fix(vignettes): static PD surfaces + 96-dpi figures to cut install size

DESCRIPTION

@@ -44,7 +44,6 @@ Suggests:
     pkgdown,
     pkgload,
     knitr,
-    plotly,
     ggraph,
     callr
 VignetteBuilder: quarto

NEWS.md

@@ -17,6 +17,11 @@ ggRandomForests v3.1.0
   prose deepened with the same framing; one-line code-comment fixes;
   fixed a stale `@return` in `gg_roc()` (documented a `yvar` column the
   function does not return). No user-facing behaviour change.
+* Vignettes: the regression and survival partial-dependence surfaces are
+  now rendered as static `ggplot2` heat maps instead of interactive
+  `plotly` widgets, and figures render at 96 dpi. This cuts the installed
+  size from ~17 MB to ~5 MB (the `plotly` library is no longer bundled into
+  the vignette HTML). `plotly` is dropped from `Suggests`.
 
 ggRandomForests v3.0.0
 ======================

vignettes/ggRandomForests-regression.qmd

@@ -4,6 +4,8 @@ author: "John Ehrlinger"
 date: today
 format:
   html:
+    fig-format: png
+    fig-dpi: 96
     toc: true
     toc-depth: 3
     html-math-method: mathjax
@@ -56,8 +58,8 @@ Boston Housing data set [@Harrison:1978; @Belsley:1980]:
 3. **Variable selection** --- VIMP and minimal depth via `max.subtree()`
 4. **Dependence plots** --- variable dependence and partial dependence via
    `gg_variable()` and `gg_partial_rfsrc()`
-5. **Variable interactions** --- conditioning plots and interactive 3-D partial
-   dependence surfaces with **plotly**
+5. **Variable interactions** --- conditioning plots and partial dependence
+   surfaces
 
 ```{r packages}
 library(ggplot2)
@@ -346,7 +348,7 @@ plot(gg_v, xvar = "rm", alpha = 0.5) +
 The `rm` effect is strongest in low-`lstat` tracts (bottom-left panels) and
 nearly flat in high-`lstat` tracts, confirming a meaningful interaction.
 
-# Interactive Partial Dependence Surface
+# Partial Dependence Surface
 
 To visualize the joint partial dependence of `medv` on `lstat` and `rm`, we
 compute partial dependence on a grid: 25 values of `rm`, each evaluated at 25
@@ -367,38 +369,12 @@ surface_list <- lapply(rm_grid, function(rm_val) {
 surface_df <- bind_rows(surface_list)
 ```
 
-```{r plotly-surface, fig.cap="Interactive partial dependence surface: median home value as a function of lstat and rm."}
-if (requireNamespace("plotly", quietly = TRUE)) {
-  library(plotly)
-
-  surface_wide <- surface_df |>
-    select(lstat = x, rm, medv = yhat) |>
-    arrange(rm, lstat)
-
-  lstat_vals <- sort(unique(surface_wide$lstat))
-  rm_vals    <- sort(unique(surface_wide$rm))
-  z_matrix   <- matrix(surface_wide$medv,
-                        nrow = length(rm_vals),
-                        ncol = length(lstat_vals),
-                        byrow = TRUE)
-
-  plot_ly(x = lstat_vals, y = rm_vals, z = z_matrix) |>
-    add_surface(colorscale = "Viridis", showscale = TRUE) |>
-    layout(
-      scene = list(
-        xaxis = list(title = "Lower Status (%)"),
-        yaxis = list(title = "Rooms per Dwelling"),
-        zaxis = list(title = "Median Value ($1000s)")
-      )
-    )
-} else {
-  message("Install the plotly package for interactive 3D surfaces.")
-  ggplot(surface_df, aes(x = x, y = rm, fill = yhat)) +
-    geom_tile() +
-    scale_fill_viridis_c(name = "Median Value") +
-    labs(x = "Lower Status (%)", y = "Rooms per Dwelling") +
-    theme_bw()
-}
+```{r pd-surface, fig.cap="Partial dependence surface: median home value as a function of lstat and rm. Fill colour is the predicted median value."}
+ggplot(surface_df, aes(x = x, y = rm, fill = yhat)) +
+  geom_tile() +
+  scale_fill_viridis_c(name = "Median Value\n($1000s)") +
+  labs(x = "Lower Status (%)", y = "Rooms per Dwelling") +
+  theme_bw()
 ```
 
 The surface confirms the strong interaction: home values are highest when `lstat`
@@ -419,7 +395,7 @@ We have walked a full random forest regression analysis with
   same shapes the raw-data EDA hinted at.
 - Partial dependence from `gg_partial_rfsrc()` gave the risk-adjusted version
   of those curves: concave for `lstat`, threshold-like for `rm`.
-- Conditioning plots and the interactive surface pulled out the `lstat`--`rm`
+- Conditioning plots and the partial dependence surface pulled out the `lstat`--`rm`
   interaction, with the room-size effect strongest in high-status tracts.
 
 Notice the pattern in all of this. Each `gg_*()` function returns a tidy

vignettes/ggRandomForests-survival.qmd

@@ -4,6 +4,8 @@ author: "John Ehrlinger"
 date: today
 format:
   html:
+    fig-format: png
+    fig-dpi: 96
     toc: true
     toc-depth: 3
     html-math-method: mathjax
@@ -68,8 +70,8 @@ primary biliary cirrhosis (PBC) data set [@fleming:1991]:
 3. **Variable selection** --- VIMP and minimal depth via `max.subtree()`
 4. **Dependence plots** --- variable dependence and partial dependence via
    `gg_variable()` and `gg_partial_rfsrc()`
-5. **Variable interactions** --- conditioning plots and interactive 3-D partial
-   dependence surfaces with **plotly**
+5. **Variable interactions** --- conditioning plots and partial dependence
+   surfaces
 
