Custom tidiers

Collection of tidiers that can be utilized in gtsummary. See details below.

Usage

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 = 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, vcov = stats::vcov(x), ...)

Arguments

x

(model)
Regression model object

exponentiate

(scalar logical)
Logical indicating whether to exponentiate the coefficient estimates. Default is FALSE.

conf.level

(scalar real)
Confidence level for confidence interval/credible interval. Defaults to 0.95.

conf.int

(scalar logical)
Logical indicating whether or not to include a confidence interval in the output. Default is TRUE.

...

Arguments passed to method;

• pool_and_tidy_mice(): mice::tidy(x, ...)

• tidy_standardize(): parameters::standardize_parameters(x, ...)

• tidy_bootstrap(): parameters::bootstrap_parameters(x, ...)

• tidy_robust(): parameters::model_parameters(x, ...)

quiet

vcov, vcov_args

• tidy_robust(): Arguments passed to parameters::model_parameters(). At least one of these arguments must be specified.

• tidy_wald_test(): vcov is the covariance matrix of the model with default stats::vcov().

pool.args

(named list)
Named list of arguments passed to mice::pool() in pool_and_tidy_mice(). Default is NULL

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.

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 parameters package includes a wonderful function to estimate standardized coefficients. The tidier uses the output from parameters::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.

• 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)

Examples

# 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
tbl_regression(mod, tidy_fun = \(x, ...) tidy_standardize(x, method = "posthoc", ...))


  
Characteristic
      
Beta
      
95% CI
    
Marker Level (ng/mL)
0.00
-0.15, 0.15
Grade



    I
—
—
    II
0.04
-0.32, 0.41
    III
0.17
-0.20, 0.53
Abbreviation: CI = Confidence Interval
    

# 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