Most regression models are handled by tbl_regression.default() , which uses broom::tidy() to perform initial tidying of results. There are, however, some model types that have modified default printing behavior. Those methods are listed below.

# S3 method for model_fit
tbl_regression(x, ...)
# S3 method for workflow
tbl_regression(x, ...)
# S3 method for survreg
tbl_regression(
  tidy_fun = function(x, ...) broom::tidy(x, ...) %>% dplyr::filter(.data$term !=
    "Log(scale)"),
# S3 method for mira
tbl_regression(x, tidy_fun = pool_and_tidy_mice, ...)
# S3 method for mipo
tbl_regression(x, ...)
# S3 method for lmerMod
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for glmerMod
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for glmmTMB
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for glmmadmb
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for stanreg
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for brmsfit
tbl_regression(
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
# S3 method for gam
tbl_regression(x, tidy_fun = tidy_gam, ...)
# S3 method for tidycrr
tbl_regression(x, tidy_fun = tidycmprsk::tidy, ...)
# S3 method for crr
tbl_regression(x, ...)
# S3 method for multinom
tbl_regression(x, ...)

Arguments

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.

• "tidycrr" : Uses the tidier tidycmprsk::tidy() to print the model 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.