R/residual_plots.R

Defines functions residual_plots

Documented in residual_plots

#' Several types of residual plots
#' @description
#' `r badge('stable')`
#'
#' Residual plots for a output model of class `performs_ammi`,
#' `waas`,  `anova_ind`, and  `anova_joint`. Seven types of plots
#' are produced: (1) Residuals vs fitted, (2) normal Q-Q plot for the residuals,
#' (3) scale-location plot (standardized residuals vs Fitted Values), (4)
#' standardized residuals vs Factor-levels, (5) Histogram of raw residuals and
#' (6) standardized residuals vs observation order, and (7) 1:1 line plot
#'
#'
#' @param x An object of class `performs_ammi`, `waas`,
#'   `anova_joint`, or  `gafem`
#' @param var The variable to plot. Defaults to `var = 1` the first
#'   variable of `x`.
#' @param conf Level of confidence interval to use in the Q-Q plot (0.95 by
#' default).
#' @param labels Logical argument. If `TRUE` labels the points outside
#' confidence interval limits.
#' @param plot_theme The graphical theme of the plot. Default is
#'   `plot_theme = theme_metan()`. For more details, see
#'   [ggplot2::theme()].
#' @param band.alpha,point.alpha The transparency of confidence band in the Q-Q
#'   plot and the points, respectively. Must be a number between 0 (opaque) and
#'   1 (full transparency).
#' @param fill.hist The color to fill the histogram. Default is 'gray'.
#' @param col.hist The color of the border of the the histogram. Default is
#' 'black'.
#' @param col.point The color of the points in the graphic. Default is 'black'.
#' @param col.line The color of the lines in the graphic. Default is 'red'.
#' @param col.lab.out The color of the labels for the 'outlying' points.
#' @param size.lab.out The size of the labels for the 'outlying' points.
#' @param size.tex.lab The size of the text in axis text and labels.
#' @param size.shape The size of the shape in the plots.
#' @param bins The number of bins to use in the histogram. Default is 30.
#' @param which Which graphics should be plotted. Default is `which =
#' c(1:4)` that means that the first four graphics will be plotted.
#' @param ncol,nrow The number of columns and rows of the plot pannel. Defaults
#'   to `NULL`
#' @param ... Additional arguments passed on to the function
#'  [patchwork::wrap_plots()].
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#' @export
#' @examples
#'\donttest{
#' library(metan)
#' model <- performs_ammi(data_ge, ENV, GEN, REP, GY)
#'
#' # Default plot
#' plot(model)
#'
#' # Normal Q-Q plot
#' # Label possible outliers
#' plot(model,
#'      which = 2,
#'      labels = TRUE)
#'
#' # Residual vs fitted,
#' # Normal Q-Q plot
#' # Histogram of raw residuals
#' # All in one row
#' plot(model,
#'      which = c(1, 2, 5),
#'      nrow = 1)
#'}
residual_plots <- function(x,
                           var = 1,
                           conf = 0.95,
                           labels = FALSE,
                           plot_theme = theme_metan(),
                           band.alpha = 0.2,
                           point.alpha = 0.8,
                           fill.hist = "gray",
                           col.hist = "black",
                           col.point = "black",
                           col.line = "red",
                           col.lab.out = "red",
                           size.lab.out = 2.5,
                           size.tex.lab = 10,
                           size.shape = 1.5,
                           bins = 30,
                           which = c(1:4),
                           ncol = NULL,
                           nrow = NULL,
                           ...) {
  if(is.numeric(var)){
    var_name <- names(x)[var]
  } else{
    var_name <- var
  }
  if(!var_name %in% names(x)){
    stop("Variable not found in ", match.call()[["x"]] , call. = FALSE)
  }
  x <- x[[var]]
  df <- x$augment %>%
    add_row_id(var = "id") %>%
    arrange(stdres)
  P <- ppoints(nrow(df))
  df$z <- qnorm(P)
  n <- nrow(df)
  Q.x <- quantile(df$stdres, c(0.25, 0.75))
  Q.z <- qnorm(c(0.25, 0.75))
  b <- diff(Q.x)/diff(Q.z)
  coef <- c(Q.x[1] - b * Q.z[1], b)
  zz <- qnorm(1 - (1 - conf)/2)
  SE <- (coef[2]/dnorm(df$z)) * sqrt(P * (1 - P)/n)
  fit.value <- coef[1] + coef[2] * df$z
  df$upper <- fit.value + zz * SE
  df$lower <- fit.value - zz * SE
  df$label <- ifelse(df$stdres > df$upper | df$stdres < df$lower, rownames(df), "")
  # residuals vs fitted
  p1 <- ggplot(df, aes(fitted, resid)) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    geom_smooth(se = F,
                method = "loess",
                formula = y ~ x,
                col = col.line) +
    geom_hline(yintercept = 0,
               linetype = 2,
               col = "gray") +
    labs(x = "Fitted values",
         y = "Residual") +
    ggtitle("Residuals vs fitted") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))
  if (labels != FALSE) {
    p1 <- p1 +
      geom_text_repel(aes(label = label),
                      size = size.lab.out,
                      col = col.lab.out)
  } else {
    p1 <- p1
  }
  # normal qq
  p2 <- ggplot(df, aes(z, stdres)) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    geom_abline(intercept = coef[1],
                slope = coef[2],
                col = col.line,
                size = 1) +
    geom_ribbon(aes_(ymin = ~lower, ymax = ~upper),
                alpha = band.alpha) +
    labs(x = "Theoretical quantiles",
         y = "Sample quantiles") +
    ggtitle("Normal Q-Q") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))
  if (labels != FALSE) {
    p2 <- p2 +
      geom_text_repel(aes(label = label),
                      size = size.lab.out,
                      col = col.lab.out)
  } else {
    p2 <- p2
  }
  # scale-location
  p3 <- ggplot(df, aes(fitted, sqrt(abs(resid)))) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    geom_smooth(se = F,
                method = "loess",
                formula = y ~ x,
                col = col.line) +
    labs(x = "Fitted values",
         y = expression(sqrt("|Standardized residuals|"))) +
    ggtitle("Scale-location") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))

