From 887393285cf73c5e3cfa00e39d71cbcc0dca6940 Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Wed, 10 Jun 2026 10:04:59 -0400 Subject: [PATCH 01/10] docs: v3.1.0 documentation sweep + gg_vimp fix (CRAN release) (#109) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * docs: v3.1.0 documentation-sweep design spec * docs: v3.1.0 spec — fix bugs surfaced by canonical-source reconciliation (with severity triage) * docs: v3.1.0 doc-sweep implementation plan * docs: deepen varPro-family roxygen (release-rules framing, vimp-vs-varpro) Co-Authored-By: Claude Sonnet 4.6 * docs: add the gg_vimp-vs-gg_varpro distinction to gg_vimp (Task 2 fix) Co-Authored-By: Claude Sonnet 4.6 * docs: deepen rfsrc partial/survival/rfsrc roxygen (ensemble + partial-dependence framing) * docs: address Task 2 review (drop invented first-person in gg_survival; non-positive VIMP wording) * docs: voice/drift cleanup on remaining roxygen topics Remove stale yvar @return item from gg_roc — the function returns sens/spec/pct (from calc_roc), not a yvar column per observation. Co-Authored-By: Claude Sonnet 4.6 * docs(vignette): deepen varpro — release-rules framing, refs Deepens all prose sections of varpro.qmd with release-rules/guided-splitting framing; adds Lee:2021 bib key (AOS 49:4) cited in PBC section; adds varProtools URL in Further Reading. Co-Authored-By: Claude Sonnet 4.6 * docs(vignette): regression — vimp-vs-varpro contrast, rfsrc ref Co-Authored-By: Claude Sonnet 4.6 * docs(vignette): survival — rfsrc ensemble framing, ref Co-Authored-By: Claude Sonnet 4.6 * docs(vignette): intro — voice/drift pass * docs(comments): correctness + gap pass on R/ source Fix a misleading AUC trapezoidal-rule comment in calc_roc.R (the old text introduced Δ(FPR) but the code uses Δspec; reworded to state the equivalence plainly). Remove a stale varPro-specific note from the categorical branch of gg_partial.R (plot.variable output has no connection to varPro one-hot encoding). Co-Authored-By: Claude Sonnet 4.6 * docs(NEWS): open v3.1.0 development heading (version unchanged) * docs(vignette): trim em-dashes in varpro per voice standard Co-Authored-By: Claude Sonnet 4.6 * fix(PR#109 review): gg_vimp positive flag (VIMP vs vimp case), Brier 0.25 precision * chore(release): prepare v3.1.0 for CRAN Bump DESCRIPTION + NEWS to 3.1.0 (CRAN never saw v3.0.0; jump from 2.7.3 is intentional) and finalize the v3.1.0 NEWS heading. Trim em-dashes and right-arrows from roxygen and code comments per the package voice standard (68 replacements across R/), then re-document so man/*.Rd carries no raw non-ASCII into the PDF manual build. Rewrite cran-comments.md for the 2.7.3 -> 3.1.0 submission: fold in the v3.0.0 feature layer, correct the local test env (R 4.6.0/darwin23). R CMD check --as-cran (with manual build, ggraph present): 0/0/0. Co-Authored-By: Claude Opus 4.8 * docs(cran-comments): correct v3.0.0 history v3.0.0 was submitted but did not complete the CRAN review cycle; 3.1.0 supersedes it. Prior wording said it was never submitted. Co-Authored-By: Claude Opus 4.8 * docs(cran-comments): note v3.0.0 pretests were clean, hold was heuristic Per the release handoff: tell the CRAN reviewer the 2026-05-28 v3.0.0 submission cleared incoming pretests on Windows + Debian (0/0/0) and the auto-hold looked like a version-jump/Depends-to-Imports heuristic, not a defect, in case the same heuristic flags v3.1.0. Co-Authored-By: Claude Opus 4.8 * docs(dev/plans): mark v3.0.0-held release mechanics as superseded The plan/design docs described the held workflow (keep Version 3.0.0, merge only after CRAN accepts v3.0.0, cut 3.1.0 at a post-acceptance RC). v3.0.0 lapsed un-reviewed, so we ship 3.1.0 directly. Banners note this; the documentation-content plan is unchanged. Addresses Copilot review. Co-Authored-By: Claude Opus 4.8 --------- Co-authored-by: Claude Sonnet 4.6 --- DESCRIPTION | 4 +- NEWS.md | 19 +- R/calc_roc.R | 7 +- R/gg_beta_varpro.R | 43 +- R/gg_brier.R | 47 ++- R/gg_isopro.R | 8 +- R/gg_ivarpro.R | 34 +- R/gg_partial.R | 26 +- R/gg_partial_rfsrc.R | 31 +- R/gg_partial_varpro.R | 5 +- R/gg_rfsrc.R | 27 +- R/gg_roc.R | 4 +- R/gg_survival.R | 31 +- R/gg_udependent.R | 10 +- R/gg_variable.R | 4 +- R/gg_varpro.R | 10 +- R/gg_vimp.R | 42 +- R/kaplan.R | 2 +- R/plot.gg_beta_varpro.R | 6 +- R/plot.gg_brier.R | 19 +- R/plot.gg_error.R | 2 +- R/plot.gg_isopro.R | 10 +- R/plot.gg_ivarpro.R | 5 +- R/plot.gg_partial.R | 43 +- R/plot.gg_partial_varpro.R | 4 +- R/plot.gg_rfsrc.R | 22 +- R/plot.gg_survival.R | 15 +- R/plot.gg_udependent.R | 6 +- R/plot.gg_variable.R | 10 +- R/plot.gg_varpro.R | 4 +- R/plot.gg_vimp.R | 13 + R/print_helpers.R | 2 +- R/print_methods.R | 2 +- R/ribbon_style.R | 8 +- R/varpro_feature_names.R | 4 +- cran-comments.md | 40 +- .../2026-05-29-v3.1.0-doc-sweep-design.md | 174 ++++++++ dev/plans/2026-05-29-v3.1.0-doc-sweep-plan.md | 384 ++++++++++++++++++ man/gg_beta_varpro.Rd | 35 +- man/gg_brier.Rd | 44 +- man/gg_isopro.Rd | 8 +- man/gg_ivarpro.Rd | 32 +- man/gg_partial.Rd | 25 +- man/gg_partial_rfsrc.Rd | 32 +- man/gg_partial_varpro.Rd | 5 +- man/gg_rfsrc.rfsrc.Rd | 21 +- man/gg_roc.rfsrc.Rd | 2 +- man/gg_survival.Rd | 30 +- man/gg_udependent.Rd | 10 +- man/gg_varpro.Rd | 8 +- man/gg_vimp.Rd | 25 +- man/plot.gg_beta_varpro.Rd | 6 +- man/plot.gg_brier.Rd | 20 +- man/plot.gg_isopro.Rd | 10 +- man/plot.gg_ivarpro.Rd | 5 +- man/plot.gg_partial.Rd | 16 +- man/plot.gg_partial_rfsrc.Rd | 28 +- man/plot.gg_partial_varpro.Rd | 4 +- man/plot.gg_rfsrc.Rd | 21 +- man/plot.gg_survival.Rd | 16 +- man/plot.gg_udependent.Rd | 6 +- man/plot.gg_varpro.Rd | 4 +- man/plot.gg_vimp.Rd | 15 +- tests/testthat/test_gg_vimp.R | 24 ++ vignettes/ggRandomForests-regression.qmd | 20 +- vignettes/ggRandomForests-survival.qmd | 85 +++- vignettes/ggRandomForests.bib | 11 + vignettes/ggRandomForests.qmd | 8 +- vignettes/varpro.qmd | 263 +++++++++--- 69 files changed, 1575 insertions(+), 391 deletions(-) create mode 100644 dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md create mode 100644 dev/plans/2026-05-29-v3.1.0-doc-sweep-plan.md diff --git a/DESCRIPTION b/DESCRIPTION index e2085c82..0b19ec4c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: ggRandomForests Type: Package Title: Visually Exploring Random Forests -Version: 3.0.0 -Date: 2026-05-28 +Version: 3.1.0 +Date: 2026-06-10 Authors@R: person("John", "Ehrlinger", role = c("aut", "cre"), email = "john.ehrlinger@gmail.com") diff --git a/NEWS.md b/NEWS.md index 7a03fd47..d982a256 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,22 @@ Package: ggRandomForests -Version: 3.0.0 +Version: 3.1.0 + +ggRandomForests v3.1.0 +====================== +* Fix: `gg_vimp()` for single-outcome rfsrc forests now correctly flags + variables with non-positive VIMP in the `positive` column (affecting + plot coloring). The column was named `VIMP` (uppercase) in single-outcome + fits but the flag check accessed `$vimp` (lowercase), leaving `positive` + stuck at `TRUE` for all variables. Surfaced by the Copilot review on + PR #109. +* Documentation pass. Deepened the varPro-family and rfsrc + importance/partial/survival help pages against the upstream + randomForestSRC and varPro documentation, and made the line between + `gg_vimp()` (permutation, Breiman-Cutler importance) and `gg_varpro()` + (varPro release-rule importance) explicit and cross-linked. Vignette + prose deepened with the same framing; one-line code-comment fixes; + fixed a stale `@return` in `gg_roc()` (documented a `yvar` column the + function does not return). No user-facing behaviour change. ggRandomForests v3.0.0 ====================== diff --git a/R/calc_roc.R b/R/calc_roc.R index da52f7c7..6b6e7285 100644 --- a/R/calc_roc.R +++ b/R/calc_roc.R @@ -206,7 +206,7 @@ calc_roc <- function(object, } # Build the sensitivity/specificity table for a single class index k. -# Plain lapply (not mclapply) — per-threshold work is a single table() +# Plain lapply (not mclapply): per-threshold work is a single table() # + a few arithmetic ops (microseconds); fork overhead would dominate, # and the closure-scope fragility caused the earlier xtabs/Windows # failure. Returns a data.frame with columns sens, spec, pct. @@ -320,8 +320,9 @@ calc_auc <- function(x) { # Sort in decreasing specificity so FPR = 1-spec increases monotonically x <- x[order(x$spec, decreasing = TRUE), ] - # Δ(FPR) = -(Δspec) — spec decreases, so (spec[i] - spec[i+1]) > 0 - # Average height of trapezoid = (sens[i] + sens[i+1]) / 2 + # Trapezoid area = sens_avg * Δspec, where Δspec = spec[i] - spec[i+1] > 0 + # (spec decreases left-to-right). This equals sens_avg * Δ(1-FPR), which + # gives the standard AUC = ∫ sens d(FPR) with a positive sign. auc <- (x$sens + shift(x$sens)) / 2 * (x$spec - shift(x$spec)) # nolint: object_usage_linter sum(auc, na.rm = TRUE) } diff --git a/R/gg_beta_varpro.R b/R/gg_beta_varpro.R index 333892bc..4648eb07 100644 --- a/R/gg_beta_varpro.R +++ b/R/gg_beta_varpro.R @@ -11,11 +11,22 @@ #' `beta.varpro()` step once and reuse the result. #' #' @section What this is doing: -#' For each rule (a tree-branch pair) in the forest, [varPro::beta.varpro()] -#' fits a one-predictor lasso regression of the response on the released -#' variable's values, restricted to the OOB observations inside the rule's -#' region. The wrapper aggregates those per-rule coefficients into one -#' number per variable. +#' Think of the varPro release-rule mechanism as asking: "given a region of +#' the feature space that the forest carved out, what changes when I remove +#' the constraint on this one variable and let observations leave?" The +#' standard importance answer (from [gg_varpro()]) measures that change as a +#' z-scored contrast between local estimators: no synthetic data, no +#' permutation. \code{beta.varpro()} asks the same question with a different +#' ruler: for each rule (a tree-branch pair), it fits a one-predictor lasso +#' regression of the response on the released variable's values, restricted +#' to the OOB observations inside the rule's region. The wrapper aggregates +#' those per-rule coefficients into one number per variable. +#' +#' The key distinction from [gg_vimp()], which measures Breiman-Cutler +#' permutation importance by perturbing a variable's values and watching OOB +#' error climb, is that neither [gg_varpro()] nor \code{gg_beta_varpro()} +#' touches the data synthetically: all contrasts are between real subsets +#' defined by the forest's rules. #' #' @section What `imp` actually is (pedantic, because the column name is misleading): #' The `imp` column on `beta.varpro()`'s `$results` is **not** a @@ -63,7 +74,7 @@ #' #' @section What you use this for: #' Picking variables when local effects matter more than aggregate -#' split-strength contribution. Compare side-by-side with [gg_varpro()] — +#' split-strength contribution. Compare side-by-side with [gg_varpro()]: #' a variable that scores high here but low in `gg_varpro` is one whose #' local linear effect inside many rules is real even though its #' release-rule contrast is modest. @@ -92,7 +103,7 @@ #' class. #' #' **Binary default**: `which_class = NULL` resolves to the *last* -#' factor level of the response — the positive-class convention used +#' factor level of the response, the positive-class convention used #' by `glm` and `gg_roc`. For a 30-day-mortality outcome with levels #' `c("no", "yes")`, that means the wrapper shows you `"yes"` (the #' event) by default. @@ -118,7 +129,7 @@ #' @section Reproducibility: #' Byte-for-byte agreement between cached (`beta_fit = b`) and uncached #' (`beta_fit = NULL`) outputs requires that `b` was computed by -#' `beta.varpro(object, ...)` on the same `object` — `set.seed()` alone is +#' `beta.varpro(object, ...)` on the same `object`; `set.seed()` alone is #' not sufficient, because `beta.varpro`'s internal `cv.glmnet` fits can #' pick slightly different folds across separate calls. Reuse `beta_fit` #' when reproducibility matters. @@ -132,15 +143,15 @@ #' @param ... Forwarded to [varPro::beta.varpro()] when `beta_fit = NULL`; #' ignored otherwise (with a warning). Documented forwardables: `use.cv`, #' `use.1se`, `nfolds`, `maxit`, `thresh`, `max.rules.tree`, `max.tree`. -#' @param cutoff Selection threshold on `beta_mean`. `NULL` (default) → +#' @param cutoff Selection threshold on `beta_mean`. `NULL` (default) means #' `mean(beta_mean)` across released variables. Numeric scalar otherwise. #' @param beta_fit Optional pre-computed [varPro::beta.varpro()] result for -#' the same `object`. `NULL` (default) → the wrapper runs `beta.varpro()` +#' the same `object`. `NULL` (default) means the wrapper runs `beta.varpro()` #' itself. When supplied, must be a `varpro`-class object whose `$results` #' has columns `tree / branch / variable / n.oob / imp`. #' @param which_class For a classification fit, name of a single response #' level to subset on. `NULL` (default) returns all classes (binary fits -#' resolve to the *last* factor level — the positive-class convention +#' resolve to the *last* factor level, the positive-class convention #' used by `glm` and `gg_roc`). Ignored with a warning on regression #' fits. #' @@ -153,7 +164,7 @@ #' the same row order. `which_class` (or the binary default #' last-factor-level) collapses the output to a single class. #' -#' @seealso [gg_varpro()], [plot.gg_beta_varpro()], [varPro::beta.varpro()]. +#' @seealso [gg_varpro()], [gg_vimp()], [plot.gg_beta_varpro()], [varPro::beta.varpro()]. #' #' @examples #' \donttest{ @@ -208,7 +219,7 @@ gg_beta_varpro.varpro <- function(object, ..., cutoff = NULL, which_class <- NULL } - # Capture use.cv from `...` here (NOT inside the internals — the dots + # Capture use.cv from `...` here (NOT inside the internals; the dots # don't pass through to the internal frame). dots_use_cv <- if (is.null(beta_fit)) isTRUE(list(...)