Fix empty-figure bugs in gg_partial_rfsrc (release v2.7.1)

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: ggRandomForests
 Type: Package
 Title: Visually Exploring Random Forests
-Version: 2.7.0.9001
-Date: 2026-03-27
+Version: 2.7.1
+Date: 2026-04-27
 Authors@R: person("John", "Ehrlinger",  
   role = c("aut", "cre"), 
   email = "john.ehrlinger@gmail.com")

NEWS.md

@@ -1,7 +1,33 @@
 Package: ggRandomForests
-Version: 2.8.0
+Version: 2.7.1
 
-ggRandomForests v2.8.0
+ggRandomForests v2.7.1
+=====================
+* Fix `gg_partial_rfsrc()` for survival forests: `partial.rfsrc()` was being
+  called without `partial.type`, causing a zero-length comparison
+  (`if (partial.type == "rel.freq") ...`) inside the C-level prediction
+  routine and aborting the call. Survival forests now pass
+  `partial.type = "surv"` (default; configurable via the new `partial.type`
+  argument accepting `"surv"`, `"chf"`, or `"mort"`). This unblocks the
+  `partial-dep` chunk in the survival vignette.
+* Fix `gg_partial_rfsrc()` for survival forests with multiple
+  `partial.time` values: `get.partial.plot.data()` returns yhat as an
+  `[length(partial.values) x length(partial.time)]` matrix, but the previous
+  code assumed a vector and crashed on column-mismatch when assigning
+  `time`. The result is now reshaped to long form so each `(x, time)` pair
+  is a single row.
+* Improve `plot.gg_partial_rfsrc()` survival layout: predictor value is now
+  on the x-axis with one curve per (rounded) time point coloured by `Time`,
+  faceted by variable name. The previous default put time on the x-axis
+  and one curve per predictor value, producing a saturated legend with
+  dozens of nearly-identical lines.
+* Add `tests/testthat/test_plot_layer_data.R`: regression suite that uses
+  `ggplot2::layer_data()` to verify each `plot.gg_*()` method renders
+  non-empty layers for every supported forest family. Catches the
+  empty-figure class of bug (transform/plot column-name mismatch) without
+  requiring visual inspection.
+
+ggRandomForests v2.7.0
 =====================
 * S3 design overhaul: `gg_partial()`, `gg_partialpro()`, and
   `gg_partial_rfsrc()` now stamp their return values with S3 classes

R/gg_partial_rfsrc.R

@@ -47,6 +47,12 @@
 #'   snapped to the nearest entry in \code{rf_model$time.interest} — see the
 #'   \strong{Survival forests} section below.  When \code{NULL} (default),
 #'   three quartile points of \code{time.interest} are used.
+#' @param partial.type Character; type of predicted value for survival
+#'   forests, passed through to \code{\link[randomForestSRC]{partial.rfsrc}}.
+#'   One of \code{"surv"} (default), \code{"chf"}, or \code{"mort"}. Ignored
+#'   for non-survival forests. \code{partial.rfsrc()} requires a non-\code{NULL}
+#'   value for survival families; supplying it here avoids a cryptic
+#'   \dQuote{argument is of length zero} error from the underlying C code.
 #' @param cat_limit Variables with fewer than \code{cat_limit} unique values in
 #'   \code{newx} are treated as categorical; all others are continuous.
 #'   Defaults to 10.
@@ -89,6 +95,7 @@ gg_partial_rfsrc <- function(rf_model,
                              xvar2.name = NULL,
                              newx = NULL,
                              partial.time = NULL,
+                             partial.type = c("surv", "chf", "mort"),
                              cat_limit = 10,
                              n_eval = 25) {
   if (is.null(newx)) {
@@ -112,17 +119,28 @@ gg_partial_rfsrc <- function(rf_model,
   is_surv <- !is.null(rf_model$family) && grepl("surv", rf_model$family)
   if (is_surv) {
     partial.time <- snap_partial_time(rf_model, partial.time)
+    # partial.rfsrc() requires a non-NULL partial.type for survival forests;
+    # NULL triggers a zero-length comparison inside the C code.
+    partial.type <- match.arg(partial.type)
+  } else {
+    partial.type <- NULL
   }
 
   if (is.null(xvar2.name)) {
     pdta <- partial_no_group(xvar.names, newx, rf_model,
-                             cat_limit, n_eval, is_surv, partial.time)
+                             cat_limit, n_eval, is_surv, partial.time,
+                             partial.type)
   } else {
     pdta <- partial_with_group(xvar.names, xvar2.name, newx, rf_model,
-                               cat_limit, n_eval, is_surv, partial.time)
+                               cat_limit, n_eval, is_surv, partial.time,
+                               partial.type)
   }
 
-  split_partial_result(do.call("rbind", pdta))
+  result <- split_partial_result(do.call("rbind", pdta))
+  # Carry partial.type so plot.gg_partial_rfsrc() can pick the correct
+  # y-axis label (Survival / CHF / Mortality).
+  attr(result, "partial.type") <- partial.type
+  result
 }
 
