custom_tidiers: Collection of custom tidiers
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

Description Usage Arguments Details Example Output Examples

\lifecycle

maturing Collection of tidiers that can be passed to tbl_regression() and tbl_uvregression() to obtain modified results. See examples below.

tidy_standardize(
  x,
  exponentiate = FALSE,
  conf.level = 0.95,
  conf.int = TRUE,
  ...,
  quiet = FALSE
)

tidy_bootstrap(
  x,
  exponentiate = FALSE,
  conf.level = 0.95,
  conf.int = TRUE,
  ...,
  quiet = FALSE
)

tidy_robust(
  x,
  exponentiate = FALSE,
  conf.level = 0.95,
  conf.int = TRUE,
  vcov_estimation = NULL,
  vcov_type = NULL,
  vcov_args = NULL,
  ...,
  quiet = FALSE
)

pool_and_tidy_mice(x, pool.args = NULL, ..., quiet = FALSE)

tidy_gam(x, conf.int = FALSE, exponentiate = FALSE, conf.level = 0.95, ...)

`x`	a regression model object
`exponentiate`	Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to `FALSE`.
`conf.level`	The confidence level to use for the confidence interval if `conf.int = TRUE`. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
`conf.int`	Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to `FALSE`.
`...`	arguments passed to method; `pool_and_tidy_mice()`: `mice::tidy(x, ...)` `tidy_standardize()`: `effectsize::standardize_parameters(x, ...)` `tidy_bootstrap()`: `parameters::bootstrap_parameters(x, ...)` `tidy_robust()`: `parameters::model_parameters(x, ...)`
`quiet`	Logical indicating whether to print messages in console. Default is `FALSE`
`vcov_estimation, vcov_type, vcov_args`	arguments passed to `parameters::model_parameters()`
`pool.args`	named list of arguments passed to `mice::pool()` in `pool_and_tidy_mice()`. Default is `NULL`

tidy_standardize() tidier to report standardized coefficients. The effectsize package includes a wonderful function to estimate standardized coefficients. The tidier uses the output from effectsize::standardize_parameters(), and merely takes the result and puts it in broom::tidy() format.
tidy_bootstrap() tidier to report bootstrapped coefficients. The parameters package includes a wonderful function to estimate bootstrapped coefficients. The tidier uses the output from parameters::bootstrap_parameters(test = "p"), and merely takes the result and puts it in broom::tidy() format.
tidy_robust() tidier to report robust standard errors, confidence intervals, and p-values. The parameters package includes a wonderful function to calculate robust standard errors, confidence intervals, and p-values The tidier uses the output from parameters::model_parameters(), and merely takes the result and puts it in broom::tidy() format. To use this function with tbl_regression(), pass a function with the arguments for tidy_robust() populated. This is easily done using purrr::partial() e.g. tbl_regression(tidy_fun = partial(tidy_robust, vcov_estimation = "CL"))
pool_and_tidy_mice() tidier to report models resulting from multiply imputed data using the mice package. Pass the mice model object before the model results have been pooled. See example.

Ensure your model type is compatible with the methods/functions used to estimate the model parameters before attempting to use the tidier with tbl_regression()

Example 1

Example 2

Example 3

# Example 1 ----------------------------------
mod <- lm(age ~ marker + grade, trial)

tbl_stnd <- tbl_regression(mod, tidy_fun = tidy_standardize)
tbl <- tbl_regression(mod)

tidy_standardize_ex1 <-
  tbl_merge(
    list(tbl_stnd, tbl),
    tab_spanner = c("**Standardized Model**", "**Original Model**")
  )

# Example 2 ----------------------------------
# use "posthoc" method for coef calculation
tidy_standardize_ex2 <-
  tbl_regression(mod, tidy_fun = purrr::partial(tidy_standardize, method = "posthoc"))

# Example 3 ----------------------------------
# Multiple Imputation using the mice package
set.seed(1123)
pool_and_tidy_mice_ex3 <-
  suppressWarnings(mice::mice(trial, m = 2)) %>%
  with(lm(age ~ marker + grade)) %>%
  tbl_regression()