$use.cv) else NA @@ -372,7 +383,7 @@ gg_beta_varpro.varpro <- function(object, ..., cutoff = NULL, ord_names <- names(sort(beta_mean_total, decreasing = TRUE)) lvl <- rev(ord_names) - # Per-class aggregation — long format + # Per-class aggregation: long format rows <- list() for (k in seq_len(n_classes)) { col <- imp_cols[k] @@ -452,8 +463,8 @@ gg_beta_varpro.varpro <- function(object, ..., cutoff = NULL, class(base) <- c("gg_beta_varpro", "data.frame") # Build provenance with shape-stable cutoff: - # regr → c("regr" = NA_real_) - # class → named NA_real_ vector, one entry per class level + # regr gives c("regr" = NA_real_) + # class gives named NA_real_ vector, one entry per class level if (fam == "class") { class_levels <- .class_levels_from_varpro(object) cutoff_empty <- stats::setNames(rep(NA_real_, length(class_levels)), diff --git a/R/gg_brier.R b/R/gg_brier.R index 6592588b..7559df61 100644 --- a/R/gg_brier.R +++ b/R/gg_brier.R @@ -16,24 +16,37 @@ #' #' The Brier score asks a familiar question of any probabilistic forecast: #' how far did the predicted probability sit from what actually happened? -#' For a survival forest the forecast is the predicted survival probability, -#' and the score is computed at each event time, so the result is a curve -#' rather than a single number -- lower is better, at every time. +#' For a survival forest the forecast is the predicted survival probability +#' at a given moment, and the "what happened" is whether the subject was +#' still alive at that moment. The score is computed at every event time, +#' so you get a curve rather than a single number -- lower is better +#' everywhere. A perfectly calibrated forest that predicts \code{0} for +#' every subject who died and \code{1} for every subject who survived would +#' score \code{0}; a forest that predicts \code{0.5} for everyone scores +#' roughly \code{0.25} regardless of the true outcome -- that is the +#' "uninformative" ceiling. #' -#' This function extracts that time-resolved Brier score for a survival -#' forest grown with \code{randomForestSRC}, both overall and split by -#' mortality-risk quartile. It also returns the continuous ranked -#' probability score (CRPS), which is the Brier score integrated over time -#' and divided by elapsed time -- a running average of the curve so far. +#' This function extracts the time-resolved Brier score for a survival +#' forest grown with \code{randomForestSRC}, both overall and broken down +#' by mortality-risk quartile (lowest-risk to highest-risk subjects). It +#' also returns the continuous ranked probability score (CRPS) -- the Brier +#' score integrated over time and divided by elapsed time, a running average +#' that summarises calibration up to each point on the time axis. #' -#' @details This wraps \code{\link[randomForestSRC]{get.brier.survival}} and -#' rebuilds the quartile decomposition and running CRPS from the returned -#' \code{brier.matx} and \code{mort} components, following the computation -#' in the internal \code{plot.survival} function of \pkg{randomForestSRC}. -#' Right-censored data make a plain Brier score biased, so the score uses -#' inverse-probability-of-censoring weighting. The censoring distribution -#' is estimated either by Kaplan-Meier (\code{cens.model = "km"}, the -#' default) or by a separate censoring forest (\code{cens.model = "rfsrc"}). +#' @details +#' Because subjects are right-censored, a plain Brier score is biased: +#' censored subjects contribute no outcome information yet still inflate the +#' denominator. The score here uses inverse-probability-of-censoring +#' weighting (IPCW), which up-weights uncensored observations to compensate. +#' The censoring distribution is estimated either by Kaplan-Meier +#' (\code{cens.model = "km"}, the default) or by a separate censoring +#' forest (\code{cens.model = "rfsrc"}) when the censoring mechanism is +#' itself covariate-dependent. +#' +#' Internally, this wraps \code{\link[randomForestSRC]{get.brier.survival}} +#' and rebuilds the quartile decomposition and running CRPS from the returned +#' \code{brier.matx} and \code{mort} components, following the approach in +#' the internal \code{plot.survival} of \pkg{randomForestSRC}. #' #' @param object A fitted \code{\link[randomForestSRC]{rfsrc}} survival #' forest (\code{object$family == "surv"}). @@ -143,7 +156,7 @@ gg_brier.rfsrc <- function(object, bs_quartile <- vapply(seq_len(4), function(k) { in_bin <- mort > mort_breaks[k] & mort <= mort_breaks[k + 1] if (!any(in_bin, na.rm = TRUE)) { - # Empty bin — can occur when mort has ties at a quantile boundary. + # Empty bin: can occur when mort has ties at a quantile boundary. return(rep(NA_real_, nrow(bs_df))) } colMeans(brier_matx[in_bin, , drop = FALSE], na.rm = TRUE) diff --git a/R/gg_isopro.R b/R/gg_isopro.R index 075efa35..59384e08 100644 --- a/R/gg_isopro.R +++ b/R/gg_isopro.R @@ -20,7 +20,7 @@ #' a typical observation sits in the dense middle of the feature cloud and #' takes many splits to isolate, while an unusual observation sits out #' near an edge and gets cut off after only a few. So \strong{the depth at -#' which an observation is isolated is a proxy for how typical it is} — +#' which an observation is isolated is a proxy for how typical it is}: #' shallow depth means anomalous, deep depth means ordinary. Average a #' single observation's depth across many trees and the noise washes out, #' leaving a stable per-observation rank. @@ -68,7 +68,7 @@ #' against a fitted model and compare the test scores to the training #' distribution. #' } -#' The score is a \emph{rank}, not a probability of being an outlier — two +#' The score is a \emph{rank}, not a probability of being an outlier: two #' observations with \code{howbad = 0.92} are both unusual, not "92\% #' likely to be anomalous". Pick a cutoff by looking at where the elbow #' rises; \code{\link{plot.gg_isopro}} can annotate either a score @@ -86,7 +86,7 @@ #' \code{howbad} (where \emph{higher} is more anomalous). The wrapper #' exposes both conventions so nothing is hidden: #' \itemize{ -#' \item \code{case.depth} carries varPro's native polarity — \emph{lower +#' \item \code{case.depth} carries varPro's native polarity, \emph{lower #' = more anomalous}. This is the unmodified output of #' \code{predict(object, newdata, quantiles = FALSE)}. Use it to #' cross-reference against raw varPro output. @@ -128,7 +128,7 @@ #' order as the rows of the data passed to #' \code{\link[varPro]{isopro}}.} #' \item{case.depth}{Numeric; mean isolation depth across the forest. -#' Lower means the observation was isolated quickly — more +#' Lower means the observation was isolated quickly, so more #' anomalous.} #' \item{howbad}{Numeric in \code{[0, 1]}; the \code{case.depth} #' values pushed through their own empirical CDF and flipped so diff --git a/R/gg_ivarpro.R b/R/gg_ivarpro.R index 837e6b0f..b8501705 100644 --- a/R/gg_ivarpro.R +++ b/R/gg_ivarpro.R @@ -10,15 +10,27 @@ #' `ivarpro()` call. #' #' @section What this is doing: -#' `ivarpro()` walks the varPro forest's rules and, for each -#' (observation, variable) pair, computes a scaled per-rule -#' contribution to predicting that observation. Per-rule LOO removes -#' the observation from its own rule before scoring. Per-region -#' scaling (`scale = "local"`, default) standardises the contribution -#' by the rule's local response standard deviation so values are -#' comparable across rules of different size. Aggregating those -#' per-rule scores into one number per (obs, variable) pair gives the -#' `local_imp` cell. +#' The varPro framework builds importance from release rules: for a given +#' rule region, it compares a local estimator inside that region to what +#' the estimator becomes after the constraint on the tested variable is +#' removed ("released"). That contrast is summed over many rules and trees +#' to get a global z-score: the quantity [gg_varpro()] shows. What +#' `ivarpro()` adds is a per-observation view of the same mechanism. +#' +#' Concretely: `ivarpro()` walks the forest's rules and, for each +#' (observation, variable) pair, computes a scaled per-rule contribution +#' to predicting that observation. Per-rule LOO removes the observation +#' from its own rule before scoring, so the contribution is not inflated +#' by the observation having helped define the region. Per-region scaling +#' (`scale = "local"`, default) standardises the contribution by the +#' rule's local response standard deviation so values are comparable +#' across rules of different size. Aggregating those per-rule scores into +#' one number per (obs, variable) pair gives the `local_imp` cell. +#' +#' No permutation, no synthetic data: the contrast is always between real +#' subsets of the observed data, defined by the forest's own rules. This +#' is the same no-synthetic-features property that distinguishes +#' [gg_varpro()] from [gg_vimp()]'s Breiman-Cutler permutation importance. #' #' @section What `local_imp` actually is (pedantic): #' `local_imp[i, v]` is the **scaled aggregated rule contribution** of @@ -126,7 +138,7 @@ #' `mean(|local_imp|)` descending across all rows (the unified #' ranking axis shared across facets / panels). #' -#' @seealso [gg_varpro()], [gg_beta_varpro()], [varPro::ivarpro()]. +#' @seealso [gg_varpro()], [gg_vimp()], [gg_beta_varpro()], [varPro::ivarpro()]. #' #' @examples #' \donttest{ @@ -415,7 +427,7 @@ gg_ivarpro.varpro <- function(object, ..., which_obs = NULL, } # Unified factor-level ordering across all (obs, class), REVERSED so the - # most-important variable lands at the TOP after coord_flip — shared + # most-important variable lands at the TOP after coord_flip; shared # across every class facet for alignment. agg <- tapply(abs(long$local_imp), long$variable, mean, na.rm = TRUE) ord_names <- names(sort(agg, decreasing = TRUE)) diff --git a/R/gg_partial.R b/R/gg_partial.R index f9df1d24..f3bdfbbf 100644 --- a/R/gg_partial.R +++ b/R/gg_partial.R @@ -1,11 +1,23 @@ ##============================================================================= #' Split partial dependence data into continuous or categorical datasets #' -#' Takes the list returned by \code{rfsrc::plot.variable(partial = TRUE)} and -#' separates the variables into two data frames: one for continuous predictors -#' and one for categorical (factor-like) predictors. The split is controlled -#' by \code{cat_limit}: variables with more unique x-values than this threshold -#' are treated as continuous; all others are categorical. +#' A partial dependence curve answers a what-if question about a forest: hold +#' every other predictor at its observed value, sweep one of them across its +#' range, and watch how the ensemble prediction moves. Marginalized over the +#' joint distribution of the other variables, the resulting curve isolates the +#' average effect of the swept predictor alone. +#' +#' \code{gg_partial} handles the bookkeeping step after you've already called +#' \code{rfsrc::plot.variable(partial = TRUE)}: it takes the list that function +#' returns and separates the variables into two tidy data frames -- one for +#' continuous predictors (plotted as lines) and one for categorical predictors +#' (plotted as bar charts). The split is controlled by \code{cat_limit}: +#' variables with more unique x-values than this threshold are treated as +#' continuous; all others are categorical. +#' +#' If you'd rather skip the \code{plot.variable} step and pass the fitted +#' forest directly, see \code{\link{gg_partial_rfsrc}}, which calls +#' \code{partial.rfsrc} for you. #' #' @param part_dta partial plot data from \code{rfsrc::plot.variable} #' @param nvars how many of the partial plot variables to calculate @@ -31,7 +43,7 @@ #' rf <- randomForestSRC::rfsrc(Ozone ~ ., data = airq, ntree = 50) #' #' ## Compute partial dependence via plot.variable (show.plots = FALSE to -#' ## suppress the base-graphics output — we only want the data) +#' ## suppress the base-graphics output, we only want the data) #' pv <- randomForestSRC::plot.variable(rf, partial = TRUE, #' show.plots = FALSE) #' @@ -76,8 +88,6 @@ gg_partial <- function(part_dta, } else { ## ---- Categorical variable: few unique x values ------------------- - ## VarPro works with logical or continuous only; factors are - ## one-hot encoded internally in the varPro call. ## Normalize to character so bind_rows sees a consistent type; we'll ## re-factor within each feature after stacking. x_chr <- as.character(x_vals) diff --git a/R/gg_partial_rfsrc.R b/R/gg_partial_rfsrc.R index 9385fd68..9b70302d 100644 --- a/R/gg_partial_rfsrc.R +++ b/R/gg_partial_rfsrc.R @@ -1,15 +1,26 @@ ##============================================================================= #' Partial dependence data from an rfsrc model #' -#' A partial dependence curve answers a what-if question: hold the rest of -#' the predictors as they are, sweep one of them across its range, and watch -#' how the forest's prediction moves. This function builds those curves for -#' one or more predictors by calling -#' \code{\link[randomForestSRC]{partial.rfsrc}} for you, then splits the -#' result into separate data frames for continuous and categorical -#' variables. Unlike \code{\link{gg_partial}}, there is no separate -#' \code{plot.variable} step -- pass the fitted \code{rfsrc} object straight -#' in. +#' A partial dependence curve marginalizes the forest's prediction over all +#' other predictors: for each evaluation point of the target variable, the +#' forest scores every training observation with that value substituted in, +#' then averages the result. What you get is the average effect of the target +#' variable after "integrating out" the rest -- a curve that would be flat if +#' the variable carried no signal. +#' +#' This function builds those curves for one or more predictors by calling +#' \code{\link[randomForestSRC]{partial.rfsrc}} and then tidy-stacking the +#' results into separate data frames for continuous and categorical variables. +#' Unlike \code{\link{gg_partial}} (which wraps \code{plot.variable}), you +#' pass the fitted \code{rfsrc} object directly -- no intermediate +#' \code{plot.variable} step. +#' +#' For survival forests, the marginalized quantity depends on +#' \code{partial.type}: survival probability (\code{"surv"}), cumulative +#' hazard function (\code{"chf"}), or expected mortality (\code{"mort"}). +#' You can request the curve at one or more time horizons via +#' \code{partial.time}; the resulting data have a \code{time} column so the +#' plot layers them as separate coloured lines. #' #' @section Survival forests and \code{partial.time}: #' \code{\link[randomForestSRC]{partial.rfsrc}} expects every value in @@ -49,7 +60,7 @@ #' \code{rf_model$xvar}. All column names must match \code{rf_model$xvar.names}. #' @param partial.time Numeric vector of desired time points for survival #' forests (ignored for regression/classification). Values are automatically -#' snapped to the nearest entry in \code{rf_model$time.interest} — see the +#' snapped to the nearest entry in \code{rf_model$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 diff --git a/R/gg_partial_varpro.R b/R/gg_partial_varpro.R index f088edbf..7851c21c 