R/plot.p_function.R

In see: Model Visualisation Toolbox for 'easystats' and 'ggplot2'

#' Plot method for plotting p-functions (aka consonance functions)
#'
#' The `plot()` method for the `parameters::p_function ()`.
#'
#' @param x An object returned by `parameters::p_function ()`.
#' @param colors Character vector of length two, indicating the colors (in
#' hex-format) used when only one parameter is plotted, resp. when panels
#' are plotted as facets.
#' @param line_alpha Numeric value specifying alpha of lines indicating the
#' emphasized compatibility interval levels (see `?parameters::p_function`).
#'
#' @inheritParams data_plot
#' @inheritParams plot.see_parameters_model
#' @inheritParams plot.see_bayesfactor_parameters
#' @inheritParams plot.see_check_normality
#' @inheritParams plot.see_check_outliers
#'
#' @return A ggplot2-object.
#'
#' @examples
#' library(parameters)
#' model <- lm(Sepal.Length ~ Species + Sepal.Width + Petal.Length, data = iris)
#' result <- p_function(model)
#' plot(result, n_columns = 2, show_labels = FALSE)
#'
#' result <- p_function(model, keep = "Sepal.Width")
#' plot(result)
#' @export
plot.see_p_function <- function(x,
                                colors = c("black", "#1b6ca8"),
                                size_point = 1.2,
                                size_line = c(0.7, 0.9),
                                size_text = 3,
                                line_alpha = 0.15,
                                show_labels = TRUE,
                                n_columns = NULL,
                                show_intercept = FALSE,
                                ...) {
  # data for ribbons
  data_ribbon <- attr(x, "data")

  # data for vertical CI level lines
  data_ci_segments <- x

  # remove intercept?
  data_ribbon <- .remove_intercept(data_ribbon, show_intercept = show_intercept)
  data_ci_segments <- .remove_intercept(data_ci_segments, show_intercept = show_intercept)

  pretty_names <- attributes(x)$pretty_names
  for (pn in seq_along(pretty_names)) {
    data_ribbon$Parameter[data_ribbon$Parameter == names(pretty_names[pn])] <- pretty_names[pn]
    data_ci_segments$Parameter[data_ci_segments$Parameter == names(pretty_names[pn])] <- pretty_names[pn]
  }

  # make sure group is factor
  data_ci_segments$group <- as.factor(data_ci_segments$group)

  # setup - no color/fill aes for ribbons when we have no facets
  if (!is.null(n_columns) || insight::n_unique(data_ribbon$Parameter) == 1) {
    p <- ggplot2::ggplot() +
      ggplot2::geom_ribbon(
        data = data_ribbon,
        mapping = ggplot2::aes(
          x = .data$x,
          ymin = 0,
          ymax = 1 - .data$CI,
          group = .data$Parameter
        ),
        fill = colors[2],
        colour = colors[2],
        alpha = 0.2
      )
  } else {
    p <- ggplot2::ggplot() +
      ggplot2::geom_ribbon(
        data = data_ribbon,
        mapping = ggplot2::aes(
          x = .data$x,
          ymin = 0,
          ymax = 1 - .data$CI,
          fill = .data$Parameter,
          colour = .data$Parameter,
          group = .data$Parameter
        ),
        alpha = 0.2
      )
  }

  p <- p +
    # points for vertical CI bars
    ggplot2::geom_point(
      data = data_ci_segments,
      mapping = ggplot2::aes(
        x = .data$CI_low,
        y = 1 - .data$CI,
        group = .data$Parameter
      ),
      colour = colors[1],
      size = size_point,
      alpha = line_alpha,
      show.legend = FALSE
    ) +
    # points for vertical CI bars
    ggplot2::geom_point(
      data = data_ci_segments,
      mapping = ggplot2::aes(
        x = .data$CI_high,
        y = 1 - .data$CI,
        group = .data$Parameter
      ),
      colour = colors[1],
      size = size_point,
      alpha = line_alpha,
      show.legend = FALSE
    ) +
    # lines for vertical CI bars
    ggplot2::geom_segment(
      data = data_ci_segments,
      mapping = ggplot2::aes(
        x = .data$CI_low,
        y = 1 - .data$CI,
        xend = .data$CI_high,
        yend = 1 - .data$CI,
        group = .data$Parameter,
        linewidth = .data$group
      ),
      colour = colors[1],
      alpha = line_alpha,
      show.legend = FALSE
    )

  # emphasize specific CI level
  if (show_labels) {
    p <- p +
      ggplot2::geom_label(
        data = data_ci_segments,
        mapping = ggplot2::aes(
          x = (.data$CI_low + .data$CI_high) / 2,
          y = 1 - .data$CI,
          group = .data$Parameter,
          label = sprintf("%.2f, %.2f", .data$CI_low, .data$CI_high)
        ),
        colour = colors[1],
        size = size_text,
        show.legend = FALSE
      )
  }

  p <- p +
    # make sure we have two different y axes
    ggplot2::scale_y_continuous(
      limits = c(0, 1),
      breaks = seq(0, 1, 0.05),
      sec.axis = ggplot2::sec_axis(
        trans = ~ 1 - .,
        name = "Compatibility Interval",
        breaks = seq(0, 1, 0.05),
        labels = sprintf("%g%%", round(100 * seq(0, 1, 0.05)))
      ),
      expand = c(0, 0)
    ) +
    # labelling
    ggplot2::labs(y = expression(paste(italic("p"), "-value")), x = "Range of Estimates", colour = NULL) +
    theme_lucid() +
    ggplot2::scale_linewidth_manual(values = size_line, guide = "none")

  # facets for grids, different color/fill when no grids
  if (!is.null(n_columns)) {
    p <- p + ggplot2::facet_wrap(~ .data$Parameter, scales = "free_x", ncol = n_columns)
  } else if (insight::n_unique(data_ribbon$Parameter) > 1L) {
    p <- p +
      scale_color_flat_d(guide = "none") +
      scale_fill_flat_d()
  }

  p
}