skpr v1.9.2: Fix correlation plot in skprGUI() not appearing right away

-Update FDS plot in skprGUI() to look better and add max PD
-Fix plot_correlations()

Author: tylermorganwall

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: skpr
 Title: Design of Experiments Suite: Generate and Evaluate Optimal Designs
-Date: 2025-09-19
-Version: 1.9.1
+Date: 2025-09-26
+Version: 1.9.2
 Authors@R: c(person("Tyler", "Morgan-Wall", email = "tylermw@gmail.com", role = c("aut", "cre")),
              person("George", "Khoury", email = "george.m.khoury@gmail.com", role = c("aut")))
 Description: Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
@@ -10,7 +10,7 @@ Depends:
   R (>= 4.1.0)
 License: GPL-3
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Imports: 
     utils,
     iterators,

R/plot_correlations.R

@@ -115,33 +115,25 @@ plot_correlations = function(
   } else {
     model1 = model
   }
-
-  factornames = colnames(design)[
-    unlist(lapply(design, class)) %in% c("factor", "character")
-  ]
-  if (length(factornames) > 0) {
-    contrastlist = list()
-    for (name in 1:length(factornames)) {
-      contrastlist[[factornames[name]]] = contr.simplex
-    }
-  } else {
-    contrastlist = NULL
-  }
+  presetcontrasts = list()
+  contrast_info = generate_contrast_list(
+    design,
+    presetcontrasts,
+    contr.simplex
+  )
+  contrastslist_cormat = contrast_info$contrastslist_cormat
   #------Normalize/Center numeric columns ------#
   if (standardize) {
-    for (column in seq_len(ncol(skpr_output))) {
-      if (is.numeric(design[, column])) {
-        midvalue = mean(c(max(design[, column]), min(design[, column])))
-        design[, column] = (design[, column] - midvalue) /
-          (max(design[, column]) - midvalue)
-      }
-    }
+    design = normalize_design(design)
   }
+  #Main effects model
+  mm_main = model.matrix(~., design, contrasts.arg = contrastslist_cormat)
+  #All interactions included
+  mm = model.matrix(model1, design, contrasts.arg = contrastslist_cormat)
 
-  mm_main = model.matrix(model, design, contrasts.arg = contrastlist)
-  mm = model.matrix(model1, design, contrasts.arg = contrastlist)
   X = mm_main[, -1, drop = FALSE]
-  int_nms = setdiff(colnames(mm_main), colnames(mm)) # just the interactions
+  int_nms = setdiff(colnames(mm), colnames(mm_main)) # just the interactions
+
   Z = mm[, int_nms, drop = FALSE]
   W = solve(V)
 
@@ -159,9 +151,10 @@ plot_correlations = function(
     imagecolors = colorRampPalette(customcolors)(101)
   }
   if (!is.null(custompar)) {
-    warning("`custompar` is no longer supported; adjust the returned ggplot object instead.")
+    warning(
+      "`custompar` is no longer supported; adjust the returned ggplot object instead."
+    )
   }
-
   labels = colnames(mm)[-1]
   plot_matrix = t(cormat[ncol(cormat):1, , drop = FALSE])
   plot_df = data.frame(