100644 --- a/R/gg_partial_varpro.R +++ b/R/gg_partial_varpro.R @@ -52,9 +52,9 @@ #' @section What you use this for: #' \itemize{ #' \item read the marginal shape of a relationship the varpro model -#' found important — monotone, threshold, U-shape, flat; +#' found important: monotone, threshold, U-shape, flat; #' \item compare the three partialpro estimators on the same variable -#' and flag the ones where parametric and nonparametric disagree — +#' and flag the ones where parametric and nonparametric disagree, #' those are the candidates for closer inspection; #' \item report a survival partial dependence on the probability or #' cumulative-hazard scale (\code{scale = "surv"} or \code{"chf"}) @@ -128,6 +128,7 @@ #' \bold{2}(3), 841--860. \doi{10.1214/08-AOAS169}. #' #' @seealso \code{\link{plot.gg_partial_varpro}}, +#' \code{\link{gg_varpro}}, \code{\link{gg_vimp}}, #' \code{\link{gg_partialpro}} (deprecated), #' \code{\link{gg_partial_rfsrc}}, \code{\link{varpro_feature_names}} #' diff --git a/R/gg_rfsrc.R b/R/gg_rfsrc.R index bd5fe891..802bfa02 100644 --- a/R/gg_rfsrc.R +++ b/R/gg_rfsrc.R @@ -14,9 +14,24 @@ #' #' Predicted response data object #' -#' Extracts the predicted response values from the -#' \code{\link[randomForestSRC]{rfsrc}} object, and formats data for plotting -#' the response using \code{\link{plot.gg_rfsrc}}. +#' Every tree in a random forest makes its own prediction, and the forest's +#' "ensemble" prediction is the average across all trees. The out-of-bag +#' (OOB) variant averages only over the trees that did not include a given +#' observation in their bootstrap sample -- a built-in cross-validation +#' estimate that requires no held-out test set. \code{gg_rfsrc} pulls those +#' ensemble predictions out of the fitted forest and arranges them for plotting +#' with \code{\link{plot.gg_rfsrc}}. +#' +#' The structure of the returned data depends on the forest family. For a +#' regression forest you get a scatter of OOB predicted values against the +#' observed response. For classification you get predicted class probabilities +#' alongside the observed class label. For a survival forest you get the +#' ensemble survival function (or cumulative hazard, or mortality, controlled +#' by \code{surv_type}) at each unique event time -- one curve per +#' observation -- which together trace the range of predicted risk in the +#' cohort. Pass \code{conf.int} to add pointwise bootstrap confidence bands +#' around the mean survival curve, or \code{by} to stratify all of the above +#' by a predictor group. #' #' @param object A fitted \code{\link[randomForestSRC]{rfsrc}} or #' \code{\link[randomForest]{randomForest}} object. @@ -190,7 +205,7 @@ gg_rfsrc.rfsrc <- function(object, # nolint: cyclocomp_linter if (object$family == "class") { # For binary classification rfsrc stores exactly two probability columns # (one per class); we drop the first (the "negative" class probability) - # since it is redundant — prob(class 2) = 1 - prob(class 1). + # since it is redundant: prob(class 2) = 1 - prob(class 1). # Multi-class forests (3+ classes) keep all columns. if (oob) { gg_dta <- @@ -286,7 +301,7 @@ gg_rfsrc.rfsrc <- function(object, # nolint: cyclocomp_linter arg_list$conf.int } - # Accept a single coverage probability (e.g. 0.95 → two-sided tails + # Accept a single coverage probability (e.g. 0.95 gives two-sided tails # at 0.025 and 0.975) or a length-2 vector of explicit quantile probs. if (length(level) == 1) { if (level > 1) { @@ -299,7 +314,7 @@ gg_rfsrc.rfsrc <- function(object, # nolint: cyclocomp_linter level_set <- sort(level) } - # Number of bootstrap resamples — default to the number of observations. + # Number of bootstrap resamples: default to the number of observations. if (is.null(arg_list$bs.sample)) { bs_samples <- nrow(gg_dta) } else { diff --git a/R/gg_roc.R b/R/gg_roc.R index bf54ec19..fc77df10 100644 --- a/R/gg_roc.R +++ b/R/gg_roc.R @@ -56,7 +56,7 @@ #' \describe{ #' \item{sens}{Sensitivity (true positive rate) at the threshold.} #' \item{spec}{Specificity (true negative rate) at the threshold.} -#' \item{yvar}{The observed class label for each observation.} +#' \item{pct}{The probability threshold used for that row.} #' } #' Pass it to \code{\link{calc_auc}} for the area under the curve. #' @@ -105,7 +105,7 @@ gg_roc.rfsrc <- function(object, which_outcome, oob = TRUE, per_class = FALSE, ...) { # Validate that the object was grown with randomForestSRC (grow or predict) - # or is a randomForest object — the two supported class signatures. + # or is a randomForest object: the two supported class signatures. if (sum(inherits(object, c("rfsrc", "grow"), TRUE) == c(1, 2)) != 2 && sum(inherits(object, c("rfsrc", "predict"), TRUE) == c(1, 2)) != 2 && !inherits(object, "randomForest")) { diff --git a/R/gg_survival.R b/R/gg_survival.R index 2551a21d..593bddc3 100644 --- a/R/gg_survival.R +++ b/R/gg_survival.R @@ -14,16 +14,29 @@ #' #' Nonparametric survival estimates. #' -#' @details \code{gg_survival} is an S3 generic for generating nonparametric -#' survival estimates. It dispatches on the class of its first argument: +#' @details +#' Comparing the forest's ensemble survival curve to the marginal +#' Kaplan-Meier baseline is a quick sanity check: if they diverge the forest +#' has found structure the predictors carry; if they track each other closely +#' the predictors may add little. \code{gg_survival} computes +#' the nonparametric baseline -- the Kaplan-Meier or Nelson-Aalen estimate -- +#' so you can place it on the same canvas as the forest predictions from +#' \code{\link{gg_rfsrc}}. +#' +#' \code{gg_survival} is an S3 generic that dispatches on the class of its +#' first argument: #' #' \describe{ -#' \item{\code{rfsrc}}{Extracts the response data from the fitted forest and -#' delegates to \code{\link{kaplan}}. Use the \code{by} argument to -#' stratify on a predictor stored in the model's \code{xvar} slot.} -#' \item{default}{Accepts raw survival data columns via the \code{interval}, -#' \code{censor}, \code{by}, and \code{data} arguments, delegating to -#' either \code{\link{kaplan}} (default) or \code{\link{nelson}}.} +#' \item{\code{rfsrc}}{Extracts the outcome columns from the fitted +#' forest's \code{$yvar} slot (time in column 1, event indicator in +#' column 2) and delegates to \code{\link{kaplan}}. Use \code{by} to +#' stratify on a predictor from \code{$xvar}: you get one +#' Kaplan-Meier curve per group, ready to compare against the forest's +#' group-specific ensemble curves.} +#' \item{default}{Accepts raw survival columns directly via +#' \code{interval}, \code{censor}, and \code{data}. Delegates to +#' \code{\link{kaplan}} (the default) or \code{\link{nelson}} depending +#' on \code{type}.} #' } #' #' @param object For the \code{rfsrc} method: a fitted @@ -52,7 +65,7 @@ #' @seealso \code{\link{plot.gg_survival}} #' #' @examples -#' ## -------- pbc data (default method — raw data columns) +#' ## -------- pbc data (default method, raw data columns) #' data(pbc, package = "randomForestSRC") #' pbc$time <- pbc$days / 364.25 #' diff --git a/R/gg_udependent.R b/R/gg_udependent.R index 70535c6a..4466610d 100644 --- a/R/gg_udependent.R +++ b/R/gg_udependent.R @@ -13,8 +13,8 @@ #' the same region-release contrasts varpro uses for supervised #' importance to ask, "which variables explain the structure in the #' data?" The lasso-driven variant frames each region-release contrast -#' as a classification task — does an observation belong to the region -#' or to its release? — and fits a lasso logistic regression with the +#' as a classification task (does an observation belong to the region +#' or to its release?) and fits a lasso logistic regression with the #' other variables as predictors. The coefficient on variable \eqn{j} #' in the model for variable \eqn{i}'s region-release contrast is the #' entry \eqn{I[i, j]} of the matrix \code{varPro::get.beta.entropy()} @@ -24,7 +24,7 @@ #' \eqn{i}'s region from its release". A large \eqn{I[i, j]} says #' \eqn{j} carries information about the structure varpro picked up in #' \eqn{i}. \code{varPro::sdependent} thresholds that matrix at a -#' user-chosen cut and returns the set of "signal" variables — the +#' user-chosen cut and returns the set of "signal" variables: the #' nodes with high enough out-degree to be worth keeping. We pass the #' threshold through to \code{sdependent} and use the same matrix to #' weight the edges of the resulting graph. @@ -50,7 +50,7 @@ #' @section What you use this for: #' \itemize{ #' \item screen a wide unsupervised dataset for the small set of -#' variables UVarPro thinks are carrying the signal — the nodes +#' variables UVarPro thinks are carrying the signal: the nodes #' with high degree, or those flagged \code{selected = TRUE}; #' \item spot clusters of mutually dependent variables (hubs and the #' spokes around them) that may be measuring the same underlying @@ -59,7 +59,7 @@ #' looking at how their dependency graphs change. #' } #' An edge in this graph is a statistical dependency in the unsupervised -#' decomposition of the data — it is not a causal arrow. A high +#' decomposition of the data. It is not a causal arrow. A high #' \eqn{I[i, j]} says \eqn{j} predicts \eqn{i}'s region membership, #' not that \eqn{j} causes \eqn{i}. #' diff --git a/R/gg_variable.R b/R/gg_variable.R index 75fa1646..7981c015 100644 --- a/R/gg_variable.R +++ b/R/gg_variable.R @@ -271,7 +271,7 @@ gg_variable.randomForest <- function(object, ...) { arg_list <- list(...) - # randomForest uses object$votes (OOB vote matrix) unconditionally — it is the + # randomForest uses object$votes (OOB vote matrix) unconditionally; it is the # only honest per-class probability estimate. In-bag class probabilities are # not exposed through a consistent randomForest API, so oob=FALSE is not # supported. Warn the caller rather than silently ignoring the argument. @@ -314,7 +314,7 @@ gg_variable.randomForest <- function(object, gg_dta <- predictors # For classification forests use per-class OOB vote fractions (object$votes), - # stored as yhat. columns — the same shape gg_variable.rfsrc + # stored as yhat. columns: the same shape gg_variable.rfsrc # produces. For regression a single numeric yhat column suffices. if (object$type == "classification") { preds <- object$votes # n × n_classes matrix; may be raw counts or fractions diff --git a/R/gg_varpro.R b/R/gg_varpro.R index e7ff4bd3..170c82ee 100644 --- a/R/gg_varpro.R +++ b/R/gg_varpro.R @@ -10,8 +10,8 @@ #' #' @section What varpro is doing: #' Permutation importance asks "what happens to OOB accuracy when I scramble -#' this variable?" That works, but it leans on artificial data — the -#' permuted column — and the answer can be unstable when variables are +#' this variable?" That works, but it leans on artificial data (the +#' permuted column) and the answer can be unstable when variables are #' correlated. The varpro framework (Lu and Ishwaran, 2024) replaces #' permutation with \emph{release rules}. The forest is grown with guided #' splitting; from a subset of trees varpro samples a collection of @@ -19,7 +19,7 @@ #' response inside the rule's region to the response after the rule's #' constraint on that variable is "released". The size of that change, #' aggregated over many rules and trees, is the variable's importance. -#' No synthetic covariates, no permutation — the contrast is between two +#' No synthetic covariates, no permutation: the contrast is between two #' real subsets of the data. #' #' Because varpro builds importance from rules sampled over trees, every @@ -55,7 +55,7 @@ #' above varpro's z cutoff; #' \item see, via the boxplot's spread and the per-tree points #' (\code{faithful = TRUE}), how stable each variable's importance -#' is across trees — a high median with a wide box is a different +#' is across trees: a high median with a wide box is a different #' story from a high median with a tight box; #' \item for a classification forest, ask which variables drive which #' class (\code{conditional = TRUE}) rather than just which @@ -269,7 +269,7 @@ gg_varpro <- function(object, faithful, local.std, conditional) { is_class <- identical(family, "class") - ## Unpack importance() data frame — different shapes per family + ## Unpack importance() data frame: different shapes per family if (is_class && is.list(imp_out) && !is.data.frame(imp_out)) { imp_df_raw <- imp_out$unconditional cond_mat <- if (conditional) imp_out$conditional.z else NULL diff --git a/R/gg_vimp.R b/R/gg_vimp.R index 62e37acb..b53309d0 100644 --- a/R/gg_vimp.R +++ b/R/gg_vimp.R @@ -26,6 +26,29 @@ #' function if the \code{\link[randomForestSRC]{rfsrc}} object does not contain #' importance information. #' +#' @details +#' \code{gg_vimp()} shows \strong{permutation (Breiman-Cutler) variable +#' importance}: the forest permutes a variable's observed values across the +#' out-of-bag (OOB) cases, runs those perturbed cases down the already-grown +#' trees, and measures how much the OOB prediction error climbs. That +#' perturbation is synthetic (the variable's link to the response is broken +#' on purpose) so a large increase means the variable was carrying genuine +#' signal; near-zero or negative values mean it added noise or nothing at all. +#' +#' \code{\link{gg_varpro}()} takes the opposite route, comparing local +#' estimators on real observed data through varPro's release rules, with no +#' permutation and no synthetic features. The two approaches answer "which +#' variables matter?" by opposite mechanisms, so a variable can rank +#' differently under each, and that disagreement is itself informative: it +#' often signals interaction structure or non-monotone effects that one +#' mechanism surfaces and the other obscures. +#' +#' For survival forests, VIMP is measured against the ensemble cumulative +#' hazard function (CHF); the error metric is one minus the concordance index +#' (C-statistic). Variables with non-positive VIMP are flagged in the +#' \code{positive} column and colored differently by +#' \code{\link{plot.gg_vimp}}. +#' #' @return \code{gg_vimp} object. A \code{data.frame} of VIMP measures, in rank #' order, optionally containing class-specific scores and a relative importance #' column. When \code{randomForest} objects lack stored importance values a @@ -33,7 +56,7 @@ #' reproducible. #' #' @seealso \code{\link{plot.gg_vimp}} \code{\link[randomForestSRC]{rfsrc}} -#' @seealso \code{\link[randomForestSRC]{vimp}} +#' @seealso \code{\link[randomForestSRC]{vimp}} \code{\link{gg_varpro}} #' #' @references #' Ishwaran H. (2007). Variable importance in binary regression trees and @@ -188,7 +211,7 @@ gg_vimp.rfsrc <- function(object, nvar, ...) { gg_dta <- data.frame(object$importance) } - # Single-outcome forests: $importance is a named vector → one-column df. + # Single-outcome forests: $importance is a named vector, one-column df. # Rename the column and add a "vars" column with the variable names. if (ncol(gg_dta) == 1) { colnames(gg_dta) <- "VIMP" @@ -233,8 +256,8 @@ gg_vimp.rfsrc <- function(object, nvar, ...) { } } else { # Look up by integer index. - # which.outcome = 0 → overall (across-class) importance, column 1 - # which.outcome = k → importance for class k, column k+1 + # which.outcome = 0 gives overall (across-class) importance, column 1 + # which.outcome = k gives importance for class k, column k+1 if (!is.numeric(arg_set$which.outcome) || arg_set$which.outcome < 0) { stop("which.outcome must be a non-negative integer or a class name.") } @@ -294,8 +317,13 @@ gg_vimp.rfsrc <- function(object, nvar, ...) { # Flag variables with non-positive VIMP so the plot can colour them # differently to indicate they do not improve (or actively hurt) predictions. + # Use the actual VIMP column name: "vimp" after a multi-outcome pivot, + # "VIMP" (uppercase) for single-outcome fits. + vimp_col <- intersect(c("vimp", "VIMP"), colnames(gg_dta))[1] gg_dta$positive <- TRUE - gg_dta$positive[which(gg_dta$vimp <= 0)] <- FALSE + if (!is.na(vimp_col)) { + gg_dta$positive[which(gg_dta[[vimp_col]] <= 0)] <- FALSE + } class(gg_dta) <- c("gg_vimp", class(gg_dta)) gg_dta <- .set_provenance(gg_dta, object) @@ -400,8 +428,8 @@ gg_vimp.randomForest <- function(object, nvar, ...) { ) } } else { - # which.outcome = 0 → overall importance (column 1) - # which.outcome = k → class k importance (column k+1) + # which.outcome = 0 gives overall importance (column 1) + # which.outcome = k gives class k importance (column k+1) if (!is.numeric(arg_set$which.outcome) || arg_set$which.outcome < 0) { stop("which.outcome must be a non-negative integer or a class name.") } diff --git a/R/kaplan.R b/R/kaplan.R index be1c0b6c..acbb84ec 100644 --- a/R/kaplan.R +++ b/R/kaplan.R @@ -87,7 +87,7 @@ kaplan <- function(interval, # When stratifying, stitch a "groups" label column onto the table. if (!is.null(by)) tbl <- .label_strata(tbl, data, by) # nolint: object_usage_linter - # Keep only rows where at least one event occurred — censoring-only rows + # Keep only rows where at least one event occurred; censoring-only rows # do not contribute new KM estimates. gg_dta <- tbl[which(tbl[["dead"]] != 0), ] diff --git a/R/plot.gg_beta_varpro.R b/R/plot.gg_beta_varpro.R index e6647fcf..8648ce84 100644 --- a/R/plot.gg_beta_varpro.R +++ b/R/plot.gg_beta_varpro.R @@ -9,8 +9,8 @@ #' #' @section Reading the chart: #' Each bar is the average magnitude of a per-rule lasso coefficient for -#' that variable. **The numeric scale carries the predictor's units** — -#' if "age" is in years and "creatinine" is in mg/dL, a longer bar for +#' that variable. **The numeric scale carries the predictor's units.** +#' If "age" is in years and "creatinine" is in mg/dL, a longer bar for #' age does not mean age is "more important" in any unit-free sense. #' Comparisons across data sets or across variables with very different #' units require keeping the units context in mind. Within one data set, @@ -19,7 +19,7 @@ #' Variables above the cutoff are coloured blue and flagged `selected`; #' variables below are grey. Lasso shrinkage can drive a rule's #' \eqn{\hat{\beta}}{beta hat} to -#' exactly zero — those rules are kept in the average, so a variable +#' exactly zero; those rules are kept in the average, so a variable #' with many shrunk-to-zero rules will sit lower in the ranking than #' one whose released coefficients are consistently non-zero. #' diff --git a/R/plot.gg_brier.R b/R/plot.gg_brier.R index ac4ae1f1..3beb3abc 100644 --- a/R/plot.gg_brier.R +++ b/R/plot.gg_brier.R @@ -13,11 +13,20 @@ ####********************************************************************** #' Plot a \code{\link{gg_brier}} object #' -#' Draws the time-resolved Brier score (the default) or the running CRPS as -#' a curve against time. Lower means more accurate probabilistic predictions. -#' Turn \code{envelope = TRUE} on and the overall line picks up a ribbon -#' spanning the 15th to 85th percentile of the per-subject contributions, -#' which shows how much the score varies across subjects at each time. +#' Draws the time-resolved Brier score or the running CRPS from a +#' \code{\link{gg_brier}} object. The curve moves across the event-time +#' grid on the x-axis; lower values mean the forest's predicted survival +#' probabilities are closer to what actually happened. Think of +#' \code{0} as "perfect" and roughly \code{0.25} as "uninformative" -- a +#' forest that predicts \code{0.5} for every subject regardless of +#' prognosis would sit near that ceiling. +#' +#' Set \code{envelope = TRUE} to add a ribbon around the overall curve +#' spanning the 15th to 85th percentile of the per-subject Brier +#' contributions at each time. The ribbon shows how heterogeneous the +#' scoring is across subjects: a narrow ribbon means most subjects are +#' predicted equally well (or equally poorly); a wide ribbon means a +#' minority of subjects are driving the average. #' #' @param x A \code{\link{gg_brier}} object. #' @param type Which series to plot: \code{"brier"} (default) or diff --git a/R/plot.gg_error.R b/R/plot.gg_error.R index 06c7ddc4..32536b30 100644 --- a/R/plot.gg_error.R +++ b/R/plot.gg_error.R @@ -255,7 +255,7 @@ plot.gg_error <- function(x, ...) { gg_plt <- gg_plt + ggplot2::geom_line() + err_labs } - # Hide the legend when there is only a single outcome variable — the colour + # Hide the legend when there is only a single outcome variable: the colour # key adds no information and clutters the plot. For single-outcome forests # (regression / survival) the data is never gathered, so there is no # "variable" column; suppress the legend unconditionally in that case. diff --git a/R/plot.gg_isopro.R b/R/plot.gg_isopro.R index d16b9bae..9da94352 100644 --- a/R/plot.gg_isopro.R +++ b/R/plot.gg_isopro.R @@ -11,8 +11,8 @@ #' #' @section Reading the elbow: #' The elbow plot is the canonical anomaly-detection picture. The x-axis -#' is observation rank — observations sorted from most ordinary to most -#' anomalous — and the y-axis is the \code{howbad} score. For a clean +#' is observation rank (observations sorted from most ordinary to most +#' anomalous) and the y-axis is the \code{howbad} score. For a clean #' population the curve sits flat near zero across the bulk of the data #' and then bends sharply upward in the right tail; that bend is where #' the anomalous observations live. The point of the plot is not to read @@ -24,7 +24,7 @@ #' @section Reading the density: #' The density panel is the same scores viewed as a distribution. A #' single tight mode near zero with a long thin right tail is the -#' picture you hope for — bulk of the data ordinary, a few clear +#' picture you hope for: bulk of the data ordinary, a few clear #' anomalies. A bimodal density says you may have two populations rather #' than one clean cluster plus outliers, and the cutoff question becomes #' harder. Either way, this panel is a sanity check on what the elbow @@ -35,14 +35,14 @@ #' bound several \code{\link{gg_isopro}} calls together), both panels #' colour by method automatically. The point of comparing \code{"rnd"}, #' \code{"unsupv"}, and \code{"auto"} is not to pick a winner from the -#' figure alone — it is to see whether the methods agree on which +#' figure alone, it is to see whether the methods agree on which #' observations are anomalous. Curves that overlap in the right tail and #' elbow at roughly the same rank are telling you the same story three #' ways. Curves that diverge are telling you the score is #' method-sensitive, which is itself useful information. #' #' @param x A \code{gg_isopro} object from \code{\link{gg_isopro}}. -#' @param panel One of \code{"both"} (default — a \code{patchwork} of +#' @param panel One of \code{"both"} (default: a \code{patchwork} of #' elbow + density), \code{"elbow"}, or \code{"density"} (each returns a #' single \code{ggplot}). #' @param threshold Numeric in \code{[0, 1]}, or \code{NULL} (default). If diff --git a/R/plot.gg_ivarpro.R b/R/plot.gg_ivarpro.R index 4dcdd6bf..811f79b5 100644 --- a/R/plot.gg_ivarpro.R +++ b/R/plot.gg_ivarpro.R @@ -22,8 +22,9 @@ #' #' The per-observation view (`which_obs`) is a horizontal bar chart of #' one observation's local importances; bars below the cutoff are grey, -#' above are blue. Think of it as a SHAP-style waterfall, but the -#' values are scaled per-rule contributions, not Shapley values. +#' above are blue. The visual resembles a SHAP waterfall, but the values +#' are release-rule contributions: scaled per-rule contrasts on observed +#' data, not Shapley values and not permutation-based. #' #' @param x A `gg_ivarpro` object from [gg_ivarpro()]. #' @param ... Not currently used. diff --git a/R/plot.gg_partial.R b/R/plot.gg_partial.R index 54b0070d..3c19df16 100644 --- a/R/plot.gg_partial.R +++ b/R/plot.gg_partial.R @@ -27,10 +27,17 @@ partial_surv_y_label <- function(partial.type) { #' Plot a \code{\link{gg_partial}} object #' -#' Produces ggplot2 partial dependence curves from the named list returned by -#' \code{\link{gg_partial}}. Continuous predictors are shown as line plots; -#' categorical predictors are shown as bar charts. Both panels are faceted by -#' variable name so multiple predictors can be compared at a glance. +#' Turns a \code{\link{gg_partial}} object into a ggplot2 figure. Each curve +#' is a partial dependence trace -- the forest's average prediction as one +#' predictor is swept across its range while the rest are marginalized over the +#' training data. Continuous predictors appear as line plots; categorical +#' predictors appear as bar charts. Both panels are faceted by variable name +#' so you can compare the shape and scale of each variable's effect at a +#' glance. +#' +#' When a \code{model} label was attached in \code{gg_partial()}, lines are +#' coloured by model -- handy for overlaying results from two forests (e.g., +#' one tuned, one default) in the same figure. #' #' @param x A \code{\link{gg_partial}} object (output of \code{\link{gg_partial}}). #' @param ... Not currently used; reserved for future arguments. @@ -95,20 +102,26 @@ plot.gg_partial <- function(x, ...) { #' Plot a \code{\link{gg_partial_rfsrc}} object #' -#' Produces ggplot2 partial dependence curves from the named list returned by -#' \code{\link{gg_partial_rfsrc}}. +#' Renders the partial dependence curves from \code{\link{gg_partial_rfsrc}} +#' as a ggplot2 figure. The layout adapts automatically to what the object +#' contains. #' -#' For standard (non-survival) forests: continuous predictors are line plots, -#' categorical predictors are bar charts, both faceted by variable name. +#' For a standard regression or classification forest, continuous predictors +#' are drawn as line plots and categorical predictors as bar charts, both +#' faceted by variable name -- the same arrangement as +#' \code{\link{plot.gg_partial}}. #' -#' 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 a survival forest, each call to \code{partial.rfsrc} returns a predicted +#' quantity (survival probability, cumulative hazard function, or mortality) at +#' one or more chosen time horizons. When a \code{time} column is present in +#' the data, each horizon becomes a separate coloured curve over the predictor's +#' value, still faceted by variable. The y-axis label (\dQuote{Predicted +#' Survival}, \dQuote{Predicted CHF}, or \dQuote{Predicted Mortality}) tracks +#' the \code{partial.type} attribute set by \code{gg_partial_rfsrc()}. #' -#' For two-variable surface plots (when a \code{grp} column is present): -#' each group level is a separate line, faceted by primary predictor name. +#' For a two-variable interaction surface (when \code{xvar2.name} was supplied +#' to \code{gg_partial_rfsrc}), the secondary variable's levels become +#' separate coloured lines, faceted by the primary predictor. #' #' @param x A \code{\link{gg_partial_rfsrc}} object. #' @param ... Not currently used. diff --git a/R/plot.gg_partial_varpro.R b/R/plot.gg_partial_varpro.R index 0cca0b80..97623cef 100644 --- a/R/plot.gg_partial_varpro.R +++ b/R/plot.gg_partial_varpro.R @@ -21,8 +21,8 @@ #' #' Where the three effect types track each other, the parametric story #' is a fair summary of what the forest is doing. Where they fan -#' apart — typically the parametric curve smoother than the -#' nonparametric, or the causal curve flatter than either — the +#' apart (typically the parametric curve smoother than the +#' nonparametric, or the causal curve flatter than either) the #' variable is one to inspect more carefully before reading a single #' effect off the plot. #' diff --git a/R/plot.gg_rfsrc.R b/R/plot.gg_rfsrc.R index 652c672f..b45769d5 100644 --- a/R/plot.gg_rfsrc.R +++ b/R/plot.gg_rfsrc.R @@ -14,11 +14,21 @@ ####********************************************************************** #' Predicted response plot from a \code{\link{gg_rfsrc}} object. #' -#' Plot the predicted response from a \code{\link{gg_rfsrc}} object, the -#' \code{\link[randomForestSRC]{rfsrc}} prediction, using the OOB prediction -#' from the forest. The plot type adapts automatically to the forest family: -#' jitter + boxplot for regression and classification, step curves for -#' survival. +#' Visualizes the ensemble predictions extracted by \code{\link{gg_rfsrc}}. +#' By default those are out-of-bag (OOB) predictions -- the forest's built-in +#' cross-validation estimate, averaging only over the trees that left a given +#' observation out of their bootstrap sample. +#' +#' The geometry adapts to the forest family. For regression or +#' classification, you get a jitter-and-boxplot: every observation is a dot +#' placed at its OOB predicted value, coloured by observed response, with a +#' notched boxplot overlaid to show the central tendency and spread. For a +#' survival forest, each observation contributes one ensemble survival curve +#' (or CHF / mortality, whichever \code{surv_type} was chosen in +#' \code{gg_rfsrc}); the bundle of step functions shows the spread of +#' predicted risk across the cohort. Pass \code{by} to \code{gg_rfsrc} +#' beforehand