diff --git a/NEWS.md b/NEWS.md
index bf6e9168..d1a61d5c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,21 @@ Version: 4.0.0
 
 ggRandomForests v4.0.0 (development)
 ====================================
+* `gg_partial_varpro(scale = "prob")` now restores each subject's level
+  before averaging, so the curve is the expected proportion it is documented
+  as. `partialpro()` fits each subject's curve separately but returns every
+  row at the cohort-mean intercept, keeping only the subject's slope. That put
+  every subject at the average log-odds, so `"prob"` nearly matched
+  `"prob_typical"` and missed the proportion it claims to estimate. Each
+  subject's curve is now shifted to pass through its own out-of-bag log-odds
+  at its observed value, and the shape `partialpro()` fitted is kept. On
+  simulated data with widely spread subjects, this cut the error against the
+  true partial dependence from 0.105 to 0.033 (8 of 8 seeds). **`"prob"`
+  curves change**, most on heterogeneous cohorts. The restoration needs
+  `object`; without it `"prob"` warns and returns the old curve. It is also
+  skipped when `...` passes a custom `learner` or `newdata`, and for binary
+  variables, which `partialpro()` already returns per subject. The provenance
+  records `anchored`.
 * `gg_partial_varpro(scale = "surv")` no longer returns survival above 1.
   `partialpro()` smooths each case's S(tau) with an unbounded polynomial, so
   where survival is near 1 (early horizons, before most events) the averaged
@@ -71,11 +86,11 @@ ggRandomForests v4.0.0 (development)
 
   They are different estimands and they disagree. The inverse logit is concave
   above zero and convex below it, so by Jensen `"prob"` is pulled toward 0.5 at
-  both ends, by more the more heterogeneous the cohort. Where the per-subject
-  log-odds carry an SD near 4.5, a point reading 0.96 under `"prob_typical"`
-  reads 0.74 under `"prob"` -- large enough to change how a figure is read, so
-  the choice should be deliberate. A figure captioned as a percentage of
-  patients wants `"prob"`. `?gg_partial_varpro` sets out both.
+  both ends, by more the more heterogeneous the cohort. In a simulation with
+  per-subject log-odds SD near 4, a point reading 0.13 under `"prob_typical"`
+  reads 0.35 under `"prob"` (true value 0.38) -- large enough to change how a
+  figure is read, so the choice should be deliberate. A figure captioned as a
+  percentage of patients wants `"prob"`. `?gg_partial_varpro` sets out both.
 
   The distinction applies only to the `continuous` frame; the `categorical`
   frame keeps values unaveraged, so both scales return the same numbers there.
diff --git a/R/gg_partial_varpro.R b/R/gg_partial_varpro.R
index 987ee265..a6e637c0 100644
--- a/R/gg_partial_varpro.R
+++ b/R/gg_partial_varpro.R
@@ -258,7 +258,28 @@
 #' back-transforms to probability \eqn{P(Y = \mathrm{target})}, \code{"odds"} to
 #' the odds, and \code{"logodds"} keeps the raw scale.  The back-transform is
 #' applied per observation \emph{before} averaging, so the curve is the mean
-#' predicted probability, not the probability of the mean log-odds.  The
+#' predicted probability, not the probability of the mean log-odds.
+#'
+#' **Restoring each subject's level (scale = "prob"):** \code{partialpro} fits
+#' each subject's curve separately but returns every row at the cohort-mean
+#' intercept, keeping only the subject's own slope. On the log-odds scale that
+#' sets every subject to the average log-odds, and averaging per-subject
+#' probabilities then no longer gives the expected proportion. So for
+#' \code{"prob"}, \code{gg_partial_varpro} shifts each subject's curve to pass
+#' through that subject's own out-of-bag log-odds at its observed value of the
+#' variable, and then back-transforms and averages. The shape
+#' \code{partialpro} fitted is unchanged. This needs \code{object}, the
+#' classification fit, and is recorded as \code{anchored} in the provenance.