 ## ---- unexported helpers -------------------------------------------------------
@@ -184,7 +202,7 @@ make_eval_grid <- function(xname, newx, cat_limit, n_eval) {
 
 ## Thin wrapper around partial.rfsrc that builds the argument list.
 call_partial_rfsrc <- function(rf_model, xname, xval,
-                                is_surv, partial.time,
+                                is_surv, partial.time, partial.type,
                                 xvar2.name = NULL, x2val = NULL) {
   args <- list(
     object         = rf_model,
@@ -197,44 +215,62 @@ call_partial_rfsrc <- function(rf_model, xname, xval,
   }
   if (is_surv) {
     args$partial.time <- partial.time
+    args$partial.type <- partial.type
   }
   do.call(randomForestSRC::partial.rfsrc, args)
 }
 
 ## Process a single predictor variable and return a tidy data.frame (or NULL).
 partial_one_var <- function(xname, newx, rf_model,
                             cat_limit, n_eval, is_surv, partial.time,
+                            partial.type,
                             xvar2.name = NULL, x2val = NULL) {
   eg <- make_eval_grid(xname, newx, cat_limit, n_eval)
   if (is.null(eg)) return(NULL)
   xval <- eg$xval
   gr   <- eg$categorical
   partial.obj <- call_partial_rfsrc(rf_model, xname, xval,
-                                     is_surv, partial.time,
+                                     is_surv, partial.time, partial.type,
                                      xvar2.name, x2val)
   pout    <- randomForestSRC::get.partial.plot.data(partial.obj, granule = gr)
-  out_dta <- data.frame(x = pout$x, yhat = pout$yhat)
+  # Survival forests with >1 partial.time return yhat as an
+  # [length(partial.values) x length(partial.time)] matrix; expand to long form
+  # so each (x, time) pair is its own row. For non-survival or single-time
+  # cases yhat is already a vector of length(partial.values).
+  if (is.matrix(pout$yhat)) {
+    pt <- if (!is.null(pout$partial.time)) pout$partial.time else seq_len(ncol(pout$yhat))
+    out_dta <- data.frame(
+      x    = rep(pout$x, times = length(pt)),
+      yhat = as.numeric(pout$yhat),
+      time = rep(pt, each = length(pout$x))
+    )
+  } else {
+    out_dta <- data.frame(x = pout$x, yhat = pout$yhat)
+    if (!is.null(pout$partial.time)) {
+      out_dta$time <- pout$partial.time
+    }
+  }
   out_dta$name <- xname
   out_dta$type <- c("continuous", "categorical")[gr + 1L]
-  if (!is.null(pout$partial.time)) {
-    out_dta$time <- pout$partial.time
-  }
   out_dta
 }
 
 ## Compute partial dependence across xvar.names (no grouping variable).
 partial_no_group <- function(xvar.names, newx, rf_model,
-                             cat_limit, n_eval, is_surv, partial.time) {
+                             cat_limit, n_eval, is_surv, partial.time,
+                             partial.type) {
   pdta <- lapply(xvar.names, partial_one_var,
                  newx = newx, rf_model = rf_model,
                  cat_limit = cat_limit, n_eval = n_eval,
-                 is_surv = is_surv, partial.time = partial.time)
+                 is_surv = is_surv, partial.time = partial.time,
+                 partial.type = partial.type)
   Filter(Negate(is.null), pdta)
 }
 
 ## Compute partial dependence across xvar.names for each level of xvar2.name.
 partial_with_group <- function(xvar.names, xvar2.name, newx, rf_model,
-                               cat_limit, n_eval, is_surv, partial.time) {
+                               cat_limit, n_eval, is_surv, partial.time,
+                               partial.type) {
   xv2 <- unique(newx[[xvar2.name]])
   xv2 <- xv2[!is.na(xv2)]
   if (length(xv2) == 0L) {
@@ -248,6 +284,7 @@ partial_with_group <- function(xvar.names, xvar2.name, newx, rf_model,
                     newx = newx, rf_model = rf_model,
                     cat_limit = cat_limit, n_eval = n_eval,
                     is_surv = is_surv, partial.time = partial.time,
+                    partial.type = partial.type,
                     xvar2.name = xvar2.name, x2val = x2val)
     p1dta <- Filter(Negate(is.null), p1dta)
     if (length(p1dta) == 0L) return(NULL)