to colour curves by a predictor group, or \code{conf.int} to +#' replace the individual curves with a mean curve and bootstrap ribbon. #' #' @param x A \code{\link{gg_rfsrc}} object, or a raw #' \code{\link[randomForestSRC]{rfsrc}} object (which will be passed through @@ -341,7 +351,7 @@ plot.gg_rfsrc <- function(x, notch = TRUE, ...) { } ) } else { - # Unknown forest type — not yet implemented + # Unknown forest type: not yet implemented stop(paste( "Plotting for ", class(gg_dta)[2], diff --git a/R/plot.gg_survival.R b/R/plot.gg_survival.R index 8a15f691..d3580016 100644 --- a/R/plot.gg_survival.R +++ b/R/plot.gg_survival.R @@ -13,7 +13,20 @@ ####********************************************************************** ####********************************************************************** #' -#' Plot a \code{\link{gg_survival}} object. +#' Plot a \code{\link{gg_survival}} object. +#' +#' Draws a Kaplan-Meier (or Nelson-Aalen) survival curve from a +#' \code{\link{gg_survival}} object. You can overlay a confidence envelope +#' around the curve using \code{error}: \code{"shade"} fills the area between +#' the pointwise confidence limits, \code{"lines"} draws them as dashed step +#' functions, and \code{"bars"} shows them as error bars. When \code{gg_survival} +#' was called with a \code{by} argument, each group gets its own step function +#' and the \code{label} argument renames the legend. +#' +#' The \code{type} argument selects which quantity to plot on the y-axis -- +#' survival probability (\code{"surv"}) is the default, but cumulative hazard, +#' density, and several transformed scales are available for the cases where a +#' linear scale reveals more about the tails. #' #' @param x \code{\link{gg_survival}} or a survival \code{\link{gg_rfsrc}} #' object created from a \code{\link[randomForestSRC]{rfsrc}} object diff --git a/R/plot.gg_udependent.R b/R/plot.gg_udependent.R index 3c03e04f..2d09ec52 100644 --- a/R/plot.gg_udependent.R +++ b/R/plot.gg_udependent.R @@ -12,7 +12,7 @@ #' Fruchterman-Reingold layout (the default) places mutually connected #' variables near each other, so the picture tends to show hubs and #' the clusters around them rather than a tidy ring. The eye usually -#' goes first to the largest blue node — a variable that is both in +#' goes first to the largest blue node: a variable that is both in #' the signal set and connects to many others is a hub of the #' dependency structure. Edges with wider, more opaque strokes are #' stronger dependencies; thin, faint edges sit near the threshold and @@ -20,9 +20,9 @@ #' #' Grey, low-degree nodes are the ones UVarPro thinks are not #' contributing much to the structure. (Truly isolated nodes are -#' dropped by `gg_udependent()` before the graph is drawn — what you +#' dropped by `gg_udependent()` before the graph is drawn; what you #' see is the connected component.) A cluster of mutually -#' connected variables is worth checking for redundancy — they may be +#' connected variables is worth checking for redundancy; they may be #' several views of the same underlying quantity. #' #' @section What this tells you: diff --git a/R/plot.gg_variable.R b/R/plot.gg_variable.R index 5ade9bc9..f7245973 100644 --- a/R/plot.gg_variable.R +++ b/R/plot.gg_variable.R @@ -227,7 +227,7 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter ccls[which(ccls == "integer")] <- "numeric" ## ========================================================= - ## PANEL PLOT branch — facet multiple predictors in one figure + ## PANEL PLOT branch: facet multiple predictors in one figure ## ========================================================= if (panel) { ## ---- Survival panel plot ---------------------------------------- @@ -356,7 +356,7 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter gg_dta_mlt$variable <- factor(gg_dta_mlt$variable, levels = xvar) - # All continuous predictors → scatter; any factor → boxplot + # All continuous predictors give scatter; any factor gives boxplot if (sum(ccls[wch_x_var] == "numeric") == length(wch_x_var)) { if (family == "class") { gg_plt <- @@ -421,7 +421,7 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter } ## ========================================================= - ## INDIVIDUAL PLOT branch — one ggplot per predictor variable + ## INDIVIDUAL PLOT branch: one ggplot per predictor variable ## ========================================================= } else { # Pre-allocate a list; collapsed to a single object when lng == 1 @@ -577,7 +577,7 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter } else { # Factor predictor (multi-class): boxplot + jitter per facet. # smooth=TRUE is intentionally a no-op here for the same reason - # as the binary factor path above — geom_smooth requires a + # as the binary factor path above: geom_smooth requires a # continuous x-axis. gg_plt[[ind]] <- gg_plt[[ind]] + ggplot2::geom_boxplot( @@ -638,7 +638,7 @@ plot.gg_variable <- function(x, # nolint: cyclocomp_linter colnames(gg_dta)[ch_indx] <- h_name } # Return a single object: one ggplot for a single variable, otherwise a - # patchwork composite (one panel per variable). Never a bare list — see + # patchwork composite (one panel per variable). Never a bare list; see # #80 / NEWS; mirrors the v2.7.3 #77/#78 plot.gg_partial* unification. if (lng == 1) { gg_plt <- gg_plt[[1]] diff --git a/R/plot.gg_varpro.R b/R/plot.gg_varpro.R index 26701618..597f986f 100644 --- a/R/plot.gg_varpro.R +++ b/R/plot.gg_varpro.R @@ -12,7 +12,7 @@ #' importance, so the eye lands on the most important variable first. #' For each variable the box spans the 15th to 85th percentile of the #' per-tree scores, the centre line is the median, and the whiskers run -#' out to the 5th and 95th percentile — not the usual Tukey 1.5 IQR +#' out to the 5th and 95th percentile, not the usual Tukey 1.5 IQR #' whiskers. The dashed vertical line is the selection \code{cutoff} #' (default \code{0.79}). On the default z-score axis #' (\code{local.std = TRUE}) that line is a z; on the raw-importance @@ -37,7 +37,7 @@ #' #' @section What this tells you: #' Take the variables above the cutoff as your candidate set. Use the -#' width of the box and the per-tree overlay to gauge confidence — a +#' width of the box and the per-tree overlay to gauge confidence: a #' narrow box well above the cutoff is a confident pick, a wide box #' that crosses it is a coin flip you should not lean on. For #' classification, conditional importance tells you which variables diff --git a/R/plot.gg_vimp.R b/R/plot.gg_vimp.R index ed108feb..a1673171 100644 --- a/R/plot.gg_vimp.R +++ b/R/plot.gg_vimp.R @@ -15,6 +15,19 @@ #' Plot a \code{\link{gg_vimp}} object, extracted variable importance of a #' \code{\link[randomForestSRC]{rfsrc}} object #' +#' Draws a horizontal bar chart of the VIMP scores extracted by +#' \code{\link{gg_vimp}}. Each bar represents one predictor; bar length is +#' proportional to its permutation VIMP -- the average rise in OOB prediction +#' error when that predictor's OOB values are randomly shuffled. Predictors +#' are sorted in descending order of importance so the most influential +#' variables appear at the top. +#' +#' Bars are coloured by the \code{positive} flag: a bar at or below zero +#' (non-positive VIMP) is colour-coded differently to flag predictors that +#' \emph{hurt} OOB accuracy when their signal is removed -- usually a sign of +#' collinearity or a very noisy variable. In a well-behaved forest most bars +#' are positive; the colour distinction matters when a handful are not. +#' #' @param x \code{\link{gg_vimp}} object created from a #' \code{\link[randomForestSRC]{rfsrc}} object #' @param relative should we plot vimp or relative vimp. Defaults to vimp. diff --git a/R/print_helpers.R b/R/print_helpers.R index 1206d307..b454b327 100644 --- a/R/print_helpers.R +++ b/R/print_helpers.R @@ -35,7 +35,7 @@ } )) } - # Unknown / non-forest object — return NULL so callers can skip cleanly. + # Unknown / non-forest object: return NULL so callers can skip cleanly. NULL } diff --git a/R/print_methods.R b/R/print_methods.R index c984fca7..d0adee8a 100644 --- a/R/print_methods.R +++ b/R/print_methods.R @@ -3,7 +3,7 @@ #### #### All print methods follow the same shape: emit a one-line header #### (class label + provenance) and return the object invisibly. They do -#### NOT print rows — gg_* objects inherit data.frame (or list), so +#### NOT print rows. gg_* objects inherit data.frame (or list), so #### head() and as.data.frame() remain available for inspecting contents. ####********************************************************************** diff --git a/R/ribbon_style.R b/R/ribbon_style.R index 1c662c87..1dd19e5e 100644 --- a/R/ribbon_style.R +++ b/R/ribbon_style.R @@ -4,7 +4,7 @@ #### Every plot.gg_* method that draws a ribbon (CI band on KM/NA curves, #### bootstrap CI band on gg_rfsrc survival curves, per-subject envelope #### on gg_brier) uses these constants so the family renders uniformly. -#### This is styling only — the underlying probability/coverage of each +#### This is styling only. The underlying probability/coverage of each #### ribbon is computed elsewhere and is intentionally heterogeneous #### (95% inferential CIs vs. 15-85 percentile descriptive envelopes). ####********************************************************************** @@ -26,9 +26,9 @@ # error that arises when alpha is both in ... and set explicitly on geom_ribbon. # # Returns a list with: -# $ribbon_alpha — numeric alpha to pass explicitly to geom_ribbon() -# $step_dots — original dots (forwarded to geom_step / geom_line) -# $ribbon_dots — dots with alpha removed (ribbon alpha is explicit) +# $ribbon_alpha : numeric alpha to pass explicitly to geom_ribbon() +# $step_dots : original dots (forwarded to geom_step / geom_line) +# $ribbon_dots : dots with alpha removed (ribbon alpha is explicit) .gg_split_alpha <- function(dots) { user_alpha <- dots[["alpha"]] list( diff --git a/R/varpro_feature_names.R b/R/varpro_feature_names.R index 02fd19a1..29e899b2 100644 --- a/R/varpro_feature_names.R +++ b/R/varpro_feature_names.R @@ -49,10 +49,10 @@ #' #' @export varpro_feature_names <- function(varpro_names, dataset) { - # Names that already match a column in dataset — keep as-is + # Names that already match a column in dataset: keep as-is inc_set <- varpro_names[which(varpro_names %in% colnames(dataset))] - # Names that do not yet match any column — need suffix stripping + # Names that do not yet match any column: need suffix stripping one_set <- varpro_names[which(!varpro_names %in% colnames(dataset))] ## Iteratively strip the last character of each unmatched name until every diff --git a/cran-comments.md b/cran-comments.md index b6d747ea..72d1f721 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,10 +1,34 @@ -## v3.0.0 — varPro integration (major release) +## v3.1.0 — varPro integration + bug fix (major release) -This is a major release following v2.7.3 (current on CRAN). The version -jumps to 3.0.0 because it adds a substantial new feature layer and -soft-deprecates one user-facing function. +This is a major release. v2.7.3 is the version currently published on +CRAN. A v3.0.0 submission (2026-05-28) cleared the incoming pretests on +Windows and Debian (0/0/0) but was held by the automated service and did +not complete the review cycle; no changes were ever requested. This +v3.1.0 release supersedes it: the submission moves CRAN from v2.7.3 to +v3.1.0 and carries the full v3.0.0 feature layer plus the v3.1.0 bug fix +and documentation work. The version is in 3.x territory because it adds a +substantial new feature layer and soft-deprecates one user-facing +function. -### Major changes in v3.0.0 +The automated hold on the v3.0.0 submission appeared to be a heuristic +flag (the major version jump from 2.7.3 plus the Depends-to-Imports move), +not a package defect: both incoming pretests were clean. We note it here +in case the same heuristic flags v3.1.0. + +### Changes in v3.1.0 + +- **Bug fix.** `gg_vimp()` on single-outcome `rfsrc` forests now correctly + flags variables with non-positive VIMP in the `positive` column (used for + plot colouring). The single-outcome fit names the column `VIMP` + (uppercase) while the flag check read `$vimp` (lowercase), so `positive` + stayed `TRUE` for every variable. Added a regression test. +- **Documentation.** Deepened the varPro-family and rfsrc importance / + partial / survival help pages, made the `gg_vimp()` (permutation + importance) vs `gg_varpro()` (release-rule importance) distinction + explicit and cross-linked, and fixed a stale `@return` in `gg_roc()`. No + user-facing behaviour change beyond the bug fix above. + +### Major changes carried from v3.0.0 - **New varPro wrapper family.** Tidy extractors and `plot()` methods for the `varPro` package: `gg_partial_varpro()` (partial dependence), @@ -14,7 +38,7 @@ soft-deprecates one user-facing function. (individual / local importance), each with `print` / `summary` / `autoplot` companions and a dedicated "varpro" vignette. - **Soft-deprecation.** `gg_partialpro()` now warns and forwards to - `gg_partial_varpro()`; it will be removed in the release after v3.0.0. + `gg_partial_varpro()`; it will be removed in a future release. - **randomForest engine fixes.** `gg_variable.randomForest()` classification, `gg_roc()` / `calc_roc()` for `randomForest` (true probability-based, macro-averaged ROC), per-class one-vs-rest ROC @@ -32,10 +56,10 @@ dependencies** on CRAN, so no downstream packages are affected. ## Test environments -* **Local:** R 4.5.3 on macOS (aarch64-apple-darwin20). +* **Local:** R 4.6.0 on macOS (aarch64-apple-darwin23). `R CMD check --as-cran` returns 0 errors, 0 warnings, 0 notes. * **GitHub Actions matrix:** ubuntu-latest (R-devel / R-release / - R-oldrel-1), windows-latest (R-release), macos-latest (R-release) — + R-oldrel-1), windows-latest (R-release), macos-latest (R-release), all green on the head commit. * **Reverse-dependency check:** `tools::package_dependencies(reverse = TRUE)` returns 0. diff --git a/dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md b/dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md new file mode 100644 index 00000000..5824e3e4 --- /dev/null +++ b/dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md @@ -0,0 +1,174 @@ +# ggRandomForests v3.1.0 — Documentation Sweep Design + +**Status:** Approved (brainstorm) — 2026-05-29; **release mechanics SUPERSEDED 2026-06-10** (see banner) +**Target release:** v3.1.0 (minor; docs-cleanup + `gg_vimp` fix) +**Branch:** `docs/v3.1.0-doc-sweep` (cut off `main`) + +> **⚠️ SUPERSEDED 2026-06-10 (release mechanics only).