R/plot_pred.R

In ggmice: Visualizations for 'mice' with 'ggplot2'

#' Plot the predictor matrix of an imputation model
#'
#' @param data A predictor matrix for `mice`, typically generated with [mice::make.predictorMatrix] or [mice::quickpred].
#' @param vrb String, vector, or unquoted expression with variable name(s), default is "all".
#' @param method Character string or vector with imputation methods.
#' @param label Logical indicating whether predictor matrix values should be displayed.
#' @param square Logical indicating whether the plot tiles should be squares.
#' @param rotate Logical indicating whether the variable name labels should be rotated 90 degrees.
#'
#' @return An object of class `ggplot2::ggplot`.
#'
#' @examples
#' pred <- mice::quickpred(mice::nhanes)
#' plot_pred(pred)
#' @export
plot_pred <-
  function(data,
           vrb = "all",
           method = NULL,
           label = TRUE,
           square = TRUE,
           rotate = FALSE) {
    verify_data(data, pred = TRUE)
    p <- nrow(data)
    if (!is.null(method) && is.character(method)) {
      if (length(method) == 1) {
        method <- rep(method, p)
      }
      if (length(method) == p) {
        ylabel <- "Imputation method"
      }
    }
    if (is.null(method)) {
      method <- rep("", p)
      ylabel <- ""
    }
    if (!is.character(method) || length(method) != p) {
      cli::cli_abort("Method should be NULL or a character string or vector (of length 1 or `ncol(data)`).")
    }
    vrb <- substitute(vrb)
    if (vrb[1] == "all") {
      vrb <- names(data)
    } else {
      vrb <- names(dplyr::select(as.data.frame(data), {{vrb}}))
    }
    vrbs <- row.names(data)
    long <- data.frame(
      vrb = 1:p,
      prd = rep(vrbs, each = p),
      ind = matrix(data, nrow = p * p, byrow = TRUE)
    ) %>% dplyr::mutate(clr = factor(
      .data$ind,
      levels = c(-2, 0, 1, 2, 3),
      labels = c(
        "cluster variable",
        "not used",
        "predictor",
        "random effect",
        "inclusion-restriction variable"
      ),
      ordered = TRUE
    ))

    gg <-
      ggplot2::ggplot(long,
                      ggplot2::aes(
                        x = .data$prd,
                        y = .data$vrb,
                        label = .data$ind,
                        fill = .data$clr
                      )) +
      ggplot2::geom_tile(color = "black", alpha = 0.6) +
      ggplot2::scale_x_discrete(limits = vrbs, position = "top") +
      ggplot2::scale_y_reverse(
        breaks = 1:p,
        labels = vrbs,
        sec.axis = ggplot2::dup_axis(labels = method, name = ylabel)
      ) +
      ggplot2::scale_fill_manual(
        values = c(
          "cluster variable" = "lightyellow",
          "not used" = "grey90",
          "predictor" = "palegreen3",
          "random effect" = "deepskyblue",
          "inclusion-restriction variable" = "orangered"
        )
      ) +
      ggplot2::labs(
        x = "Imputation model predictor",
        y = "Variable to impute",
        fill = "",
        color = ""
      ) +
      theme_minimice()

    if (all(method == "")) {
      gg <-
        gg + ggplot2::theme(axis.ticks.y.right = ggplot2::element_blank())
    }
    if (label) {
      gg <- gg + ggplot2::geom_text(color = "black", show.legend = FALSE)
    }
    if (square) {
      gg <- gg + ggplot2::coord_fixed(expand = FALSE)
    } else {
      gg <- gg + ggplot2::coord_cartesian(expand = FALSE)
    }
    if (rotate) {
      gg <-
        gg + ggplot2::theme(axis.text.x.top = ggplot2::element_text(angle = 90))
    }
    return(gg)
  }