  if (labels != FALSE) {
    p3 <- p3 +
      geom_text_repel(aes(label = label),
                      size = size.lab.out,
                      col = col.lab.out)
  } else {
    p3 <- p3
  }
  # Residuals vs Factor-levels
  p4 <- ggplot(df, aes(factors, stdres)) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    geom_hline(yintercept = 0, linetype = 2, col = "gray") +
    labs(x = "Factor levels", y = "Standardized residuals") +
    ggtitle("Residuals vs factor-levels") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          panel.grid.major.x = element_blank(),
          axis.text.x = element_text(color = "white"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))
  if (labels != FALSE) {
    p4 <- p4 +
      geom_text_repel(aes(label = label),
                      size = size.lab.out,
                      col = col.lab.out)
  } else {
    p4 <- p4
  }
  # Histogram of residuals
  p5 <- ggplot(df, aes(x = resid)) +
    geom_histogram(bins = bins,
                   colour = col.hist,
                   fill = fill.hist, aes(y = ..density..)) +
    stat_function(fun = dnorm,
                  color = col.line,
                  size = 1,
                  args = list(mean = mean(df$resid), sd = sd(df$resid))) +
    labs(x = "Raw residuals", y = "Density") +
    scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
    ggtitle("Histogram of residuals") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))
  # Residuals vs order
  p6 <- ggplot(df, aes(as.numeric(id), stdres, group = 1)) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    geom_line() +
    geom_hline(yintercept = 0,
               linetype = 2,
               col = col.line) +
    labs(x = "Observation order", y = "Standardized Residuals") +
    ggtitle("Residuals vs observation order") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1))

  p7 <- ggplot(df, aes(fitted, mean)) +
    geom_point(col = col.point,
               size = size.shape,
               alpha = point.alpha) +
    facet_wrap(~GEN) +
    geom_abline(intercept = 0,
                slope = 1,
                col = col.line) +
    labs(x = "Fitted values",
         y = "Observed values") +
    ggtitle("1:1 line plot") +
    plot_theme %+replace%
    theme(axis.text = element_text(size = size.tex.lab, colour = "black"),
          axis.title = element_text(size = size.tex.lab, colour = "black"),
          panel.grid.major.x = element_blank(),
          panel.grid.major.y = element_blank(),
          plot.title = element_text(size = size.tex.lab, hjust = 0, vjust = 1),
          panel.spacing = unit(0, "cm"))
  plots <- list(p1, p2, p3, p4, p5, p6, p7)

  p1 <-
    wrap_plots(plots[c(which)],
               ncol = ncol,
               nrow = nrow,
               ...) +
    plot_annotation(title = var_name)
  return(p1)
  }

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metan documentation built on Nov. 10, 2021, 9:11 a.m.