** The v3.0.0 submission +> lapsed at CRAN un-reviewed, so the "version stays 3.0.0 / held until v3.0.0 +> acceptance / cut at a post-acceptance RC" mechanics below are void. This +> branch ships **directly as 3.1.0** — DESCRIPTION + NEWS bumped here, hold +> lifted, PR #109 merges to `main` as the v3.1.0 release. The documentation +> design below is unchanged. + +## Goal + +Deepen and tighten the ggRandomForests documentation against two upstream +sources, in John's voice, without re-treading the prose the v3.0.0 voice audit +(PR #92) already shipped. Three surfaces: roxygen, vignettes, and R/ code +comments. The single highest-value clarification is making the **`gg_vimp` +(rfsrc permutation / Breiman-Cutler VIMP) vs `gg_varpro` (varPro release-rule, +no-synthetic-features importance)** distinction explicit and correctly +attributed to each upstream method. + +## Scope (the v3.0.0 function set — no RHF) + +RHF docs (`gg_rhf`, `gg_auct`, …) are handled separately on the `dev` v4.0.0 +line and are out of scope here. This branch is off `main`, so it sees only the +released v3.0.0 surface. + +## Voice standard + +`memory/writing-voice.md` (the two-register fingerprint) **governs**; the +`memory/preferences.md` anti-slop banned-words and banned-structures are layered +on top. On the conflicts, writing-voice.md wins for package docs: +- **Em-dashes:** allowed sparingly, only where one earns the pause; otherwise a + comma, parentheses, or a full stop. +- **Analogy:** one carried concrete analogy is allowed in the *narrative* + register (vignettes, `@description`/`@details`); none in the terse register. +- **Registers:** Narrative = vignettes, README, roxygen `@description`/`@details`, + methods prose. Terse = roxygen `@param`/`@return`, NEWS bullets. Code comments + use the terse register ("why," not "what"). +- Anti-slop banned words/phrases/structures apply throughout regardless. +- **Reference exemplars** (from writing-voice.md): `boilerplates/methods/VarPro + Modeling in Plain English.docx` (OneDrive, CORR) is the gold-standard + plain-English varPro explainer — mirror its framing for the varpro family; the + TemporalHazard 1.0.3 LinkedIn post is the short-form exemplar. + +## Upstream anchors (content sources) + +- **varPro family** — : + release rules on local neighborhoods of observed data ("no artificial + permutations or synthetic features"), guided splitting, rule harvesting, + split-weights, local estimators / local standardization, model-independent; + importance `z` is prefiltered (noise vars dropped); `cv.varpro` + `$imp/$imp.conserve/$imp.liberal` (1-SD rule); classification + `$unconditional`/`$conditional.z`; `get.orgvimp`/`get.topvars` one-hot + consolidation. +- **rfsrc family** — articles: survival.html + (ensemble survival/CHF/mortality), rfsrc-subsample.html (permutation VIMP + + subsampling inference), partial.html (partial dependence), minidep.html, + competing.html. + +## Per-surface plan + +### 1. Deep content — roxygen (upstream-anchored) +**varPro family:** `gg_varpro`/`plot`, `gg_beta_varpro`/`plot`, +`gg_ivarpro`/`plot`, `gg_udependent`/`plot`, `gg_isopro`/`plot`, +`gg_partial_varpro`/`plot` (+ `gg_partialpro` shim). +**rfsrc importance/partial/survival:** `gg_vimp`/`plot`, `gg_partial`/`plot`, +`gg_partial_rfsrc`/`plot`, `gg_rfsrc`/`plot`, `gg_survival`/`plot`, +`gg_brier`/`plot`. +Add the release-rules framing, the `gg_vimp`-vs-`gg_varpro` distinction (anchor +it in both functions' `@details` and cross-`@seealso`), and upstream +terminology. Keep API/`@param`/`@return` factually exact — deepen the +explanatory prose, do not restate mechanics inaccurately. + +### 2. Drift check — roxygen (lighter) +Every other exported topic: `gg_roc`/`plot`, `gg_variable`/`plot`, +`gg_error`/`plot`, `calc_roc`/`calc_auc`, `kaplan`/`nelson`, the S3-method doc +pages (`print.gg`/`summary.gg`/`autoplot.gg`), utilities (`quantile_pts`, +`varpro_feature_names`), package doc. Read for voice regressions and staleness +since #92; fix only what's off — no gratuitous rewriting. + +### 3. Vignettes (all 4) +`ggRandomForests` (intro), `ggRandomForests-survival`, `ggRandomForests-regression`, +`varpro`. Content deepening where the upstream sources help (especially the +varpro vignette and the VIMP-vs-varpro contrast in the regression/intro +vignettes) + drift/voice check. Add the two upstream URLs and the relevant +references (Lee et al. 2021; randomForestSRC VIMP-subsampling) to the +bibliographies where cited. + +### 4. Code comments (all R/) +Correctness + gaps only: fix stale/misleading comments (e.g. references to +removed args or old behavior); add comments only where non-obvious logic is +genuinely undocumented (the "why"). Do not comment self-explanatory code; do not +standardize-for-its-own-sake. Terse register. + +## Version & NEWS mechanics + +- **Version stays `3.0.0`** during the held sweep — no dev-suffix bump. This + avoids colliding with the `dev` v4.0.0 line (`3.0.0.9001`) and keeps the + NEWS-version test green (DESCRIPTION `3.0.0` == NEWS line 2 `3.0.0`). +- NEWS entries are written under a **new `ggRandomForests v3.1.0` heading** + added above the v3.0.0 section, but the `Version:` line is NOT changed. +- The cut to `3.1.0` (DESCRIPTION + NEWS `Version:` line, dual update) happens at + the **RC, post-acceptance** — same pattern as the 2.7.3.9015 → 3.0.0 cut. + +## Method + +Subagent-driven by unit, each subagent handed: the voice standard + anti-slop +rules verbatim, the specific upstream framing for its unit, and the +"deepen-prose / keep-API-exact" rule. Units: +1. varPro-family roxygen +2. rfsrc importance/partial/survival roxygen +3. drift-check roxygen (the remainder) +4. each vignette (4 units) +5. code comments (by file-group) + +Two-stage review per unit: (a) voice + anti-slop compliance against +writing-voice.md; (b) accuracy — every claim still matches the R code / `@param` +/ `@return` (no drift introduced). Matches the PR #92 + `vignette-clarity-pass.md` +precedent. + +## Verification + +- `devtools::document()` clean (roxygen edits regenerate `man/` cleanly). +- `R CMD check --as-cran` **with** the manual build (standing release gate) — + target 0/0/0 (dev-version NOTE not expected since version stays 3.0.0; a + "New submission"-style note is irrelevant pre-release). +- All 4 vignettes render. +- Spell-check (`devtools::spell_check()`). +- No factual drift: spot-check that deepened `@details` claims match the code. + +## Branch / release workflow + +- Work on `docs/v3.1.0-doc-sweep` now; open a **draft PR into `main`** and do + NOT merge until CRAN accepts v3.0.0. +- If a CRAN reviewer requests v3.0.0 changes, that fix branch is the priority; + rebase this docs branch onto the updated `main` afterward. +- On v3.0.0 acceptance: cut version to `3.1.0`, finalize NEWS + `cran-comments.md`, + run the full release gate, merge to `main`, submit to CRAN, then **merge `main` + forward into `dev`** so the deepened varpro/rfsrc docs flow into v4.0.0. + +## Bugs surfaced while reconciling docs against the canonical sources + +Reconciling our prose against varprotools.org / randomForestSRC.org will expose +mismatches. Each subagent that hits one must **flag it explicitly** (do not +silently "fix" by softening the docs to match buggy code). Triage: + +- **Doc error** (our description is wrong, the code is right): fix the docs — + that is the sweep's job. +- **Code bug** (the code is wrong relative to documented / canonical behavior): + **fix it here**, in this v3.1.0 branch, TDD'd — write a failing test that pins + the correct behavior, fix, confirm green, and add a NEWS bullet under the + v3.1.0 heading. A code fix may change a vdiffr snapshot; re-record only the + affected snapshot (snapshot-prune guard applies). +- **Severity routing:** if a surfaced bug is a *serious* correctness defect + (wrong numbers, not cosmetic), STOP and surface it to John before fixing — + it may belong in the v3.0.0 CRAN resubmission (a fix branch off `main`, merged + ahead of this docs branch) rather than waiting for the v3.1.0 release. Cosmetic + / minor bugs ride v3.1.0. + +This keeps v3.1.0 honest as a "docs + the fixes that doc reconciliation turns +up" minor release, without letting it silently absorb a release-critical fix. + +## Out of scope / deferred + +- RHF docs (v4.0.0, on `dev`). +- Pre-planned code features unrelated to the doc reconciliation. The candidate + v3.1.0 code items (loess-warning cleanup #80, ROC CIs #7, varPro + survival/multivariate families, hazard estimates) are NOT pulled in by this + sweep on their own; decide separately whether any ride the v3.1.0 RC or defer + to 3.2.0. (Distinct from the bug-fix clause above: that covers defects the + doc work *surfaces*, not pre-planned features.) diff --git a/dev/plans/2026-05-29-v3.1.0-doc-sweep-plan.md b/dev/plans/2026-05-29-v3.1.0-doc-sweep-plan.md new file mode 100644 index 00000000..6c551a91 --- /dev/null +++ b/dev/plans/2026-05-29-v3.1.0-doc-sweep-plan.md @@ -0,0 +1,384 @@ +# ggRandomForests v3.1.0 Documentation Sweep — Implementation Plan + +> **⚠️ SUPERSEDED 2026-06-10 (release mechanics only).** The held-workflow +> described below — keep `Version: 3.0.0`, don't merge until CRAN accepts +> v3.0.0, cut to `3.1.0` at a post-acceptance RC — no longer applies. The +> v3.0.0 submission lapsed at CRAN un-reviewed, so this branch ships +> **directly as 3.1.0**: DESCRIPTION + NEWS are bumped here, the hold is +> lifted, and PR #109 merges to `main` as the v3.1.0 release. The +> documentation-content plan below was executed as written. + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans. Steps use checkbox (`- [ ]`) syntax. Every task is a documentation/prose unit; dispatch one subagent per task and run the two-stage review (voice/anti-slop, then accuracy-vs-code) after each. + +**Goal:** Deepen and tighten ggRandomForests docs (roxygen + 4 vignettes + R/ code comments) against varprotools.org and randomForestSRC.org, in John's voice, fixing any code bugs the reconciliation surfaces. + +**Architecture:** A prose/doc sweep on the v3.0.0 function set. No new functions. roxygen edits regenerate `man/` via `devtools::document()`. Version stays `3.0.0` throughout; a new `v3.1.0` NEWS heading collects bullets but the `Version:` line is NOT bumped (cut to `3.1.0` happens at the post-acceptance RC). + +**Tech Stack:** R, roxygen2 (markdown mode), Quarto vignettes, testthat/vdiffr (only if a surfaced bug needs a test), `devtools::document`/`check`/`spell_check`. + +**Branch / workflow:** `docs/v3.1.0-doc-sweep` (already cut off `main`; spec at `dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md`). Held draft PR into `main`; do NOT merge until CRAN accepts v3.0.0. + +--- + +## VOICE STANDARD — APPLY TO EVERY TASK + +Hand this block verbatim to every subagent. It is the authority; do not rely on +any archived `dev/archive/voice-fingerprint.md` (may be stale). + +**Two registers.** +- **Narrative** (`@description`, `@details`, methods prose, vignette body): open + from the familiar; carry ONE concrete analogy through a hard idea (don't stack + analogies); question-headed sections are fine ("Why cluster?"); first person + plural ("in our practice", "we proceed") and address the reader as "you"; gloss + terms inline in parens; scare-quote a piece of jargon on first use; starting a + sentence with But/Yet/Thus/Now is fine; vary sentence length. Conversational, + not chatty — a colleague at a whiteboard, not a blog post. Cut winking asides + and cute phrasing. +- **Terse** (`@param`, `@return`, NEWS bullets, code comments): compressed, plain, + concrete, not sterile. State the thing; skip preamble ("Logical; if TRUE, ..." + not "This argument controls whether..."). No analogies, no question headers. + +**Rules.** +- Em-dashes: sparingly — keep one only where it earns the pause; else comma, + parens, or full stop. (writing-voice.md allows this; it overrides the stricter + anti-slop "no em-dashes" for package docs.) +- No ellipses in package docs. +- Don't overstate. Cut "enhanced", "powerful", "seamlessly", "robust" (as brag), + "comprehensive". State what the thing does, at its size. +- Imperfection allowed; mild redundancy and an occasional long sentence are human + texture. Pedagogical repetition (state problems up front, answer each later) is + voice, keep it. + +**Anti-slop banned words/phrases (layered on top — do NOT use unless John's own input has them):** +amazing, fascinating, mind-blowing, must-read, fast-moving world, cut through the +hype/noise, groundbreaking, paradigm-shifting, transformative, pivotal, paramount, +outstanding, a significant leap, delve, dive into, embark/embarking, endeavour, +realm, tapestry, vibrant, leverage, harness, seamlessly integrates, start from the +ground up, tackle a novel problem, crucial, critical, invaluable, +significant/significantly, surprisingly, simply, neatly, "the best part is", "real +magic happens", "recipe for disaster", thrive, "unlock the real power", "in order +to", "it is important to note", utilize. + +**Banned sentence structures:** "It isn't just X, it's Y." / "X is more than just +Y; it's Z." / "It wasn't X, it was Y." / "This is where X comes in." Banned +openers/closers: "In today's fast-paced world...", "As we navigate the +complexities...", "In conclusion...". + +**AI tells to hunt and kill:** mechanical parallelism (same-shaped sentences with +no teaching purpose; lists padded to equal length — but preserve pedagogical +repetition); flavorlessness (correct but no picture); missing "we"/"you" (abstract +authorless prose); forced tricolons ("fast, simple, and reliable"); hedge-free +sterile balance ("X. However, Y. It is worth noting Z."). Punctuation density is +NOT a tell; the tells are structural. + +**Accuracy rule (non-negotiable):** Deepen explanatory prose only. `@param`, +`@return`, argument names, defaults, and described behavior MUST stay +byte-accurate to the R code. If the prose would need to claim something the code +doesn't do, that's a flagged discrepancy (see Bug Handling), not a doc edit. + +## UPSTREAM SOURCE FRAMING — for the deep-content tasks + +**varPro** (varprotools.org): varPro selects variables with *release rules* — +it compares a local estimator inside a rule-defined region against a "released" +version of that region where the constraint on the tested variable is removed, +all *within neighborhoods