Experimental lifecycle 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
)

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

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;

quiet

Logical indicating whether to print messages in console. Default is FALSE

pool.args

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

Details

  • 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.

  • 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 Output

Example 1

Example 2

Example 3

Examples

# 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 pool_and_tidy_mice_ex3 <- suppressWarnings(mice::mice(trial, m = 2)) %>% with(lm(age ~ marker + grade)) %>% tbl_regression() # mice method called that uses `pool_and_tidy_mice()` as tidier
#> #> 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)`
#> There was an error calling `stats::model.frame(x)`, and the #> model N will not be available in the output.