[Maturing] Collection of tidiers that can be utilized in gtsummary. See details 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, ...)

tidy_wald_test(x, tidy_fun = NULL, ...)

Arguments

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_fun

Option to specify a particular tidier function for 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.

Regression Model Tidiers

These tidiers are passed to tbl_regression() and tbl_uvregression() to obtain modified results.

  • 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.

Other Tidiers

  • tidy_wald_test() tidier to report Wald p-values, wrapping the aod::wald.test() function. Use this tidier with add_global_p(anova_fun = tidy_wald_test)

Example Output

Example 1

Example 2

Example 3

Examples

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

tbl_stnd <- tbl_regression(mod, tidy_fun = tidy_standardize)
#> tidy_standardize(): Estimating standardized coefs with
#> `effectsize::standardize_parameters(model = x, ci = 0.95)`
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"))
#> tidy_standardize(): Estimating standardized coefs with
#> `effectsize::standardize_parameters(model = x, ci = 0.95, 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()
#> 
#>  iter imp variable
#>   1   1  age  marker  response
#>   1   2  age  marker  response
#>   2   1  age  marker  response
#>   2   2  age  marker  response
#>   3   1  age  marker  response
#>   3   2  age  marker  response
#>   4   1  age  marker  response
#>   4   2  age  marker  response
#>   5   1  age  marker  response
#>   5   2  age  marker  response
#> pool_and_tidy_mice(): Tidying mice model with
#> `mice::pool(x) %>% mice::tidy(exponentiate = FALSE, conf.int = TRUE, conf.level = 0.95)`
# }