R/grouped_glm.R

In IndrajeetPatil/groupedstats: Grouped Statistical Analyses in a Tidy Way

#' @title Function to run generalized linear model (`glm`) across multiple
#'   grouping variables.
#' @name grouped_glm
#' @return A tibble dataframe with tidy results from linear model.
#'
#' @inheritParams grouped_lm
#'
#' @importFrom broomExtra grouped_tidy grouped_glance grouped_augment
#' @importFrom rlang !! enquos
#'
#' @seealso \code{\link{grouped_lm}}, \code{\link{grouped_lmer}},
#' \code{\link{grouped_glmer}}
#'
#' @examples
#' groupedstats::grouped_glm(
#'   data = mtcars,
#'   formula = am ~ wt,
#'   grouping.vars = cyl,
#'   family = stats::binomial(link = "logit")
#' )
#' @export

grouped_glm <- function(data,
                        grouping.vars,
                        ...,
                        output = "tidy",
                        tidy.args = list(conf.int = TRUE, conf.level = 0.95),
                        augment.args = list()) {
  if (output == "tidy") {
    # tidy results
    combined_df <-
      broomExtra::grouped_tidy(
        data = data,
        grouping.vars = {{ grouping.vars }},
        ..f = stats::glm,
        ...,
        tidy.args = tidy.args
      )

    # add a column with significance labels if p-values are present
    if ("p.value" %in% names(combined_df)) {
      combined_df %<>% signif_column(data = ., p = p.value)
    }
  }

  if (output == "glance") {
    # tidy results
    combined_df <-
      broomExtra::grouped_glance(
        data = data,
        grouping.vars = {{ grouping.vars }},
        ..f = stats::glm,
        ...
      )
  }

  if (output == "augment") {
    # tidy results
    combined_df <-
      broomExtra::grouped_augment(
        data = data,
        grouping.vars = {{ grouping.vars }},
        ..f = stats::glm,
        ...,
        augment.args = augment.args
      )
  }

  return(combined_df)
}