+#' Without \code{object}, or when \code{...} passes \code{partialpro} a custom
+#' \code{learner} or \code{newdata}, the curve is left as \code{partialpro}
+#' returned it (with a warning when \code{object} is missing). A precomputed
+#' \code{part_dta} is anchored whenever \code{object} is supplied, which
+#' assumes it came from \code{partialpro(object)} with its default learner; a
+#' \code{part_dta} built with your own learner should be passed with
+#' \code{scale = "logodds"} instead. A subject that was never out of bag takes
+#' its in-bag prediction as its anchor. Binary variables
+#' are never shifted, because \code{partialpro} already returns per-subject
+#' levels for them.  The
 #' \code{causal} contrast is shown only on \code{"logodds"} (see
 #' \code{\link{plot.gg_partial_varpro}}).
 #'
@@ -285,9 +306,13 @@
 #' Jensen's inequality \code{"prob"} is pulled toward \eqn{0.5} relative to
 #' \code{"prob_typical"}, at both ends of the curve. The gap widens with the
 #' spread of per-subject log-odds, and on a heterogeneous cohort it is not
-#' small: where the per-subject log-odds carry an SD near 4.5, a point reading
-#' \eqn{0.96} under \code{"prob_typical"} reads \eqn{0.74} under
-#' \code{"prob"}.
+#' small. In a simulation where a second variable spreads the subjects'
+#' log-odds to an SD near 4, one point reads \eqn{0.13} under
+#' \code{"prob_typical"} and \eqn{0.35} under \code{"prob"}, against a true
+#' partial dependence of \eqn{0.38}. Before the level restoration described
+#' above, \code{"prob"} read \eqn{0.14} there: with every subject at the mean
+#' log-odds, the two scales nearly coincide. \code{"prob_typical"} uses
+#' \code{partialpro}'s values as returned.
 #'
 #' Which to report is a question about the claim, not about the code. If the
 #' sentence is "what fraction of these patients would wean", that is
@@ -381,7 +406,8 @@
 #' )
 #' ## The two probability scales differ by the ORDER of averaging and
 #' ## back-transform, and disagree whenever subjects are heterogeneous.
-#' pa <- gg_partial_varpro(mock_data, scale = "prob")
+#' ## Mock data has no fit, so "prob" cannot restore subject levels and warns.
+#' pa <- suppressWarnings(gg_partial_varpro(mock_data, scale = "prob"))
 #' pt <- gg_partial_varpro(mock_data, scale = "prob_typical")
 #' head(data.frame(prob = pa$continuous$parametric,
 #'                 prob_typical = pt$continuous$parametric))
@@ -479,6 +505,9 @@ gg_partial_varpro <- function(part_dta  = NULL,
   ## 'value_scale' differs from the reported 'scale' only for surv recomputed
   ## here: those values are S(tau) from our learner and get clamped to [0, 1].
   value_scale <- scale
+  ## '...' reaches partialpro() only when part_dta is computed here; otherwise
+  ## it is warned as ignored above and must not change the result.
+  pp_dots <- if (is.null(part_dta)) names(list(...)) else character(0)
   if (is.null(part_dta)) {
     if (scale == "surv") value_scale <- "surv_learner"
     learner <- switch(scale,
@@ -506,6 +535,10 @@ gg_partial_varpro <- function(part_dta  = NULL,
   prov <- .varpro_provenance(object, scale, time, path = "A",
                              target = .varpro_target(object, list(...)))
 
+  anc <- .anchor_prob_scale(part_dta, object, scale, prov$target, pp_dots)
+  part_dta      <- anc$part_dta
+  prov$anchored <- anc$anchored
+
   dfs <- .build_varpro_dfs(part_dta, nvars, cat_limit, value_scale)
   continuous  <- dfs$continuous
   categorical <- dfs$categorical
@@ -738,6 +771,76 @@ gg_partial_varpro <- function(part_dta  = NULL,
 ## Classification target class label: the `target` passed through ... if any,
 ## else the last factor level of the response (partialpro's default target).
 ## NA for non-classification fits or when only part_dta is supplied.
+## partialpro() fits each case's curve separately but returns every case at the
+## cohort-mean intercept (yhat.par = global.mean + B %*% x^k). On the log-odds
+## scale that pins every case to the mean log-odds, so "prob"'s per-case
+## plogis-then-average no longer gives the expected proportion of the cohort.
