R/plot.hdi.R

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

#' @export
data_plot.hdi <- function(x,
                          data = NULL,
                          show_intercept = FALSE,
                          ...) {
  .data_plot_hdi(x = x, data = data, show_intercept = show_intercept)
}

#' @export
data_plot.bayestestR_hdi <- data_plot.hdi

#' @export
data_plot.bayestestR_eti <- data_plot.hdi



#' @keywords internal
.data_plot_hdi <- function(x,
                           data = NULL,
                           parms = NULL,
                           show_intercept = FALSE,
                           ...) {
  if (is.null(data)) {
    data <- .retrieve_data(x)
  }

  if (inherits(x, "bayestestR_hdi")) {
    legend_title <- "HDI"
    plot_title <- "Highest Density Interval (HDI)"
  } else {
    legend_title <- "CI"
    plot_title <- "Credible Interval (CI)"
  }

  if (!is.null(parms)) {
    params <- parms
  } else {
    params <- NULL
  }

  if (inherits(data, "emmGrid")) {
    insight::check_if_installed("emmeans")
    data <- as.data.frame(as.matrix(emmeans::as.mcmc.emmGrid(data, names = FALSE)))
  } else if (inherits(data, c("stanreg", "brmsfit"))) {
    params <- insight::clean_parameters(data)
    data <- as.data.frame(data, optional = FALSE)
  } else if (inherits(data, "BFBayesFactor")) {
    data <- insight::get_parameters(data)
  } else if (inherits(data, "MCMCglmm")) {
    params <- insight::clean_parameters(data)
    nF <- data$Fixed$nfl
    data <- as.data.frame(data$Sol[, 1:nF, drop = FALSE])
  } else {
    data <- as.data.frame(data)
  }

  if (ncol(data) > 1L) {
    levels_order <- unique(rev(x$Parameter))
    data <- data[levels_order]
    dataplot <- data.frame()
    for (i in names(data)) {
      if (!is.null(params)) {
        dataplot <- rbind(
          dataplot,
          cbind(
            .compute_densities_hdi(data[[i]], hdi = as.data.frame(x[x$Parameter == i, ]), name = i),
            "Effects" = params$Effects[params$Parameter == i],
            "Component" = params$Component[params$Parameter == i]
          )
        )
      } else {
        dataplot <- rbind(
          dataplot,
          .compute_densities_hdi(data[[i]], hdi = as.data.frame(x[x$Parameter == i, ]), name = i)
        )
      }
    }

    if ("Effects" %in% names(dataplot) && "Component" %in% names(dataplot)) {
      if (length(unique(dataplot$Effects)) == 1 && length(unique(dataplot$Component)) == 1) {
        dataplot$Effects <- NULL
        dataplot$Component <- NULL
      } else {
        if (is.factor(dataplot$Effects)) {
          dataplot$Effects <- factor(dataplot$Effects, levels = sort(levels(dataplot$Effects)))
        } else {
          dataplot$Effects <- factor(dataplot$Effects, levels = unique(dataplot$Effects))
        }
        if (is.factor(dataplot$Component)) {
          dataplot$Component <- factor(dataplot$Component, levels = sort(levels(dataplot$Component)))
        } else {
          dataplot$Component <- factor(dataplot$Component, levels = unique(dataplot$Component))
        }
      }
    }
  } else {
    levels_order <- NULL
    dataplot <- .compute_densities_hdi(x = data[, 1], hdi = x, name = "Posterior")
  }

  cn <- intersect(c("x", "y", "height", "fill", "Effects", "Component"), colnames(dataplot))
  dataplot <- dataplot[, cn, drop = FALSE]

  if (!is.null(levels_order)) {
    dataplot$y <- factor(dataplot$y, levels = levels_order)
  }

  groups <- unique(dataplot$y)
  if (!show_intercept) {
    dataplot <- .remove_intercept(dataplot, column = "y", show_intercept)
    groups <- unique(setdiff(groups, .intercept_names))
  }

  if (length(groups) == 1) {
    ylab <- groups
    dataplot$y <- 0
  } else {
    ylab <- "Parameters"
  }

  dataplot <- .fix_facet_names(dataplot)

