close easystats/performance#593

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: see
 Title: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
-Version: 0.7.5.9
+Version: 0.7.5.10
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -8,6 +8,9 @@
   gains a `type` argument, to either create density plots, or discrete dots resp.
   interval plots for posterior predictive checks.
 
+* `plot()` for `performance::check_model()` gains an `n_column` argument, to
+  define the number of columns for the diagnostic plots (by default, two columns).
+
 * `plot()` for `performance::check_model()` sometimes failed to create the plot
   under certain conditions, e.g. when the screen or app windows was zoomed-in.
   If an error occurs, a much more informative error message is shown, providing

R/plot.check_collinearity.R

@@ -56,3 +56,132 @@ plot.see_check_collinearity <- function(x,
     is_check_model = FALSE
   )
 }
+
+
+.plot_diag_vif <- function(x,
+                           size_point,
+                           size_line,
+                           theme_style = theme_lucid,
+                           colors = unname(social_colors(c("green", "blue", "red"))),
+                           ci_data = NULL,
+                           is_check_model = FALSE) {
+  ylim <- ceiling(max(x$y, na.rm = TRUE))
+  xlim <- nrow(x)
+  if (ylim < 10) ylim <- 10
+
+  if (!is.null(ci_data)) {
+    x <- cbind(x, ci_data)
+  } else {
+    x$VIF_CI_low <- NA_real_
+    x$VIF_CI_high <- NA_real_
+  }
+
+  # make sure legend is properly sorted
+  x$group <- factor(x$group, levels = c("low", "moderate", "high"))
+  levels(x$group) <- c("Low (< 5)", "Moderate (< 10)", "High (\u2265 10)")
+  names(colors) <- c("Low (< 5)", "Moderate (< 10)", "High (\u2265 10)")
+
+  p <- ggplot2::ggplot(x) +
+    ggplot2::aes(
+      x = .data$x,
+      y = .data$y,
+      color = .data$group,
+      ymin = .data$VIF_CI_low,
+      ymax = .data$VIF_CI_high
+    ) +
+    ggplot2::annotate(
+      geom = "rect",
+      xmin = -Inf,
+      xmax = Inf,
+      ymin = 1,
+      ymax = 5,
+      fill = colors[1],
+      color = NA,
+      alpha = 0.15
+    ) +
+    ggplot2::annotate(
+      geom = "rect",
+      xmin = -Inf,
+      xmax = Inf,
+      ymin = 5,
+      ymax = 10,
+      fill = colors[2],
+      color = NA,
+      alpha = 0.15
+    ) +
+    ggplot2::annotate(
+      geom = "rect",
+      xmin = -Inf,
+      xmax = Inf,
+      ymin = 10,
+      ymax = Inf,
+      fill = colors[3],
+      color = NA,
+      alpha = 0.15
+    ) +
+    {
+      if (!is.null(ci_data)) {
+        list(
+          ggplot2::geom_linerange(
+            linewidth = size_line,
+            na.rm = TRUE
+          ),
+          ggplot2::geom_segment(
+            data = x[x$VIF_CI_high > ylim * 1.15, ],
+            mapping = aes(
+              x = .data$x,
+              xend = .data$x,
+              y = .data$y,
+              yend = .data$VIF_CI_high
+            ),
+            lineend = "round",
+            linejoin = "round",
+            arrow = ggplot2::arrow(
+              ends = "last", type = "closed",
+              angle = 20, length = ggplot2::unit(0.03, "native")
+            ),
+            show.legend = FALSE
+          )
+        )
+      }
+    } +
+    geom_point2(
+      size = size_point,
+      na.rm = TRUE
+    ) +
+    ggplot2::labs(
+      title = "Collinearity",
+      subtitle = "High collinearity (VIF) may inflate parameter uncertainty",
+      x = NULL,
+      y = paste("Variance Inflation", "Factor (VIF, log-scaled)", sep = ifelse(is_check_model, "\n", " "))
+    ) +
+    ggplot2::scale_color_manual(
+      values = colors,
+      aesthetics = c("color", "fill"),
+      guide = ggplot2::guide_legend(title = NULL)
+    ) +
+    theme_style(
+      base_size = 10,
+      plot.title.space = 3,
+      axis.title.space = 5
+    ) +
+    ggplot2::scale_y_continuous(
+      limits = c(1, ylim * 1.15),
+      oob = scales::oob_squish,
+      trans = "log10",
+      expand = c(0, 0),
+      breaks = scales::log_breaks(n = 7, base = 10)
+    ) +
+    ggplot2::scale_x_discrete() +
+    ggplot2::theme(
+      legend.position = "bottom",
+      legend.margin = ggplot2::margin(0, 0, 0, 0),
+      legend.box.margin = ggplot2::margin(-5, -5, -5, -5)
+    )
+
+  if ("facet" %in% colnames(x)) {
+    p <- p + ggplot2::facet_wrap(~facet, nrow = 1, scales = "free")
+  }
+
+  p
+}

R/plot.check_homogeneity.R

@@ -125,3 +125,45 @@ plot.see_check_homogeneity <- function(x, data = NULL, ...) {
     ) +
     theme(plot.title.position = "plot")
 }
+
+
+.plot_diag_homogeneity <- function(x,
+                                   size_point,
+                                   size_line,
+                                   alpha_level = 0.2,
+                                   theme_style = theme_lucid,
+                                   colors = unname(social_colors(c("green", "blue", "red"))),
+                                   dot_alpha_level = 0.8,
+                                   show_dots = TRUE) {
+  p <- ggplot2::ggplot(x, ggplot2::aes(x = .data$x, .data$y))
+
+  if (isTRUE(show_dots)) {
+    p <- p +
+      geom_point2(
+        colour = colors[2],
+        size = size_point,
+        alpha = dot_alpha_level
+      )
+  }
+
+  p +
+    ggplot2::stat_smooth(
+      method = "loess",
+      se = TRUE,
+      alpha = alpha_level,
+      formula = y ~ x,
+      linewidth = size_line,
+      colour = colors[1]
+    ) +
+    ggplot2::labs(
+      title = "Homogeneity of Variance",
+      subtitle = "Reference line should be flat and horizontal",
+      y = expression(sqrt("|Std. residuals|")),
+      x = "Fitted values"
+    ) +
+    theme_style(
+      base_size = 10,
+      plot.title.space = 3,
+      axis.title.space = 5
+    )
+}