of observed data*. It uses no artificial permutations +or synthetic features (this is the key contrast with permutation VIMP and +knockoffs). Terms: release rules, guided splitting, rule harvesting, split-weights, +local estimators, local standardization, model-independent. Importance `z` is +prefiltered (noise variables are dropped before scoring). `cv.varpro` returns +`$imp` / `$imp.conserve` / `$imp.liberal` (the conserve/liberal use a one-SD +rule). Classification importance has `$unconditional` and per-class +`$conditional.z`. One-hot-encoded factors are consolidated back via +`get.orgvimp` / `get.topvars`. + +**rfsrc** (randomforestsrc.org): permutation (Breiman-Cutler) VIMP measures the +increase in OOB prediction error when a variable's values are permuted — +a synthetic-perturbation method, the conceptual opposite of varPro's release +rules. Subsampling gives VIMP confidence intervals (rfsrc-subsample article). +Partial dependence (partial article) marginalizes the forest prediction over the +other variables on a grid. Survival forests yield ensemble survival, cumulative +hazard (CHF), and mortality (the article's framing for `gg_rfsrc`/`gg_survival`); +Brier score / CRPS measure calibrated survival prediction error (`gg_brier`). + +**The headline clarification (put in BOTH `gg_vimp` and `gg_varpro` `@details`, +cross-linked via `@seealso`):** `gg_vimp` shows rfsrc *permutation* VIMP +(perturb-and-measure-error, synthetic); `gg_varpro` shows varPro *release-rule* +importance (compare local estimators on observed data, no synthetic features). +They answer "which variables matter?" through opposite mechanisms; a variable can +rank differently under each, and that disagreement is informative. + +## BUG HANDLING — for every task + +If reconciling prose against the canonical source reveals the **code** disagrees +with documented/canonical behavior: +1. Do NOT soften the docs to match buggy code. +2. **Doc error** (code right, our words wrong): fix the words. Normal sweep work. +3. **Code bug** (code wrong): write a failing testthat test pinning correct + behavior, fix the code, confirm green, add a bullet under the v3.1.0 NEWS + heading. If a vdiffr snapshot changes, re-record ONLY the affected one + (`git status -s tests/testthat/_snaps/` must show no unrelated deletions; + restore with `git checkout -- tests/testthat/_snaps/` if it does). +4. **Serious correctness defect** (wrong numbers, not cosmetic): STOP. Report it + to the controller/John before fixing — it may belong in the v3.0.0 CRAN + resubmission (a fix branch off `main`), not this docs branch. + +--- + +## Task 1: varPro-family roxygen — deep content + +**Files (modify roxygen blocks only; then regenerate man/):** +- `R/gg_varpro.R`, `R/plot.gg_varpro.R` +- `R/gg_beta_varpro.R`, `R/plot.gg_beta_varpro.R` +- `R/gg_ivarpro.R`, `R/plot.gg_ivarpro.R` +- `R/gg_udependent.R`, `R/plot.gg_udependent.R` +- `R/gg_isopro.R`, `R/plot.gg_isopro.R` +- `R/gg_partial_varpro.R`, `R/plot.gg_partial_varpro.R`, `R/gg_partialpro.R` + +- [ ] **Step 1: Read every file above** plus the UPSTREAM SOURCE FRAMING block. For each function, read the actual function body to confirm `@param`/`@return`/defaults before editing prose. + +- [ ] **Step 2: Edit `@description`/`@details` (narrative register)** to add the varPro release-rules framing and terminology where it teaches. In `gg_varpro`'s `@details`, add the headline `gg_vimp`-vs-`gg_varpro` distinction (release-rule vs permutation) and a `@seealso{\link{gg_vimp}}`. Apply the VOICE STANDARD. Keep `@param`/`@return` factually exact — only reword for voice if they violate the terse register, never change meaning. + +- [ ] **Step 3: Regenerate docs.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::document()'` + Expected: writes the affected `man/*.Rd`, no errors. + +- [ ] **Step 4: Verify the package still loads and the Rd is valid.** + Run: `Rscript -e 'devtools::load_all(quiet=TRUE); cat("ok\n")'` + Expected: `ok` (no roxygen/Rd parse error). + +- [ ] **Step 5: Commit.** +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests +git add R/gg_varpro.R R/plot.gg_varpro.R R/gg_beta_varpro.R R/plot.gg_beta_varpro.R \ + R/gg_ivarpro.R R/plot.gg_ivarpro.R R/gg_udependent.R R/plot.gg_udependent.R \ + R/gg_isopro.R R/plot.gg_isopro.R R/gg_partial_varpro.R R/plot.gg_partial_varpro.R \ + R/gg_partialpro.R man/ +git commit -m "docs: deepen varPro-family roxygen (release-rules framing, vimp-vs-varpro)" +``` +Do NOT stage unrelated files. + +--- + +## Task 2: rfsrc importance/partial/survival roxygen — deep content + +**Files:** +- `R/gg_vimp.R`, `R/plot.gg_vimp.R` +- `R/gg_partial.R`, `R/plot.gg_partial.R` (the latter also holds `plot.gg_partial_rfsrc`) +- `R/gg_partial_rfsrc.R` +- `R/gg_rfsrc.R`, `R/plot.gg_rfsrc.R` +- `R/gg_survival.R`, `R/plot.gg_survival.R` +- `R/gg_brier.R`, `R/plot.gg_brier.R` + +- [ ] **Step 1: Read every file above** + the UPSTREAM SOURCE FRAMING block; confirm each `@param`/`@return`/default against the function body. + +- [ ] **Step 2: Edit `@description`/`@details` (narrative register)** to add the rfsrc framing (permutation/Breiman-Cutler VIMP, OOB error, subsampling CIs; partial dependence; ensemble survival/CHF/mortality; Brier/CRPS) where it teaches. In `gg_vimp`'s `@details`, add the headline distinction (permutation vs release-rule) and `@seealso{\link{gg_varpro}}` — mirror image of Task 1 Step 2. Keep `@param`/`@return` byte-exact. + +- [ ] **Step 3: Regenerate docs.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::document()'` + Expected: writes affected `man/*.Rd`, no errors. + +- [ ] **Step 4: Verify load.** + Run: `Rscript -e 'devtools::load_all(quiet=TRUE); cat("ok\n")'` + Expected: `ok`. + +- [ ] **Step 5: Commit.** +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests +git add R/gg_vimp.R R/plot.gg_vimp.R R/gg_partial.R R/plot.gg_partial.R \ + R/gg_partial_rfsrc.R R/gg_rfsrc.R R/plot.gg_rfsrc.R R/gg_survival.R \ + R/plot.gg_survival.R R/gg_brier.R R/plot.gg_brier.R man/ +git commit -m "docs: deepen rfsrc importance/partial/survival roxygen (vimp-vs-varpro, ensemble framing)" +``` + +--- + +## Task 3: drift-check roxygen (lighter) + +**Files (read; edit ONLY where voice has regressed or content is stale):** +- `R/gg_roc.R`, `R/plot.gg_roc.R`, `R/calc_roc.R` (holds `calc_roc`/`calc_auc`) +- `R/gg_variable.R`, `R/plot.gg_variable.R` +- `R/gg_error.R`, `R/plot.gg_error.R` +- `R/kaplan.R` / `R/nelson.R` (or wherever `kaplan`/`nelson` are documented — grep) +- `R/print_methods.R`, `R/summary_methods.R`, `R/autoplot_methods.R` (the `print.gg`/`summary.gg`/`autoplot.gg` doc pages) +- `R/quantile_pts.R`, `R/varpro_feature_names.R`, the package doc (`R/ggRandomForests-package.R` or `R/help.R` — grep) + +- [ ] **Step 1: Locate the doc homes.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && grep -rln "kaplan\|nelson\|quantile_pts\|varpro_feature_names\|@docType\|_PACKAGE" R/ | sort -u` + Use the result to confirm exact files for `kaplan`/`nelson`/`quantile_pts`/`varpro_feature_names`/package doc. + +- [ ] **Step 2: Read each doc block, apply the VOICE STANDARD as a checklist.** Fix only: anti-slop banned words/structures that slipped in, AI-tell structures, overstatement, stale references (args/behavior that changed). Do NOT rewrite prose that already reads in-voice and is accurate. If a block is clean, leave it. + +- [ ] **Step 3: Regenerate docs.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::document()'` + Expected: no errors; only touched topics' Rd change. + +- [ ] **Step 4: Verify load.** + Run: `Rscript -e 'devtools::load_all(quiet=TRUE); cat("ok\n")'` + Expected: `ok`. + +- [ ] **Step 5: Commit** the touched `R/*.R` + `man/`. +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add -u R/ man/ && git commit -m "docs: voice/drift cleanup on remaining roxygen topics" +``` + +--- + +## Task 4a: Vignette — varpro.qmd + +**Files:** `vignettes/varpro.qmd`, `vignettes/ggRandomForests.bib` + +- [ ] **Step 1: Read `vignettes/varpro.qmd` and the UPSTREAM SOURCE FRAMING.** This is the highest-value vignette for varPro deepening. + +- [ ] **Step 2: Deepen the prose (narrative register)** — add the release-rules / no-synthetic-features framing and the varPro-vs-permutation-VIMP contrast where it teaches; apply the VOICE STANDARD; fix any drift since #92. Do NOT change code chunks except to fix a chunk that errors (a code bug → Bug Handling). + +- [ ] **Step 3: Add references.** Append to `vignettes/ggRandomForests.bib` (only if not already present — grep first) BibTeX for: Lee, Chen & Ishwaran (2021) *Annals of Statistics* 49:2101-2128 (``); and add the varProtools URL as a `\url{}`/footnote in the vignette where varPro is introduced. Cite the new bib key in the vignette where the method is described. + +- [ ] **Step 4: Render the vignette to confirm it builds.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'quarto::quarto_render("vignettes/varpro.qmd")'` + Expected: renders without error (HTML produced). If a code chunk errors, triage per Bug Handling. + +- [ ] **Step 5: Commit** `vignettes/varpro.qmd` + `vignettes/ggRandomForests.bib` (do NOT commit rendered `.html`/`_files`/`.quarto` — they're `.Rbuildignore`d/gitignored; verify `git status` and stage only the source + bib). +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add vignettes/varpro.qmd vignettes/ggRandomForests.bib && git commit -m "docs(vignette): deepen varpro — release-rules framing, refs" +``` + +--- + +## Task 4b: Vignette — ggRandomForests-regression.qmd + +**Files:** `vignettes/ggRandomForests-regression.qmd`, `vignettes/ggRandomForests.bib` + +- [ ] **Step 1: Read the vignette + UPSTREAM SOURCE FRAMING.** Regression is where the `gg_vimp`-vs-`gg_varpro` contrast lands most naturally. +- [ ] **Step 2: Deepen prose (narrative register)** — add the importance-method contrast where the vignette discusses variable importance; apply VOICE STANDARD; fix drift. Don't alter working code chunks. +- [ ] **Step 3: Add the rfsrc-subsample VIMP reference** to `ggRandomForests.bib` if cited (grep first); add the randomForestSRC.org URL where rfsrc VIMP is introduced. +- [ ] **Step 4: Render.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'quarto::quarto_render("vignettes/ggRandomForests-regression.qmd")'` + Expected: renders without error. +- [ ] **Step 5: Commit** source + bib only. +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add vignettes/ggRandomForests-regression.qmd vignettes/ggRandomForests.bib && git commit -m "docs(vignette): regression — vimp-vs-varpro contrast, refs" +``` + +--- + +## Task 4c: Vignette — ggRandomForests-survival.qmd + +**Files:** `vignettes/ggRandomForests-survival.qmd`, `vignettes/ggRandomForests.bib` + +- [ ] **Step 1: Read the vignette + the rfsrc survival framing** (ensemble survival/CHF/mortality, Brier/CRPS). +- [ ] **Step 2: Deepen prose (narrative register)** where the survival/CHF/mortality/Brier discussion benefits from the rfsrc framing; apply VOICE STANDARD; fix drift. Don't alter working code chunks. +- [ ] **Step 3: Add references** (randomForestSRC survival article URL; any missing survival refs) to the bib/vignette where cited. +- [ ] **Step 4: Render.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'quarto::quarto_render("vignettes/ggRandomForests-survival.qmd")'` + Expected: renders without error. +- [ ] **Step 5: Commit** source + bib only. +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add vignettes/ggRandomForests-survival.qmd vignettes/ggRandomForests.bib && git commit -m "docs(vignette): survival — rfsrc ensemble framing, refs" +``` + +--- + +## Task 4d: Vignette — ggRandomForests.qmd (intro) + +**Files:** `vignettes/ggRandomForests.qmd` + +- [ ] **Step 1: Read the intro vignette.** It orients the whole package; lighter touch. +- [ ] **Step 2: Drift/voice pass (narrative register)** — fix anti-slop/AI-tells/staleness; ensure the package framing names both engines (rfsrc + varPro) accurately. Add the importance-method contrast in one or two sentences if it fits the overview. +- [ ] **Step 3: Render.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'quarto::quarto_render("vignettes/ggRandomForests.qmd")'` + Expected: renders without error. +- [ ] **Step 4: Commit** source only. +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add vignettes/ggRandomForests.qmd && git commit -m "docs(vignette): intro — voice/drift pass" +``` + +--- + +## Task 5: R/ code comments — correctness + gaps + +**Files:** all of `R/*.R` (45 files), in file-groups to keep each subagent focused. Suggested groups: (a) `gg_*.R` extractors, (b) `plot.gg_*.R` methods, (c) `calc_*.R`/`*roc*`/survival internals, (d) `utilities_*.R` + helpers + `print_/summary_/autoplot_methods.R`. + +- [ ] **Step 1: For each file-group, read the source.** Identify comments that are (i) factually wrong/stale (reference removed args, describe old behavior), or (ii) absent where the *why* of a non-obvious block is undocumented. Do NOT comment self-explanatory code; do NOT restyle correct comments. + +- [ ] **Step 2: Edit comments (terse register, "why" not "what").** Fix stale comments; add a one-line "why" only where logic is genuinely non-obvious (e.g. a column-major melt, a polarity flip, a defensive `%||%`). If a stale comment reveals a code bug, apply Bug Handling. + +- [ ] **Step 3: Verify load + lint clean.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::load_all(quiet=TRUE); cat("ok\n")'` + Expected: `ok` (comments don't affect load; this just guards against an accidental code edit). + +- [ ] **Step 4: Commit per file-group.** +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add -u R/ && git commit -m "docs(comments): correctness + gap pass on " +``` + +--- + +## Task 6: NEWS — v3.1.0 heading (version line unchanged) + +**Files:** `NEWS.md` + +- [ ] **Step 1: Add a new top section** above the `ggRandomForests v3.0.0` heading. Do NOT change line 2 (`Version: 3.0.0`). Insert: +``` +ggRandomForests v3.1.0 (development) +==================================== +* Documentation pass: deepened the varPro-family and rfsrc + importance/partial/survival help pages against the upstream + randomForestSRC and varPro documentation, and made the distinction + between `gg_vimp()` (rfsrc permutation importance) and `gg_varpro()` + (varPro release-rule importance) explicit. Vignette and code-comment + cleanup throughout. No user-facing behaviour change. + +``` +(If Bug Handling added code-fix bullets during Tasks 1-5, they live under this +heading too — keep this doc bullet first.) + +- [ ] **Step 2: Confirm the NEWS-version test still passes** (version line is still `3.0.0`, matching DESCRIPTION). + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::load_all(quiet=TRUE); testthat::test_file("tests/testthat/test_ggrandomforests_news.R")'` + Expected: pass (DESCRIPTION `3.0.0` still present as NEWS line 2). + +- [ ] **Step 3: Commit.