R/one-sample-test.R

In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

#' @title One-sample tests
#' @name one_sample_test
#'
#' @param x A numeric variable from the data frame `data`.
#' @param test.value A number indicating the true value of the mean (Default:
#'   `0`).
#' @param effsize.type Type of effect size needed for *parametric* tests. The
#'   argument can be `"d"` (for Cohen's *d*) or `"g"` (for Hedge's *g*).
#' @inheritParams long_to_wide_converter
#' @inheritParams stats_type_switch
#' @inheritParams add_expression_col
#' @inheritParams two_sample_test
#' @inheritParams oneway_anova
#' @inheritParams stats::t.test
#' @inheritParams insight::format_value
#'
#' @description
#' Parametric, non-parametric, robust, and Bayesian one-sample tests.
#'
#' @section One-sample tests:
#'
#' ```{r child="man/rmd-fragments/table_intro.Rmd"}
#' ```
#'
#' ```{r child="man/rmd-fragments/one_sample_test.Rmd"}
#' ```
#'
#' @returns
#'
#' ```{r child="man/rmd-fragments/return.Rmd"}
#' ```
#'
#' @autoglobal
#'
#' @example man/examples/examples-one-sample-test.R
#' @export
one_sample_test <- function(
    data,
    x,
    type = "parametric",
    test.value = 0,
    alternative = "two.sided",
    digits = 2L,
    conf.level = 0.95,
    tr = 0.2,
    bf.prior = 0.707,
    effsize.type = "g",
    ...) {
  type <- stats_type_switch(type)
  x_vec <- stats::na.omit(pull(data, {{ x }}))

  # parametric ---------------------------------------

  if (type == "parametric") {
    .f <- stats::t.test
    # styler: off
    if (effsize.type %in% c("unbiased", "g")) .f.es <- effectsize::hedges_g
    if (effsize.type %in% c("biased", "d"))   .f.es <- effectsize::cohens_d
    # styler: on
  }

  # non-parametric ---------------------------------------

  if (type == "nonparametric") c(.f, .f.es) %<-% c(stats::wilcox.test, effectsize::rank_biserial)

  if (type %in% c("parametric", "nonparametric")) {
    stats_df <- exec(.f, x = x_vec, mu = test.value, alternative = alternative, exact = FALSE) %>%
      tidy_model_parameters() %>%
      select(-matches("^est|^conf|^diff|^term|^ci"))

    ez_df <- exec(
      .f.es,
      x       = x_vec,
      mu      = test.value,
      verbose = FALSE,
      ci      = conf.level
    ) %>%
      tidy_model_effectsize()

    stats_df <- bind_cols(stats_df, ez_df)
  }

  # robust ---------------------------------------

  if (type == "robust") {
    stats_df <- exec(WRS2::trimcibt, x = x_vec, nv = test.value, tr = tr, alpha = 1.0 - conf.level) %>%
      tidy_model_parameters()
  }

  # Bayesian ---------------------------------------

  if (type == "bayes") {
    stats_df <- BayesFactor::ttestBF(x = x_vec, rscale = bf.prior, mu = test.value) %>%
      tidy_model_parameters(ci = conf.level)
  }

  # expression ---------------------------------------

  add_expression_col(stats_df, n = length(x_vec), digits = digits)
}