Methods for tbl_regression

Most regression models are handled by tbl_regression(), 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.

Usage

# S3 method for class 'model_fit'
tbl_regression(x, ...)

# S3 method for class 'workflow'
tbl_regression(x, ...)

# S3 method for class 'survreg'
tbl_regression(
  x,
  tidy_fun = function(x, ...) dplyr::filter(broom::tidy(x, ...), .data$term !=
    "Log(scale)"),
  ...
)

# S3 method for class 'mira'
tbl_regression(x, tidy_fun = pool_and_tidy_mice, ...)

# S3 method for class 'mipo'
tbl_regression(x, ...)

# S3 method for class 'lmerMod'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'glmerMod'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'glmmTMB'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'glmmadmb'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'stanreg'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'brmsfit'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
  ...
)

# S3 method for class 'gam'
tbl_regression(x, tidy_fun = tidy_gam, ...)

# S3 method for class 'crr'
tbl_regression(x, ...)

Arguments

x

(regression model)
Regression model object

...

arguments passed to tbl_regression()

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.

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.