gtsummary: tbl_regression_methods – R documentation

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tbl_regression_methods

Methods for tbl_regression

Description

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.

Usage

## S3 method for class 'survreg'
tbl_regression(
  x,
  tidy_fun = function(x, ...) broom::tidy(x, ...) %>% dplyr::filter(.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 'multinom'
tbl_regression(x, ...)

Arguments

`x`	Regression model object
`tidy_fun`	Option to specify a particular tidier function if 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.
`...`	arguments passed to `tbl_regression.default()`

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 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.

gtsummary

Presentation-Ready Data Summary and Analytic Result Tables

v1.4.0

MIT + file LICENSE

Authors

Daniel D. Sjoberg [aut, cre] (<https://orcid.org/0000-0003-0862-2018>), Michael Curry [aut] (<https://orcid.org/0000-0002-0261-4044>), Margie Hannum [aut] (<https://orcid.org/0000-0002-2953-0449>), Joseph Larmarange [aut] (<https://orcid.org/0000-0001-7097-700X>), Karissa Whiting [aut] (<https://orcid.org/0000-0002-4683-1868>), Emily C. Zabor [aut] (<https://orcid.org/0000-0002-1402-4498>), Esther Drill [ctb] (<https://orcid.org/0000-0002-3315-4538>), Jessica Flynn [ctb] (<https://orcid.org/0000-0001-8310-6684>), Jessica Lavery [ctb] (<https://orcid.org/0000-0002-2746-5647>), Stephanie Lobaugh [ctb], Gustavo Zapata Wainberg [ctb] (<https://orcid.org/0000-0002-2524-3637>)

Initial release