R/cor_test.R

In correlation: Methods for Correlation Analysis

#' Correlation test
#'
#' This function performs a correlation test between two variables.
#' You can easily visualize the result using [`plot()`][visualisation_recipe.easycormatrix()] (see examples [**here**](https://easystats.github.io/correlation/reference/visualisation_recipe.easycormatrix.html#ref-examples)).
#'
#' @param data A data frame.
#' @param x,y Names of two variables present in the data.
#' @param ci Confidence/Credible Interval level. If `"default"`, then it is
#'   set to `0.95` (`95%` CI).
#' @param method A character string indicating which correlation coefficient is
#'   to be used for the test. One of `"pearson"` (default),
#'   `"kendall"`, `"spearman"` (but see also the `robust` argument), `"biserial"`,
#'   `"polychoric"`, `"tetrachoric"`, `"biweight"`,
#'   `"distance"`, `"percentage"` (for percentage bend correlation),
#'   `"blomqvist"` (for Blomqvist's coefficient), `"hoeffding"` (for
#'   Hoeffding's D), `"gamma"`, `"gaussian"` (for Gaussian Rank
#'   correlation) or `"shepherd"` (for Shepherd's Pi correlation). Setting
#'   `"auto"` will attempt at selecting the most relevant method
#'   (polychoric when ordinal factors involved, tetrachoric when dichotomous
#'   factors involved, point-biserial if one dichotomous and one continuous and
#'   pearson otherwise). See below the **details** section for a description of
#'   these indices.
#' @param bayesian If `TRUE`, will run the correlations under a Bayesian
#'   framework.
#' @param partial_bayesian If partial correlations under a Bayesian framework
#'   are needed, you will also need to set `partial_bayesian` to `TRUE` to
#'   obtain "full" Bayesian partial correlations. Otherwise, you will obtain
#'   pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on
#'   frequentist partialization).
#' @param include_factors If `TRUE`, the factors are kept and eventually
#'   converted to numeric or used as random effects (depending of
#'   `multilevel`). If `FALSE`, factors are removed upfront.
#' @param partial Can be `TRUE` or `"semi"` for partial and
#'   semi-partial correlations, respectively.
#' @inheritParams datawizard::adjust
#' @param bayesian_prior For the prior argument, several named values are
#'   recognized: `"medium.narrow"`, `"medium"`, `"wide"`, and
#'   `"ultrawide"`. These correspond to scale values of `1/sqrt(27)`,
#'   `1/3`, `1/sqrt(3)` and `1`, respectively. See the
#'   `BayesFactor::correlationBF` function.
#' @param bayesian_ci_method,bayesian_test See arguments in
#'   [`model_parameters()`][parameters] for `BayesFactor` tests.
#' @param ranktransform If `TRUE`, will rank-transform the variables prior to
#'   estimating the correlation, which is one way of making the analysis more
#'   resistant to extreme values (outliers). Note that, for instance, a Pearson's
#'   correlation on rank-transformed data is equivalent to a Spearman's rank
#'   correlation. Thus, using `robust=TRUE` and `method="spearman"` is
#'   redundant. Nonetheless, it is an easy option to increase the robustness of the
#'   correlation as well as flexible way to obtain Bayesian or multilevel
#'   Spearman-like rank correlations.
#' @param winsorize Another way of making the correlation more "robust" (i.e.,
#'   limiting the impact of extreme values). Can be either `FALSE` or a
#'   number between 0 and 1 (e.g., `0.2`) that corresponds to the desired
#'   threshold. See the [`winsorize()`][winsorize] function for more details.
#' @param verbose Toggle warnings.
#' @param ... Additional arguments (e.g., `alternative`) to be passed to
#'   other methods. See `stats::cor.test` for further details.
#'
#'
#' @inherit correlation details
#'
#' @examples
#' library(correlation)
#'
#' cor_test(iris, "Sepal.Length", "Sepal.Width")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "spearman")
#' \dontrun{
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "kendall")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "biweight")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "distance")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "percentage")
#'
#' if (require("wdm", quietly = TRUE)) {
#'   cor_test(iris, "Sepal.Length", "Sepal.Width", method = "blomqvist")
#' }
#'
#' if (require("Hmisc", quietly = TRUE)) {
#'   cor_test(iris, "Sepal.Length", "Sepal.Width", method = "hoeffding")
#' }
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "gamma")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "gaussian")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "shepherd")
#' if (require("BayesFactor", quietly = TRUE)) {
#'   cor_test(iris, "Sepal.Length", "Sepal.Width", bayesian = TRUE)
#' }
#'
#' # Robust (these two are equivalent)
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "spearman")
#' cor_test(iris, "Sepal.Length", "Sepal.Width", method = "pearson", ranktransform = TRUE)
#'
#' # Winsorized
#' cor_test(iris, "Sepal.Length", "Sepal.Width", winsorize = 0.2)
#'
#' # Tetrachoric
#' if (require("psych", quietly = TRUE) && require("rstanarm", quietly = TRUE)) {
#'   data <- iris
#'   data$Sepal.Width_binary <- ifelse(data$Sepal.Width > 3, 1, 0)
#'   data$Petal.Width_binary <- ifelse(data$Petal.Width > 1.2, 1, 0)
#'   cor_test(data, "Sepal.Width_binary", "Petal.Width_binary", method = "tetrachoric")
#'
#'   # Biserial
#'   cor_test(data, "Sepal.Width", "Petal.Width_binary", method = "biserial")
#'
#'   # Polychoric
#'   data$Petal.Width_ordinal <- as.factor(round(data$Petal.Width))
#'   data$Sepal.Length_ordinal <- as.factor(round(data$Sepal.Length))
#'   cor_test(data, "Petal.Width_ordinal", "Sepal.Length_ordinal", method = "polychoric")
#'
#'   # When one variable is continuous, will run 'polyserial' correlation
#'   cor_test(data, "Sepal.Width", "Sepal.Length_ordinal", method = "polychoric")
#' }
#'
#' # Partial
#' cor_test(iris, "Sepal.Length", "Sepal.Width", partial = TRUE)
#' cor_test(iris, "Sepal.Length", "Sepal.Width", multilevel = TRUE)
#' cor_test(iris, "Sepal.Length", "Sepal.Width", partial_bayesian = TRUE)
#' }
#' @export
cor_test <- function(data,
                     x,
                     y,
                     method = "pearson",
                     ci = 0.95,
                     bayesian = FALSE,
                     bayesian_prior = "medium",
                     bayesian_ci_method = "hdi",
                     bayesian_test = c("pd", "rope", "bf"),
                     include_factors = FALSE,
                     partial = FALSE,
                     partial_bayesian = FALSE,
                     multilevel = FALSE,
                     ranktransform = FALSE,
                     winsorize = FALSE,
                     verbose = TRUE,
                     ...) {
  # valid matrix checks
  if (!all(x %in% names(data)) || !all(y %in% names(data))) {
    insight::format_error("The names you entered for x and y are not available in the dataset. Make sure there are no typos!")
  }