R/plot.gg_partial.R

@@ -12,6 +12,19 @@
 ####
 ####**********************************************************************
 ####**********************************************************************
+
+# Map partial.type ("surv" / "chf" / "mort") to a human y-axis label.
+# Falls back to "Predicted Survival" when the attribute is absent (e.g. an
+# object built before this attribute was introduced).
+partial_surv_y_label <- function(partial.type) {
+  if (is.null(partial.type)) return("Predicted Survival")
+  switch(partial.type,
+         surv = "Predicted Survival",
+         chf  = "Predicted CHF",
+         mort = "Predicted Mortality",
+         "Predicted Survival")
+}
+
 #' Plot a \code{\link{gg_partial}} object
 #'
 #' Produces ggplot2 partial dependence curves from the named list returned by
@@ -85,8 +98,11 @@ plot.gg_partial <- function(x, ...) {
 #' For standard (non-survival) forests: continuous predictors are line plots,
 #' categorical predictors are bar charts, both faceted by variable name.
 #'
-#' For survival forests (when a \code{time} column is present): each predictor
-#' value is a separate curve over time, faceted by variable name.
+#' For survival forests (when a \code{time} column is present): each evaluation
+#' time point is a separate curve over the predictor's value, faceted by
+#' variable name. The y-axis label adapts to the \code{partial.type} stored on
+#' the object (\dQuote{Predicted Survival}, \dQuote{Predicted CHF}, or
+#' \dQuote{Predicted Mortality}).
 #'
 #' For two-variable surface plots (when a \code{grp} column is present):
 #' each group level is a separate line, faceted by primary predictor name.
@@ -109,19 +125,27 @@ plot.gg_partial_rfsrc <- function(x, ...) {
     cont <- gg_dta$continuous
 
     if (!is.null(cont$time)) {
-      ## Survival forest: predictor value is the grouping variable; x-axis is time
+      ## Survival forest: predictor value on x-axis, one curve per time point.
+      ## Group/colour by the *full-precision* time so distinct horizons that
+      ## happen to round to the same value are not silently merged. The legend
+      ## is relabelled with rounded values for readability.
+      time_levels <- sort(unique(cont$time))
+      cont$.time_factor <- factor(cont$time, levels = time_levels)
+      legend_labels <- format(round(time_levels, 2), trim = TRUE)
+      y_lab <- partial_surv_y_label(attr(gg_dta, "partial.type"))
       gg_cont <- ggplot2::ggplot(
         cont,
         ggplot2::aes(
-          x     = .data$time,
+          x     = .data$x,
           y     = .data$yhat,
-          color = factor(.data$x),
-          group = factor(.data$x)
+          color = .data$.time_factor,
+          group = .data$.time_factor
         )
       ) +
         ggplot2::geom_line() +
-        ggplot2::facet_wrap(~name, scales = "free") +
-        ggplot2::labs(x = "Time", y = "Partial Effect", color = "Predictor value")
+        ggplot2::facet_wrap(~name, scales = "free_x") +
+        ggplot2::scale_color_discrete(labels = legend_labels) +
+        ggplot2::labs(x = NULL, y = y_lab, color = "Time")
 
     } else if (!is.null(cont$grp)) {
       ## Two-variable surface: group is xvar2; x-axis is the primary predictor

cran-comments.md

@@ -1,21 +1,28 @@
-This is ggRandomForests package submission v2.7.0
+This is ggRandomForests package submission v2.7.1
 -------------------------------------------------------------------------
-This is a bug-fix and code-quality release. Key changes:
+This is a bug-fix release. Key changes:
 
-* Fix critical visual bug: `aes()` calls throughout `plot.gg_rfsrc` and
-  `plot.gg_roc` used bare string literals instead of `.data[[col]]`,
-  causing aesthetics to map to constant strings rather than data columns.
-* Fix `bootstrap_survival` CI-band indexing and `gg_rfsrc.randomForest`
-  incorrect use of non-existent `object$xvar` field.
-* Fix `seq_len(nvar)` vs `1:nvar` silent bug in `gg_vimp` and `plot.gg_vimp`.
-* Full test suite migration to testthat 3.x API.
-* Improved GitHub Actions CI (lintr enforcement, warnings-as-errors).
+* Fix `gg_partial_rfsrc()` for survival forests: `partial.rfsrc()` is now
+  called with `partial.type = "surv"` (default; also accepts `"chf"` /
+  `"mort"`). Without this, a zero-length comparison inside the underlying
+  C code aborted the call and left the survival-vignette partial-dep chunks
+  empty.
+* Fix `gg_partial_rfsrc()` for multiple `partial.time` values: yhat is
+  reshaped from the matrix returned by `get.partial.plot.data()` into
+  long form so each `(x, time)` pair is one row.
+* Improve `plot.gg_partial_rfsrc()` survival layout: predictor on the
+  x-axis with one curve per time point coloured by `Time`, faceted by
+  variable name.
+* New regression test file `test_plot_layer_data.R` uses
+  `ggplot2::layer_data()` to verify each `plot.gg_*()` method renders
+  non-empty layers across all forest families, catching empty-figure
+  regressions without visual inspection.
 
 ## R CMD check results
 0 errors | 0 warnings | 0 notes
 
 ## Test environments
-* local R installation (R 4.4, macOS)
+* local R installation (R 4.5, macOS)
 * GitHub Actions: ubuntu-latest (R devel)
 * GitHub Actions: ubuntu-latest (R release)
 * GitHub Actions: ubuntu-latest (R oldrel-1)