R/plot_fds.R

@@ -89,6 +89,7 @@ plot_fds = function(
         values = fds_values[[i]]
         midval = values[mid_index(values)]
         fraction = seq_along(values) / length(values)
+        maxval = max(values)
         df = data.frame(
           fraction = fraction,
           variance = values
@@ -108,19 +109,49 @@ plot_fds = function(
             linetype = "dashed",
             color = "red"
           ) +
+          ggplot2::geom_hline(
+            yintercept = maxval,
+            linetype = "dashed",
+            color = "black"
+          ) +
+          ggplot2::annotate(
+            "text",
+            label = sprintf("Mid PV: %0.3f", midval),
+            x = 0,
+            hjust = 0.0,
+            y = midval,
+            vjust = -0.25,
+            fontface = "bold",
+            size = 6,
+            color = "red"
+          ) +
+          ggplot2::annotate(
+            "text",
+            label = sprintf("Max PV: %0.3f", maxval),
+            x = 0,
+            hjust = 0.0,
+            y = maxval,
+            vjust = -0.25,
+            size = 6,
+            color = "black",
+            fontface = "bold",
+          ) +
           ggplot2::scale_x_continuous(
             limits = c(0, 1),
-            expand = ggplot2::expansion(mult = 0)
+            expand = c(0, 0.0)
           ) +
           ggplot2::scale_y_continuous(
-            limits = c(0, yaxis_max),
+            limits = c(0, maxyaxis),
             expand = ggplot2::expansion(mult = 0)
           ) +
           ggplot2::labs(
-            x = description[i],
+            x = description,
             y = "Prediction Variance"
           ) +
-          ggplot2::theme_minimal()
+          ggplot2::theme_minimal() +
+          ggplot2::theme(margins = ggplot2::unit(c(20, 20, 20, 20), "points")) +
+          ggplot2::theme(text = ggplot2::element_text(size = 24)) +
+          ggplot2::coord_cartesian(clip = F)
       }
       if (requireNamespace("gridExtra", quietly = TRUE)) {
         do.call(
@@ -349,6 +380,7 @@ plot_fds = function(
       fraction = seq_along(varsorderedscaled) / length(varsorderedscaled),
       variance = varsorderedscaled
     )
+    maxval = max(varsorderedscaled)
     plot_obj = ggplot2::ggplot(df, ggplot2::aes(x = fraction, y = variance)) +
       ggplot2::geom_line(color = "blue", linewidth = 1) +
       ggplot2::geom_vline(
@@ -361,9 +393,36 @@ plot_fds = function(
         linetype = "dashed",
         color = "red"
       ) +
+      ggplot2::geom_hline(
+        yintercept = maxval,
+        linetype = "dashed",
+        color = "black"
+      ) +
+      ggplot2::annotate(
+        "text",
+        label = sprintf("Mid PV: %0.3f", midval),
+        x = 0,
+        hjust = 0.0,
+        y = midval,
+        vjust = -0.25,
+        fontface = "bold",
+        size = 6,
+        color = "red"
+      ) +
+      ggplot2::annotate(
+        "text",
+        label = sprintf("Max PV: %0.3f", maxval),
+        x = 0,
+        hjust = 0.0,
+        y = maxval,
+        vjust = -0.25,
+        size = 6,
+        color = "black",
+        fontface = "bold",
+      ) +
       ggplot2::scale_x_continuous(
         limits = c(0, 1),
-        expand = ggplot2::expansion(mult = 0)
+        expand = c(0, 0.0)
       ) +
       ggplot2::scale_y_continuous(
         limits = c(0, maxyaxis),
@@ -373,7 +432,10 @@ plot_fds = function(
         x = description,
         y = "Prediction Variance"
       ) +
-      ggplot2::theme_minimal()
+      ggplot2::theme_minimal() +
+      ggplot2::theme(margins = ggplot2::unit(c(20, 20, 20, 20), "points")) +
+      ggplot2::theme(text = ggplot2::element_text(size = 24)) +
+      ggplot2::coord_cartesian(clip = F)
     print(plot_obj)
     return(invisible(varsorderedscaled))
   }

R/skprGUI.R

@@ -2591,18 +2591,22 @@ skprGUI = function(
     ) |>
       bindEvent(powerresultssurv())
 
-    output$aliasplot = renderPlot({
-      req(runmatrix(), cancelOutput = TRUE)
-      if (displayed_design_number_factors() > 1) {
-		print(runmatrix())
-        runmatrix() %>%
-          plot_correlations()
-      }
-    }) |>
-      bindEvent(runmatrix(), ignoreInit = TRUE)
+    output$aliasplot = renderPlot(
+      {
+        req(runmatrix(), cancelOutput = TRUE)
+        if (displayed_design_number_factors() > 1) {
+          runmatrix() %>%
+            plot_correlations() +
+            ggplot2::theme(text = ggplot2::element_text(size = 12))
+        }
+      },
+      width = fdsplot_width
+    ) |>
+      bindEvent(input$evalbutton, ignoreInit = TRUE)
 
     output$fdsplot = renderPlot(
       {
+        req(runmatrix(), cancelOutput = TRUE)
         runmatrix() %>%
           plot_fds()
       },