  if (ci == "default") ci <- 0.95
  if (!partial && (partial_bayesian || multilevel)) partial <- TRUE

  # Make sure factor is no factor
  if (!method %in% c("tetra", "tetrachoric", "poly", "polychoric")) {
    data[c(x, y)] <- datawizard::to_numeric(data[c(x, y)], dummy_factors = FALSE)
  }

  # Partial
  if (!isFALSE(partial)) {
    # partial
    if (isTRUE(partial)) {
      data[[x]] <- datawizard::adjust(data[names(data) != y], multilevel = multilevel, bayesian = partial_bayesian)[[x]]
      data[[y]] <- datawizard::adjust(data[names(data) != x], multilevel = multilevel, bayesian = partial_bayesian)[[y]]
    }

    # semi-partial
    if (partial == "semi") {
      insight::format_error("Semi-partial correlations are not supported yet. Get in touch if you want to contribute.")
    }
  }

  # Winsorize
  if (!isFALSE(winsorize) && !is.null(winsorize)) {
    # set default (if not specified)
    if (isTRUE(winsorize)) {
      winsorize <- 0.2
    }

    # winsorization would otherwise fail in case of NAs present
    data <- as.data.frame(
      datawizard::winsorize(stats::na.omit(data[c(x, y)]),
        threshold = winsorize,
        verbose = verbose
      )
    )
  }

  # Rank transform (i.e., "robust")
  if (ranktransform) {
    data[c(x, y)] <- datawizard::ranktransform(data[c(x, y)], sign = FALSE, method = "average")
  }

  # check if enough no. of obs ------------------------------

  # this is a trick in case the number of valid observations is lower than 3
  n_obs <- length(.complete_variable_x(data, x, y))
  invalid <- FALSE
  if (n_obs < 3L) {
    if (isTRUE(verbose)) {
      insight::format_warning(paste(x, "and", y, "have less than 3 complete observations. Returning NA."))
    }
    invalid <- TRUE
    original_info <- list(data = data, x = x, y = y)
    data <- datasets::mtcars # Basically use a working dataset so the correlation doesn't fail
    x <- "mpg"
    y <- "disp"
  }


  # Find method
  method <- tolower(method)
  if (method == "auto" && !bayesian) method <- .find_correlationtype(data, x, y)
  if (method == "auto" && bayesian) method <- "pearson"

