custom_tidiers
Collection of custom tidiers
Description
![[Experimental]](../help/figures/lifecycle-experimental.svg)
Collection of tidiers that can be passed to tbl_regression()
and tbl_uvregression() to obtain modified results. See examples 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 ) pool_and_tidy_mice(x, pool.args = NULL, ..., quiet = FALSE) tidy_gam(x, conf.int = FALSE, exponentiate = FALSE, conf.level = 0.95, ...)
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 |
conf.level |
The confidence level to use for the confidence interval
if |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
... |
arguments passed to method;
|
quiet |
Logical indicating whether to print messages in console. Default is
|
pool.args |
named list of arguments passed to |
Details
-
tidy_standardize()tidier to report standardized coefficients. The effectsize package includes a wonderful function to estimate standardized coefficients. The tidier uses the output fromeffectsize::standardize_parameters(), and merely takes the result and puts it inbroom::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 fromparameters::bootstrap_parameters(test = "p"), and merely takes the result and puts it inbroom::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)
tbl <- tbl_regression(mod)
if (requireNamespace("effectsize")) {
tidy_standardize_ex1 <-
tbl_merge(
list(tbl_stnd, tbl),
tab_spanner = c("**Standardized Model**", "**Original Model**")
)
}
# Example 2 ----------------------------------
# use "posthoc" method for coef calculation
if (requireNamespace("parameters")) {
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)
if (requireNamespace("mice")) {
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