+## Put each case's level back: shift its curve so it passes through the case's
+## own OOB log-odds at its observed x. The shape (the slopes partialpro kept) is
+## untouched. Binary variables are skipped: partialpro returns per-case level
+## means for them, without the swap.
+#' @keywords internal
+.anchor_varpro_levels <- function(part_dta, anchor) {
+  for (k in seq_along(part_dta)) {
+    feat <- part_dta[[k]]
+    if (length(unique(feat$xorg)) == 2L || is.null(feat$case)) next
+    offset <- vapply(seq_along(feat$case), function(i) {
+      r <- feat$case[i]
+      anchor[r] - stats::approx(feat$xvirtual, feat$yhat.par[i, ], feat$xorg[r],
+                                rule = 2)$y
+    }, numeric(1))
+    feat$yhat.par    <- feat$yhat.par    + offset
+    feat$yhat.nonpar <- feat$yhat.nonpar + offset
+    part_dta[[k]] <- feat
+  }
+  part_dta
+}
+
+## "prob" averages per-case probabilities, which needs each case's own level;
+## see .anchor_varpro_levels(). The anchors are the forest's OOB predictions,
+## so a caller's own learner or newdata puts the curves on a footing the
+## anchors don't share, and those are left as partialpro returned them.
+#' @keywords internal
+.anchor_prob_scale <- function(part_dta, object, scale, target, dot_names) {
+  if (scale != "prob") return(list(part_dta = part_dta, anchored = FALSE))
+  if (!is.null(object$rf) && identical(object$family, "class") &&
+      !any(c("learner", "newdata") %in% dot_names)) {
+    return(list(part_dta = .anchor_varpro_levels(
+                  part_dta, .varpro_oob_logodds(object, target)),
+                anchored = TRUE))
+  }
+  if (is.null(object)) {
+    warning("gg_partial_varpro: scale = 'prob' without 'object' cannot ",
+            "restore each case's level, so the curve averages cases pinned ",
+            "to the cohort-mean log-odds and is not the expected ",
+            "proportion. Supply 'object' (the classification varpro fit).",
+            call. = FALSE)
+  }
+  list(part_dta = part_dta, anchored = FALSE)
+}
+
+## Per-row OOB log-odds of the target class, clamped at 0.001 as partialpro's
+## own mylogodds() is, so the anchor and the curves share a scale. A case that
+## was never out of bag (common with few trees) has no OOB prediction; it takes
+## its in-bag one instead, since an NA anchor would drop the case from the
+## average without saying so.
+#' @keywords internal
+.varpro_oob_logodds <- function(object, target) {
+  pr  <- randomForestSRC::predict.rfsrc(object$rf, perf.type = "none")
+  pick <- function(p) {
+    if (is.null(p) || is.null(dim(p))) return(p)
+    ## 'target' is a class label, or an index when passed as a number.
+    col <- if (target %in% colnames(p)) target else as.integer(target)
+    p[, col]
+  }
+  p   <- pick(pr$predicted.oob)
+  inb <- pick(pr$predicted)
+  if (is.null(p)) p <- inb
+  miss <- is.na(p)
+  p[miss] <- inb[miss]
+  stats::qlogis(pmin(pmax(p, 1e-3), 1 - 1e-3))
+}
+
 #' @keywords internal
 .varpro_target <- function(object, dots) {
   if (is.null(object) || !identical(object$family, "class"))
@@ -972,8 +1075,10 @@ gg_partial_varpro <- function(part_dta  = NULL,
 ## plogis is concave above 0 and convex below it, so by Jensen these are not
 ## the same curve: "prob" is pulled toward 0.5 relative to "prob_typical", and
 ## the gap widens with the spread of per-subject log-odds.  On a heterogeneous
-## cohort it is large -- a point where "prob_typical" reads 0.96 can read 0.74
-## under "prob" when the per-subject log-odds carry an SD near 4.5.
+## cohort it is large: 0.13 under "prob_typical" against 0.35 under "prob" at
+## a per-subject log-odds SD near 4 (see the roxygen). "prob" needs the
+## per-case levels .anchor_varpro_levels() restores, or it collapses toward
+## "prob_typical".
