default is style_pvalue. The function must have a numeric vector input (the numeric, exact p-value), and return a string that is the rounded/formatted p-value (e.g. pvalue_fun = function(x) style_pvalue(x, digits = 2) or equivalently, purrr::partial(style_pvalue, digits = 2)).

tidy_fun

Option to specify a particular tidier function for the model. Default is to use broom::tidy, but if an error occurs then tidying of the model is attempted with parameters::model_parameters(), if installed.

add_estimate_to_reference_rows

add a reference value. Default is FALSE

show_yesno

DEPRECATED

exclude

DEPRECATED

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.

Note

The N reported in the output is the number of observations in the data frame model.frame(x). Depending on the model input, this N may represent different quantities. In most cases, it is the number of people or units in your model. Here are some common exceptions.

  1. Survival regression models including time dependent covariates.

  2. Random- or mixed-effects regression models with clustered data.

  3. GEE regression models with clustered data.

This list is not exhaustive, and care should be taken for each number reported.

Example Output

Example 1

Example 2

Example 3

See also

See tbl_regression vignette for detailed examples

Other tbl_regression tools: add_global_p(), add_q(), bold_italicize_labels_levels, combine_terms(), inline_text.tbl_regression(), modify, tbl_merge(), tbl_stack()

Author

Daniel D. Sjoberg

Examples

# Example 1 ---------------------------------- library(survival) tbl_regression_ex1 <- coxph(Surv(ttdeath, death) ~ age + marker, trial) %>% tbl_regression(exponentiate = TRUE) # Example 2 ---------------------------------- tbl_regression_ex2 <- glm(response ~ age + grade, trial, family = binomial(link = "logit")) %>% tbl_regression(exponentiate = TRUE) # Example 3 ---------------------------------- suppressMessages(library(lme4)) tbl_regression_ex3 <- glmer(am ~ hp + (1 | gear), mtcars, family = binomial) %>% tbl_regression(exponentiate = TRUE)