  attr(dataplot, "info") <- list(
    xlab = "Possible parameter values",
    ylab = ylab,
    legend_fill = legend_title,
    title = plot_title
  )

  class(dataplot) <- c("data_plot", "see_hdi", class(dataplot))
  dataplot
}




#' @keywords internal
.compute_densities_hdi <- function(x, hdi, name = "Y") {
  hdi <- hdi[order(hdi$CI, decreasing = TRUE), ]

  out <- .as.data.frame_density(stats::density(x))

  out$HDI_low <- sapply(out$x, .classify_hdi, hdi$CI_low, c(100, 100 * hdi$CI))
  out$HDI_high <- sapply(out$x, .classify_hdi, rev(hdi$CI_high), c(rev(100 * hdi$CI), 100))
  out$fill <- as.factor(pmax(out$HDI_low, out$HDI_high))
  out$height <- out$y
  out$y <- name

  levels(out$fill) <- sprintf("%s%%", levels(out$fill))

  # normalize
  out$height <- as.vector((out$height - min(out$height, na.rm = TRUE)) / diff(range(out$height, na.rm = TRUE), na.rm = TRUE))

  out
}




#' @keywords internal
.classify_hdi <- function(x, breakpoints, labels, if_lower = TRUE) {
  limits <- list(
    breakpoints = breakpoints,
    labels = labels
  )

  check <- x < limits$breakpoints
  if (TRUE %in% check) {
    index <- min(which(check))
  } else {
    index <- length(limits$labels)
  }
  out <- limits$labels[index]
  out
}





# Plot --------------------------------------------------------------------

#' Plot method for uncertainty or credible intervals
#'
#' The `plot()` method for the `bayestestR::hdi()` and related
#' function.
#'
#' @param show_zero Logical. If `TRUE`, will add a vertical (dotted) line
#'   at 0.
#' @param show_title Logical. If `TRUE`, will show the title of the plot.
#' @inheritParams data_plot
#' @inheritParams plot.see_bayesfactor_parameters
#' @inheritParams plot.see_parameters_model
#'
#' @return A ggplot2-object.
#'
#' @examplesIf identical(Sys.getenv("NOT_CRAN"), "true") && require("rstanarm")
#' library(rstanarm)
#' library(bayestestR)
#' set.seed(123)
#' m <- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
#' result <- bayestestR::hdi(m)
#' result
#' plot(result)
#'
#' @export
plot.see_hdi <- function(x,
                         data = NULL,
                         show_intercept = FALSE,
                         show_zero = TRUE,
                         show_title = TRUE,
                         n_columns = 1,
                         ...) {
  if (!inherits(x, "data_plot")) {
    x <- data_plot(x, data = data, show_intercept = show_intercept)
  }

  # check if we have multiple panels
  if ((!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1L) &&
    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1L)) {
    n_columns <- NULL
  }

  # get labels
  labels <- .clean_parameter_names(x$y, grid = !is.null(n_columns))

  insight::check_if_installed("ggridges")

  p <- ggplot(
    as.data.frame(x),
    aes(
      x = .data$x,
      y = .data$y,
      height = .data$height,
      group = .data$y,
      fill = .data$fill
    )
  ) +
    ggridges::geom_ridgeline_gradient() +
    add_plot_attributes(x)

  # Show
  if (show_zero) {
    p <- p + geom_vline(xintercept = 0, linetype = "dotted")
  }

  if (!show_title) {
    p <- p + ggtitle("")
  }

  if (length(unique(x$y)) == 1 && is.numeric(x$y)) {
    p <- p + scale_y_continuous(breaks = NULL, labels = NULL)
  } else {
    p <- p + scale_y_discrete(labels = labels)
  }


  if (!is.null(n_columns)) {
    if ("Component" %in% names(x) && "Effects" %in% names(x)) {
      p <- p + facet_wrap(~ Effects + Component, scales = "free", ncol = n_columns)
    } else if ("Effects" %in% names(x)) {
      p <- p + facet_wrap(~Effects, scales = "free", ncol = n_columns)
    } else if ("Component" %in% names(x)) {
      p <- p + facet_wrap(~Component, scales = "free", ncol = n_columns)
    }
  }

  p + scale_fill_flat(reverse = TRUE)
}


#' @export
plot.see_eti <- plot.see_hdi