 #' @keywords internal
 .varpro_column_summary <- function(mat, scale) {
   if (identical(scale, "prob_typical")) {
diff --git a/R/plot.gg_partial_varpro.R b/R/plot.gg_partial_varpro.R
index 2a575341..02bbd67b 100644
--- a/R/plot.gg_partial_varpro.R
+++ b/R/plot.gg_partial_varpro.R
@@ -230,7 +230,8 @@
 #' plot(pp, type = "parametric", panels = spec) & ggplot2::theme_minimal()
 #'
 #' ## complement = TRUE reads a failure model as its success probability.
-#' pp_prob <- gg_partial_varpro(mock_data, scale = "prob")
+#' ## (Mock data has no fit to restore subject levels from, hence the warning.)
+#' pp_prob <- suppressWarnings(gg_partial_varpro(mock_data, scale = "prob"))
 #' plot(pp_prob, type = "parametric", complement = TRUE)
 #'
 #' @importFrom ggplot2 .data ggplot aes geom_line geom_boxplot facet_wrap labs
diff --git a/man/gg_partial_varpro.Rd b/man/gg_partial_varpro.Rd
index f82f5fa4..f848ed9b 100644
--- a/man/gg_partial_varpro.Rd
+++ b/man/gg_partial_varpro.Rd
@@ -245,7 +245,28 @@ of the target class.  \code{scale = "prob"} (the classification default)
 back-transforms to probability \eqn{P(Y = \mathrm{target})}, \code{"odds"} to
 the odds, and \code{"logodds"} keeps the raw scale.  The back-transform is
 applied per observation \emph{before} averaging, so the curve is the mean
-predicted probability, not the probability of the mean log-odds.  The
+predicted probability, not the probability of the mean log-odds.
+
+\strong{Restoring each subject's level (scale = "prob"):} \code{partialpro} fits
+each subject's curve separately but returns every row at the cohort-mean
+intercept, keeping only the subject's own slope. On the log-odds scale that
+sets every subject to the average log-odds, and averaging per-subject
+probabilities then no longer gives the expected proportion. So for
+\code{"prob"}, \code{gg_partial_varpro} shifts each subject's curve to pass
+through that subject's own out-of-bag log-odds at its observed value of the
+variable, and then back-transforms and averages. The shape
+\code{partialpro} fitted is unchanged. This needs \code{object}, the
+classification fit, and is recorded as \code{anchored} in the provenance.
+Without \code{object}, or when \code{...} passes \code{partialpro} a custom
+\code{learner} or \code{newdata}, the curve is left as \code{partialpro}
+returned it (with a warning when \code{object} is missing). A precomputed
+\code{part_dta} is anchored whenever \code{object} is supplied, which
+assumes it came from \code{partialpro(object)} with its default learner; a
+\code{part_dta} built with your own learner should be passed with
+\code{scale = "logodds"} instead. A subject that was never out of bag takes
+its in-bag prediction as its anchor. Binary variables
+are never shifted, because \code{partialpro} already returns per-subject
+levels for them.  The
 \code{causal} contrast is shown only on \code{"logodds"} (see
 \code{\link{plot.gg_partial_varpro}}).
 
@@ -272,9 +293,13 @@ These are different estimands and they do not agree.
 Jensen's inequality \code{"prob"} is pulled toward \eqn{0.5} relative to
 \code{"prob_typical"}, at both ends of the curve. The gap widens with the
 spread of per-subject log-odds, and on a heterogeneous cohort it is not
-small: where the per-subject log-odds carry an SD near 4.5, a point reading
-\eqn{0.96} under \code{"prob_typical"} reads \eqn{0.74} under
-\code{"prob"}.
+small. In a simulation where a second variable spreads the subjects'
+log-odds to an SD near 4, one point reads \eqn{0.13} under
+\code{"prob_typical"} and \eqn{0.35} under \code{"prob"}, against a true
+partial dependence of \eqn{0.38}. Before the level restoration described
+above, \code{"prob"} read \eqn{0.14} there: with every subject at the mean
+log-odds, the two scales nearly coincide. \code{"prob_typical"} uses
+\code{partialpro}'s values as returned.
