custom_tidiers {gtsummary} | R Documentation |
Collection of custom tidiers
Description
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, ...)
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
|
vcov , vcov_args |
arguments passed to
|
pool.args |
named list of arguments passed to |
tidy_fun |
Option to specify a particular tidier function for the
model. Default is to use |
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 fromparameters::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. -
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 fromparameters::model_parameters()
, and merely takes the result and puts it inbroom::tidy()
format. To use this function withtbl_regression()
, pass a function with the arguments fortidy_robust()
populated. This is easily done usingpurrr::partial()
e.g.tbl_regression(tidy_fun = partial(tidy_robust, vcov = "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 theaod::wald.test()
function. Use this tidier withadd_global_p(anova_fun = tidy_wald_test)
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)
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()