  # Frequentist
  if (!bayesian) {
    if (method %in% c("tetra", "tetrachoric")) {
      out <- .cor_test_tetrachoric(data, x, y, ci = ci, ...)
    } else if (method %in% c("poly", "polychoric")) {
      out <- .cor_test_polychoric(data, x, y, ci = ci, ...)
    } else if (method %in% c("biserial", "pointbiserial", "point-biserial")) {
      out <- .cor_test_biserial(data, x, y, ci = ci, method = method, ...)
    } else if (method == "biweight") {
      out <- .cor_test_biweight(data, x, y, ci = ci, ...)
    } else if (method == "distance") {
      out <- .cor_test_distance(data, x, y, ci = ci, ...)
    } else if (method %in% c("percentage", "percentage_bend", "percentagebend", "pb")) {
      out <- .cor_test_percentage(data, x, y, ci = ci, ...)
    } else if (method %in% c("blomqvist", "median", "medial")) {
      out <- .cor_test_blomqvist(data, x, y, ci = ci, ...)
    } else if (method == "hoeffding") {
      out <- .cor_test_hoeffding(data, x, y, ci = ci, ...)
    } else if (method == "somers") {
      out <- .cor_test_somers(data, x, y, ci = ci, ...)
    } else if (method == "gamma") {
      out <- .cor_test_gamma(data, x, y, ci = ci, ...)
    } else if (method == "gaussian") {
      out <- .cor_test_gaussian(data, x, y, ci = ci, ...)
    } else if (method %in% c("shepherd", "sheperd", "shepherdspi", "pi")) {
      out <- .cor_test_shepherd(data, x, y, ci = ci, bayesian = FALSE, ...)
    } else {
      out <- .cor_test_freq(data, x, y, ci = ci, method = method, ...)
    }

    # Bayesian
  } else {
    if (method %in% c("tetra", "tetrachoric")) {
      insight::format_error("Tetrachoric Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method %in% c("poly", "polychoric")) {
      insight::format_error("Polychoric Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method %in% c("biserial", "pointbiserial", "point-biserial")) {
      insight::format_error("Biserial Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method == "biweight") {
      insight::format_error("Biweight Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method == "distance") {
      insight::format_error("Bayesian distance correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method %in% c("percentage", "percentage_bend", "percentagebend", "pb")) {
      insight::format_error("Bayesian Percentage Bend correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method %in% c("blomqvist", "median", "medial")) {
      insight::format_error("Bayesian Blomqvist correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor).")
    } else if (method == "hoeffding") {
      insight::format_error("Bayesian Hoeffding's correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor).")
    } else if (method == "gamma") {
      insight::format_error("Bayesian gamma correlations are not supported yet. Get in touch if you want to contribute.")
    } else if (method %in% c("shepherd", "sheperd", "shepherdspi", "pi")) {
      out <- .cor_test_shepherd(data, x, y, ci = ci, bayesian = TRUE, ...)
    } else {
      out <- .cor_test_bayes(
        data,
        x,
        y,
        ci = ci,
        method = method,
        bayesian_prior = bayesian_prior,
        bayesian_ci_method = bayesian_ci_method,
        bayesian_test = bayesian_test,
        ...
      )
    }
  }

  # Replace by NANs if invalid
  if (isTRUE(invalid)) {
    data <- original_info$data
    out$Parameter1 <- original_info$x
    out$Parameter2 <- original_info$y
    out[!names(out) %in% c("Parameter1", "Parameter2")] <- NA
  }

  # Number of observations and CI
  out$n_Obs <- n_obs
  out$CI <- ci

  # Reorder columns
  if ("CI_low" %in% names(out)) {
    order <- c("Parameter1", "Parameter2", "r", "rho", "tau", "Dxy", "CI", "CI_low", "CI_high")
    out <- out[c(order[order %in% names(out)], setdiff(colnames(out), order[order %in% names(out)]))]
  }

  # Output
  attr(out, "coefficient_name") <- c("rho", "r", "tau", "Dxy")[c("rho", "r", "tau", "Dxy") %in% names(out)][1]
  attr(out, "ci") <- ci
  attr(out, "data") <- data
  class(out) <- unique(c("easycor_test", "easycorrelation", "parameters_model", class(out)))
  out
}





# Utilities ---------------------------------------------------------------



#' @keywords internal
.complete_variable_x <- function(data, x, y) {
  data[[x]][stats::complete.cases(data[[x]], data[[y]])]
}

#' @keywords internal
.complete_variable_y <- function(data, x, y) {
  data[[y]][stats::complete.cases(data[[x]], data[[y]])]
}