reg_log_sort_by_outcomes: reg_log_sort_by_outcomes

In janbrederecke/analyzeD: Functions that help to easily analyze data

reg_log_sort_by_outcomes

Description

This function calls the actual reg_log_predictors function that shuffles through the provided predictors.

Usage

reg_log_sort_by_outcomes(
  .data,
  .outcomes,
  .predictors,
  .covariates,
  .annotation,
  .cpus,
  .std_prd,
  .std_cov,
  .summary,
  .interaction,
  .firth,
  .imputed_predictors,
  .imputed_outcomes,
  ...
)

Arguments

.data	A data.frame or .mids object.
.outcomes	A character vector containing the outcomes.
.predictors	A character vector containing the predictors.
.covariates	A character vector containing covariates.
.annotation	A matrix or data.frame of format (name, pname, unit, short_pname, comment) that contains pretty names for the used variables.
.cpus	Input number of desired cpus to use. Useful only in case of big datasets and multiple outcomes/predictors.
.std_prd	If TRUE, predictors are standardized.
.std_cov	Character vector of covariates that should be standardized.
.summary	If TRUE, an additional summary of all analyses is returned.
.interaction	Can be used to specify interactions using a list of character vectors containing the interaction variables, e.g. list(c("variable1", "variable2"), c("variable2", "variable3")).
.firth	If TRUE, a Firth-corrected version of glm in brglm() is called.
.imputed_predictors	If TRUE, cases with imputed predictors are used.
.imputed_outcomes	If TRUE, cases with imputed outcomes are used.
...	Optional input passed directly to the regression function.

janbrederecke/analyzeD documentation built on Jan. 2, 2023, 5:41 a.m.