 
 Which to report is a question about the claim, not about the code. If the
 sentence is "what fraction of these patients would wean", that is
@@ -416,7 +441,8 @@ mock_data <- list(
 )
 ## The two probability scales differ by the ORDER of averaging and
 ## back-transform, and disagree whenever subjects are heterogeneous.
-pa <- gg_partial_varpro(mock_data, scale = "prob")
+## Mock data has no fit, so "prob" cannot restore subject levels and warns.
+pa <- suppressWarnings(gg_partial_varpro(mock_data, scale = "prob"))
 pt <- gg_partial_varpro(mock_data, scale = "prob_typical")
 head(data.frame(prob = pa$continuous$parametric,
                 prob_typical = pt$continuous$parametric))
diff --git a/man/plot.gg_partial_varpro.Rd b/man/plot.gg_partial_varpro.Rd
index e4ebace8..cc0a5d7d 100644
--- a/man/plot.gg_partial_varpro.Rd
+++ b/man/plot.gg_partial_varpro.Rd
@@ -270,7 +270,8 @@ plot(pp, type = c("parametric", "nonparametric"))
 plot(pp, type = "parametric", panels = spec) & ggplot2::theme_minimal()
 
 ## complement = TRUE reads a failure model as its success probability.
-pp_prob <- gg_partial_varpro(mock_data, scale = "prob")
+## (Mock data has no fit to restore subject levels from, hence the warning.)
+pp_prob <- suppressWarnings(gg_partial_varpro(mock_data, scale = "prob"))
 plot(pp_prob, type = "parametric", complement = TRUE)
 
 }
diff --git a/tests/testthat/test_gg_partial_varpro.R b/tests/testthat/test_gg_partial_varpro.R
index 8966f88f..047d2228 100644
--- a/tests/testthat/test_gg_partial_varpro.R
+++ b/tests/testthat/test_gg_partial_varpro.R
@@ -291,7 +291,7 @@ test_that(".is_bounded_scale flags prob/odds/surv only", {
 ## ── v3.3.0 conversion in the extractor (mean of probabilities) ───────────────
 test_that("gg_partial_varpro: scale='prob' is mean of plogis, causal NA", {
   d <- make_mock_vpro_data()
-  res <- gg_partial_varpro(d, scale = "prob")
+  res <- suppressWarnings(gg_partial_varpro(d, scale = "prob"))  # no object
   age <- res$continuous[res$continuous$name == "age", ]
   expected <- colMeans(stats::plogis(d$age$yhat.par), na.rm = TRUE)
   expect_equal(age$parametric, expected)
@@ -335,7 +335,7 @@ test_that(".partial_varpro_ylabel: prob/odds/logodds/surv labels", {
 ## ── v3.3.0 plot: causal hidden on bounded scales ─────────────────────────────
 test_that("plot.gg_partial_varpro: bounded scale drops causal, warns if asked", {
   d   <- make_mock_vpro_data()
-  res <- gg_partial_varpro(d, nvars = 1, scale = "prob")
+  res <- suppressWarnings(gg_partial_varpro(d, nvars = 1, scale = "prob"))
   expect_s3_class(plot(res), "ggplot")
   expect_warning(plot(res, type = "causal"), regexp = "causal")
 })
@@ -551,7 +551,7 @@ test_that("gg_partial_varpro: scale='surv' stays in [0,1] near S = 1", {
   r <- suppressWarnings(gg_partial_varpro(object = vp, scale = "surv",
                                           time = tau))
   vals <- c(r$continuous$parametric, r$continuous$nonparametric,
-            r$categorical$parametric, r$categorical$nonparametric)
+            r$categorical[["parametric"]], r$categorical[["nonparametric"]])
   expect_true(length(vals) > 0L)
   expect_true(all(vals >= 0 & vals <= 1))
 })
@@ -586,6 +586,118 @@ test_that("surv_learner: continuous curve is averaged, then clamped", {
   expect_equal(sex$parametric, pmin(pmax(as.vector(d$sex$yhat.par), 0), 1))
 })
 
+## ── 4.0.0 "prob" anchored at each case's own level ──────────────────────────
+## partialpro returns every case at the cohort-mean intercept; "prob" restores
+## each case's level from the forest's OOB log-odds before plogis-then-average.
