tbl_regression
Display regression model results in table
Description
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.
Usage
tbl_regression(x, ...) ## Default S3 method: 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. |
exponentiate |
Logical indicating whether to exponentiate the
coefficient estimates. Default is |
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 |
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 |
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.
|
tidy_fun |
Option to specify a particular tidier function if the
model. Default is to use |
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.
-
"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 tidiertidy_gam()to print both parametric and smooth terms. -
"lmerMod","glmerMod","glmmTMB","glmmadmb","stanreg","brmsfit": These mixed effects models usebroom.mixed::tidy(x, effects = "fixed"). Specifytidy_fun = broom.mixed::tidyto 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.
-
Survival regression models including time dependent covariates.
-
Random- or mixed-effects regression models with clustered data.
-
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
Author(s)
Daniel D. Sjoberg
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()
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)