 ```{r packages}
 library(ggplot2)
@@ -452,7 +454,7 @@ survival.
 ::: {.callout-warning}
 **Known issue (draft):** `randomForestSRC::partial.rfsrc()` currently fails for
 survival forests in randomForestSRC ≥ 3.3.  The partial dependence and
-interactive surface sections below will show an error until this upstream bug
+surface sections below will show an error until this upstream bug
 is resolved.  All other sections of this vignette are fully functional.
 :::
 
@@ -540,7 +542,7 @@ plot(gg_v1, xvar = "bili", alpha = 0.5) +
 The effect of bilirubin attenuates at higher albumin levels, suggesting an
 interaction between these two liver function markers.
 
-# Interactive Partial Dependence Surfaces
+# Partial Dependence Surfaces
 
 For a richer view of the interaction between bilirubin and albumin, we construct
 a partial dependence surface. We compute partial dependence on a grid of 25
@@ -564,37 +566,10 @@ surface_list <- lapply(alb_grid, function(alb_val) {
 surface_df <- bind_rows(surface_list)
 ```
 
-```{r plotly-surface, error=TRUE, fig.cap="Interactive partial dependence surface: survival as a function of bilirubin and albumin."}
+```{r pd-surface, error=TRUE, fig.height=5, fig.cap="Partial dependence surface: survival at 1 year as a function of bilirubin and albumin. Fill colour is the predicted survival probability."}
 if (!exists("surface_df")) {
-  message("surface_df not available — skipping plotly surface (see surface-data chunk error above).")
-} else if (requireNamespace("plotly", quietly = TRUE)) {
-  # Reshape for surface
-  library(plotly)
-
-  surface_wide <- surface_df |>
-    select(bili = x, albumin, survival = yhat) |>
-    arrange(albumin, bili)
-
-  # Create matrix form
-  bili_vals <- sort(unique(surface_wide$bili))
-  alb_vals  <- sort(unique(surface_wide$albumin))
-  z_matrix  <- matrix(surface_wide$survival,
-                       nrow = length(alb_vals),
-                       ncol = length(bili_vals),
-                       byrow = TRUE)
-
-  plot_ly(x = bili_vals, y = alb_vals, z = z_matrix) |>
-    add_surface(colorscale = "Viridis", showscale = TRUE) |>
-    layout(
-      scene = list(
-        xaxis = list(title = "Bilirubin"),
-        yaxis = list(title = "Albumin"),
-        zaxis = list(title = "Survival")
-      )
-    )
+  message("surface_df not available --- skipping surface (see surface-data chunk error above).")
 } else {
-  message("Install the plotly package for interactive 3D surfaces.")
-  # Fallback: contour plot with ggplot2
   ggplot(surface_df, aes(x = x, y = albumin, fill = yhat)) +
     geom_tile() +
     scale_fill_viridis_c(name = "Survival") +
@@ -605,7 +580,7 @@ if (!exists("surface_df")) {
 
 The surface shows that survival is highest when bilirubin is low and albumin is
 high (upper-left corner), and drops steeply as bilirubin increases or albumin
-decreases. The non-planar shape of the surface --- particularly the steep
+decreases. The curvature of the surface --- particularly the steep
 gradient at low albumin and high bilirubin --- confirms the interaction detected
 in the conditional plots.
 
@@ -685,7 +660,7 @@ We have walked a full random survival forest analysis with
   where the gap between time horizons widens.
 - Partial dependence from `gg_partial_rfsrc()` gave the risk-adjusted version
   of those curves and backed the log-transforms used in the parametric model.
-- Conditioning plots and the interactive surface drew out the
+- Conditioning plots and the partial dependence surface drew out the
   bilirubin--albumin interaction.
 - `gg_brier()` measured how accurate the predictions actually were, both
   across time and as a single CRPS summary.

vignettes/ggRandomForests.qmd

@@ -4,6 +4,8 @@ author: "John Ehrlinger"
 date: today
 format: 
   html:
+    fig-format: png
+    fig-dpi: 96
     toc: true
     html-math-method: mathjax
 editor: 

vignettes/varpro.qmd

@@ -4,6 +4,8 @@ author: "John Ehrlinger"
 date: today
 format:
   html:
+    fig-format: png
+    fig-dpi: 96
     toc: true
     toc-depth: 3
     html-math-method: mathjax