+test_that(".anchor_varpro_levels passes each curve through its anchor", {
+  d <- make_mock_vpro_data()
+  d$age$case <- seq_len(nrow(d$age$yhat.par))
+  d$sex$case <- seq_len(nrow(d$sex$yhat.par))
+  anchor <- seq(-2, 2, length.out = nrow(d$age$yhat.par))
+  out <- ggRandomForests:::.anchor_varpro_levels(d, anchor)
+  at_obs <- vapply(seq_along(anchor), function(i) {
+    stats::approx(out$age$xvirtual, out$age$yhat.par[i, ], d$age$xorg[i],
+                  rule = 2)$y
+  }, numeric(1))
+  expect_equal(at_obs, anchor)
+  ## Shape is kept: each row moves by one constant.
+  shift <- out$age$yhat.par - d$age$yhat.par
+  expect_equal(apply(shift, 1, stats::sd), rep(0, nrow(shift)))
+  expect_equal(out$age$yhat.nonpar - d$age$yhat.nonpar, shift)
+  expect_equal(out$age$yhat.causal, d$age$yhat.causal)
+  ## Binary variables carry per-case levels already and are left alone.
+  expect_identical(out$sex, d$sex)
+})
+
+test_that("gg_partial_varpro: 'prob' is anchored and tracks the true PD", {
+  skip_on_cran()
+  skip_if_not_installed("varPro")
+  set.seed(3)
+  n <- 300
+  d <- data.frame(x1 = stats::rnorm(n), x2 = stats::rnorm(n),
+                  x3 = stats::rnorm(n))
+  ## x2 spreads the cases' levels widely; that is where the swap bites.
+  d$y <- factor(stats::rbinom(n, 1, stats::plogis(1.5 * d$x1 + 3 * d$x2)))
+  vp  <- varPro::varpro(y ~ ., d, ntree = 100)
+  set.seed(1)
+  pp  <- varPro::partialpro(vp, xvar.names = "x1")
+  r   <- gg_partial_varpro(part_dta = pp, object = vp, scale = "prob")
+  expect_true(attr(r, "provenance")$anchored)
+  un  <- suppressWarnings(gg_partial_varpro(part_dta = pp, scale = "prob"))
+  grid  <- stats::quantile(d$x1, seq(0.05, 0.95, by = 0.1))
+  truth <- vapply(grid, function(g) mean(stats::plogis(1.5 * g + 3 * d$x2)),
+                  numeric(1))
+  err <- function(res) {
+    cont <- res$continuous
+    mean(abs(stats::approx(cont$variable, cont$parametric, grid,
+                           rule = 2)$y - truth))
+  }
+  expect_lt(err(r), err(un))
+  expect_lt(err(r), 0.06)
+})
+
+test_that("gg_partial_varpro: 'prob' without object warns and is not anchored", {
+  expect_warning(res <- gg_partial_varpro(make_mock_vpro_data(),
+                                          scale = "prob"),
+                 "cannot restore each case's level")
+  expect_false(attr(res, "provenance")$anchored)
+})
+
+test_that("gg_partial_varpro: a custom learner in ... is not anchored", {
+  skip_on_cran()
+  skip_if_not_installed("varPro")
+  set.seed(4)
+  dat <- data.frame(y = factor(rep(c("a", "b"), 60)),
+                    x1 = stats::rnorm(120), x2 = stats::rnorm(120))
+  vp  <- varPro::varpro(y ~ ., dat, ntree = 40, nvar = 2)
+  lrn <- function(newx) {
+    if (missing(newx)) newx <- vp$x
+    randomForestSRC::predict.rfsrc(vp$rf, newx, perf.type = "none")$predicted
+  }
+  r <- suppressMessages(gg_partial_varpro(object = vp, scale = "prob",
+                                          nvars = 1, learner = lrn))
+  expect_false(attr(r, "provenance")$anchored)
+})
+
+test_that("gg_partial_varpro: ignored ... cannot switch anchoring off", {
+  skip_on_cran()
+  skip_if_not_installed("varPro")
+  set.seed(6)
+  ## x1 carries real signal, so partialpro() reliably returns it. On a pure
+  ## noise outcome it can come back empty (seen on R 4.5), and part_dta = NULL
+  ## would then mean "compute it", which is not the path under test.
+  x1  <- stats::rnorm(120)
+  dat <- data.frame(y = factor(stats::rbinom(120, 1, stats::plogis(2 * x1))),
+                    x1 = x1, x2 = stats::rnorm(120))
+  vp  <- varPro::varpro(y ~ ., dat, ntree = 40, nvar = 2)
+  set.seed(1)
+  pp  <- varPro::partialpro(vp, xvar.names = "x1")
+  expect_true("x1" %in% names(pp))
+  base <- gg_partial_varpro(part_dta = pp, object = vp, scale = "prob")
+  ## With part_dta supplied, '...' is reported as ignored, so it must be.
+  r <- suppressWarnings(gg_partial_varpro(part_dta = pp, object = vp,
+                                          scale = "prob",
+                                          learner = function(newx) 0))
+  expect_true(attr(r, "provenance")$anchored)
+  expect_equal(r$continuous, base$continuous)
+})
+
+test_that(".varpro_oob_logodds keeps never-OOB cases via in-bag predictions", {
+  skip_on_cran()
+  skip_if_not_installed("varPro")
+  set.seed(1)
+  d  <- data.frame(y = factor(stats::rbinom(200, 1, 0.5)),
+                   x1 = stats::rnorm(200), x2 = stats::rnorm(200))
+  vp <- varPro::varpro(y ~ ., d, ntree = 5)
+  pr <- randomForestSRC::predict.rfsrc(vp$rf, perf.type = "none")
+  expect_true(anyNA(pr$predicted.oob))       # the case being tested
+  a  <- ggRandomForests:::.varpro_oob_logodds(vp, "1")
+  expect_false(anyNA(a))
+  ok <- !is.na(pr$predicted.oob[, "1"])
+  expect_equal(stats::plogis(a[ok]),
+               pmin(pmax(pr$predicted.oob[ok, "1"], 1e-3), 1 - 1e-3))
+})
+
 test_that("gg_partial_varpro: precomputed part_dta on 'surv' is not clamped", {
   ## A precomputed part_dta carries only the label; its values may not be on
   ## the S scale at all, so they pass through untouched.
diff --git a/vignettes/explainability.qmd b/vignettes/explainability.qmd
index 87f412c1..93ed5aa6 100644
--- a/vignettes/explainability.qmd
+++ b/vignettes/explainability.qmd
@@ -328,9 +328,16 @@ and the *order* of those two steps is a modelling choice:
   once. That is the probability for a subject at the mean log-odds.
 
 They disagree, and by more the more heterogeneous your cohort is, because the
-inverse logit bends. On a real fit whose per-subject log-odds carried a
-standard deviation near 4.5, a point reading 0.96 under `"prob_typical"` read
-0.74 under `"prob"`. A figure captioned as a percentage of patients wants
+inverse logit bends. In a simulation where a second variable spreads the
+subjects' log-odds to a standard deviation near 4, one point reads 0.13 under
+`"prob_typical"` and 0.35 under `"prob"`, against a true partial dependence of
+0.38. A figure captioned as a percentage of patients wants `"prob"`.
+
+One catch makes `"prob"` depend on the fit. `partialpro()` returns every
+subject at the cohort-mean level and keeps only each subject's slope, which on
+its own would put every subject at the average log-odds and make the two
+scales nearly agree. `gg_partial_varpro()` restores each subject's level from
+the forest's out-of-bag predictions, so pass `object =` whenever you want
 `"prob"`. See `?gg_partial_varpro` for the full argument.
 
 ## Which one do you reach for?
diff --git a/vignettes/varpro_precomputed.rds b/vignettes/varpro_precomputed.rds
index 4ec86acb..7d759a9b 100644
Binary files a/vignettes/varpro_precomputed.rds and b/vignettes/varpro_precomputed.rds differ