** +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && git add NEWS.md && git commit -m "docs(NEWS): open v3.1.0 development heading (version unchanged)" +``` + +--- + +## Task 7: Verification gate + held draft PR + +**Files:** none (verification + PR). + +- [ ] **Step 1: Regenerate + spell-check.** + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && Rscript -e 'devtools::document(); print(devtools::spell_check())'` + Expected: document clean; review the spell_check output — add genuine technical terms to `inst/WORDLIST` (grep for it; create if absent), fix real typos. Commit any WORDLIST/typo fixes. + +- [ ] **Step 2: Full `R CMD check --as-cran` WITH the manual build** (standing release gate; never `--no-manual`). + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && _R_CHECK_FORCE_SUGGESTS_=false Rscript -e 'rcmdcheck::rcmdcheck(args=c("--as-cran"), error_on="never")'` + Expected: 0 errors, 0 warnings, 0 notes (version is `3.0.0`, so no dev-version NOTE). Remove any stray `tests/testthat/Rplots.pdf` before building. + +- [ ] **Step 3: Confirm all 4 vignettes render** (already done per-task; final guard). + Run: `cd /Users/ehrlinj/Documents/GitHub/ggRandomForests && for v in vignettes/*.qmd; do Rscript -e "quarto::quarto_render('$v')" || echo "FAILED: $v"; done` + Expected: no FAILED lines. + +- [ ] **Step 4: Drift spot-check.** For 3-4 of the deepened functions, diff the new `@param`/`@return`/`@details` claims against the function body; confirm no claim contradicts the code. Report the spot-check result. + +- [ ] **Step 5: Restore any pruned snapshots / clean strays**, then push and open the HELD draft PR. +```bash +cd /Users/ehrlinj/Documents/GitHub/ggRandomForests +git checkout -- tests/testthat/_snaps/ 2>/dev/null; rm -f tests/testthat/Rplots.pdf +git push -u origin docs/v3.1.0-doc-sweep +gh pr create --base main --draft \ + --title "docs: v3.1.0 documentation sweep (HELD — do not merge until v3.0.0 accepted)" \ + --body "Documentation sweep per dev/plans/2026-05-29-v3.1.0-doc-sweep-design.md. Version stays 3.0.0; cut to 3.1.0 at the post-acceptance RC. DRAFT/HELD: do NOT merge until CRAN accepts v3.0.0. R CMD check --as-cran: 0/0/0." +``` + +- [ ] **Step 6: Address Copilot review, resolve threads** — but leave the PR as a draft / unmerged. Merge happens only post-acceptance (then cut to 3.1.0, finalize cran-comments.md, full gate, submit, forward-merge main→dev). + +--- + +## Self-Review + +**Spec coverage:** Deep-content varpro roxygen → Task 1 ✓; deep-content rfsrc roxygen → Task 2 ✓; gg_vimp-vs-gg_varpro distinction in both + cross-@seealso → Tasks 1+2 Step 2 ✓; drift-check roxygen → Task 3 ✓; 4 vignettes → Tasks 4a-4d ✓; upstream URLs + Lee(2021)/rfsrc-subsample refs → Tasks 4a/4b/4c Step 3 ✓; code comments → Task 5 ✓; voice standard + anti-slop embedded inline → VOICE STANDARD block (referenced by every task) ✓; bug handling w/ severity routing → BUG HANDLING block ✓; NEWS v3.1.0 heading without version bump → Task 6 ✓; version stays 3.0.0 → stated in header + Task 6 ✓; verification (document/check-with-manual/render/spell_check/drift) → Task 7 ✓; held draft PR into main → Task 7 Step 5 ✓. + +**Placeholder scan:** Task 3 Step 1 and Task 7 Step 1 use `grep` to locate exact files (kaplan/nelson/quantile_pts/package-doc, WORDLIST) rather than guessing paths — deliberate, not a placeholder; the grep command is given. No TBD/TODO. Voice rules + banned list are inline, not referenced-by-link. + +**Consistency:** function→file map verified against the branch (plot.gg_partial_rfsrc lives in R/plot.gg_partial.R, noted in Task 2). Version-stays-3.0.0 consistent in header, Task 6, Task 7. NEWS heading text matches the doc-bullet-first rule from Bug Handling. diff --git a/man/gg_beta_varpro.Rd b/man/gg_beta_varpro.Rd index aa95b033..eaaebf45 100644 --- a/man/gg_beta_varpro.Rd +++ b/man/gg_beta_varpro.Rd @@ -14,17 +14,17 @@ classification family).} ignored otherwise (with a warning). Documented forwardables: \code{use.cv}, \code{use.1se}, \code{nfolds}, \code{maxit}, \code{thresh}, \code{max.rules.tree}, \code{max.tree}.} -\item{cutoff}{Selection threshold on \code{beta_mean}. \code{NULL} (default) → +\item{cutoff}{Selection threshold on \code{beta_mean}. \code{NULL} (default) means \code{mean(beta_mean)} across released variables. Numeric scalar otherwise.} \item{beta_fit}{Optional pre-computed \code{\link[varPro:beta.varpro]{varPro::beta.varpro()}} result for -the same \code{object}. \code{NULL} (default) → the wrapper runs \code{beta.varpro()} +the same \code{object}. \code{NULL} (default) means the wrapper runs \code{beta.varpro()} itself. When supplied, must be a \code{varpro}-class object whose \verb{$results} has columns \code{tree / branch / variable / n.oob / imp}.} \item{which_class}{For a classification fit, name of a single response level to subset on. \code{NULL} (default) returns all classes (binary fits -resolve to the \emph{last} factor level — the positive-class convention +resolve to the \emph{last} factor level, the positive-class convention used by \code{glm} and \code{gg_roc}). Ignored with a warning on regression fits.} } @@ -55,11 +55,22 @@ unsupported-family path errors with a message pointing at that work. } \section{What this is doing}{ -For each rule (a tree-branch pair) in the forest, \code{\link[varPro:beta.varpro]{varPro::beta.varpro()}} -fits a one-predictor lasso regression of the response on the released -variable's values, restricted to the OOB observations inside the rule's -region. The wrapper aggregates those per-rule coefficients into one -number per variable. +Think of the varPro release-rule mechanism as asking: "given a region of +the feature space that the forest carved out, what changes when I remove +the constraint on this one variable and let observations leave?" The +standard importance answer (from \code{\link[=gg_varpro]{gg_varpro()}}) measures that change as a +z-scored contrast between local estimators: no synthetic data, no +permutation. \code{beta.varpro()} asks the same question with a different +ruler: for each rule (a tree-branch pair), it fits a one-predictor lasso +regression of the response on the released variable's values, restricted +to the OOB observations inside the rule's region. The wrapper aggregates +those per-rule coefficients into one number per variable. + +The key distinction from \code{\link[=gg_vimp]{gg_vimp()}}, which measures Breiman-Cutler +permutation importance by perturbing a variable's values and watching OOB +error climb, is that neither \code{\link[=gg_varpro]{gg_varpro()}} nor \code{gg_beta_varpro()} +touches the data synthetically: all contrasts are between real subsets +defined by the forest's rules. } \section{What \code{imp} actually is (pedantic, because the column name is misleading)}{ @@ -116,7 +127,7 @@ Provenance attribute carries \code{source}, \code{family}, \code{ntree}, \code{c \section{What you use this for}{ Picking variables when local effects matter more than aggregate -split-strength contribution. Compare side-by-side with \code{\link[=gg_varpro]{gg_varpro()}} — +split-strength contribution. Compare side-by-side with \code{\link[=gg_varpro]{gg_varpro()}}: a variable that scores high here but low in \code{gg_varpro} is one whose local linear effect inside many rules is real even though its release-rule contrast is modest. @@ -148,7 +159,7 @@ pedantic-beta semantics as regression, applied independently to each class. \strong{Binary default}: \code{which_class = NULL} resolves to the \emph{last} -factor level of the response — the positive-class convention used +factor level of the response, the positive-class convention used by \code{glm} and \code{gg_roc}. For a 30-day-mortality outcome with levels \code{c("no", "yes")}, that means the wrapper shows you \code{"yes"} (the event) by default. @@ -176,7 +187,7 @@ plot(gg) Byte-for-byte agreement between cached (\code{beta_fit = b}) and uncached (\code{beta_fit = NULL}) outputs requires that \code{b} was computed by -\code{beta.varpro(object, ...)} on the same \code{object} — \code{set.seed()} alone is +\code{beta.varpro(object, ...)} on the same \code{object}; \code{set.seed()} alone is not sufficient, because \code{beta.varpro}'s internal \code{cv.glmnet} fits can pick slightly different folds across separate calls. Reuse \code{beta_fit} when reproducibility matters. @@ -195,5 +206,5 @@ if (requireNamespace("varPro", quietly = TRUE)) { } \seealso{ -\code{\link[=gg_varpro]{gg_varpro()}}, \code{\link[=plot.gg_beta_varpro]{plot.gg_beta_varpro()}}, \code{\link[varPro:beta.varpro]{varPro::beta.varpro()}}. +\code{\link[=gg_varpro]{gg_varpro()}}, \code{\link[=gg_vimp]{gg_vimp()}}, \code{\link[=plot.gg_beta_varpro]{plot.gg_beta_varpro()}}, \code{\link[varPro:beta.varpro]{varPro::beta.varpro()}}. } diff --git a/man/gg_brier.Rd b/man/gg_brier.Rd index a791f2e4..403e8bda 100644 --- a/man/gg_brier.Rd +++ b/man/gg_brier.Rd @@ -37,25 +37,37 @@ The integrated CRPS (a single scalar matching \description{ The Brier score asks a familiar question of any probabilistic forecast: how far did the predicted probability sit from what actually happened? -For a survival forest the forecast is the predicted survival probability, -and the score is computed at each event time, so the result is a curve -rather than a single number -- lower is better, at every time. +For a survival forest the forecast is the predicted survival probability +at a given moment, and the "what happened" is whether the subject was +still alive at that moment. The score is computed at every event time, +so you get a curve rather than a single number -- lower is better +everywhere. A perfectly calibrated forest that predicts \code{0} for +every subject who died and \code{1} for every subject who survived would +score \code{0}; a forest that predicts \code{0.5} for everyone scores +roughly \code{0.25} regardless of the true outcome -- that is the +"uninformative" ceiling. } \details{ -This function extracts that time-resolved Brier score for a survival -forest grown with \code{randomForestSRC}, both overall and split by -mortality-risk quartile. It also returns the continuous ranked -probability score (CRPS), which is the Brier score integrated over time -and divided by elapsed time -- a running average of the curve so far. +This function extracts the time-resolved Brier score for a survival +forest grown with \code{randomForestSRC}, both overall and broken down +by mortality-risk quartile (lowest-risk to highest-risk subjects). It +also returns the continuous ranked probability score (CRPS) -- the Brier +score integrated over time and divided by elapsed time, a running average +that summarises calibration up to each point on the time axis. -This wraps \code{\link[randomForestSRC]{get.brier.survival}} and -rebuilds the quartile decomposition and running CRPS from the returned -\code{brier.matx} and \code{mort} components, following the computation -in the internal \code{plot.survival} function of \pkg{randomForestSRC}. -Right-censored data make a plain Brier score biased, so the score uses -inverse-probability-of-censoring weighting. The censoring distribution -is estimated either by Kaplan-Meier (\code{cens.model = "km"}, the -default) or by a separate censoring forest (\code{cens.model = "rfsrc"}). +Because subjects are right-censored, a plain Brier score is biased: +censored subjects contribute no outcome information yet still inflate the +denominator. The score here uses inverse-probability-of-censoring +weighting (IPCW), which up-weights uncensored observations to compensate. +The censoring distribution is estimated either by Kaplan-Meier +(\code{cens.model = "km"}, the default) or by a separate censoring +forest (\code{cens.model = "rfsrc"}) when the censoring mechanism is +itself covariate-dependent. + +Internally, this wraps \code{\link[randomForestSRC]{get.brier.survival}} +and rebuilds the quartile decomposition and running CRPS from the returned +\code{brier.matx} and \code{mort} components, following the approach in +the internal \code{plot.survival} of \pkg{randomForestSRC}. } \note{ Brier score / CRPS is \code{randomForestSRC} survival-only; there diff --git a/man/gg_isopro.Rd b/man/gg_isopro.Rd index 5276b771..2a76b00c 100644 --- a/man/gg_isopro.Rd +++ b/man/gg_isopro.Rd @@ -30,7 +30,7 @@ one row per observation. Columns: order as the rows of the data passed to \code{\link[varPro]{isopro}}.} \item{case.depth}{Numeric; mean isolation depth across the forest. -Lower means the observation was isolated quickly — more +Lower means the observation was isolated quickly, so more anomalous.} \item{howbad}{Numeric in \code{[0, 1]}; the \code{case.depth} values pushed through their own empirical CDF and flipped so @@ -54,7 +54,7 @@ observation lands in its own terminal node. The intuition is geometric: a typical observation sits in the dense middle of the feature cloud and takes many splits to isolate, while an unusual observation sits out near an edge and gets cut off after only a few. So \strong{the depth at -which an observation is isolated is a proxy for how typical it is} — +which an observation is isolated is a proxy for how typical it is}: shallow depth means anomalous, deep depth means ordinary. Average a single observation's depth across many trees and the noise washes out, leaving a stable per-observation rank. @@ -106,7 +106,7 @@ for a manual review; against a fitted model and compare the test scores to the training distribution. } -The score is a \emph{rank}, not a probability of being an outlier — two +The score is a \emph{rank}, not a probability of being an outlier: two observations with \code{howbad = 0.92} are both unusual, not "92\\% likely to be anomalous". Pick a cutoff by looking at where the elbow rises; \code{\link{plot.gg_isopro}} can annotate either a score @@ -126,7 +126,7 @@ more anomalous}, which is the opposite polarity of the wrapper's \code{howbad} (where \emph{higher} is more anomalous). The wrapper exposes both conventions so nothing is hidden: \itemize{ -\item \code{case.depth} carries varPro's native polarity — \emph{lower +\item \code{case.depth} carries varPro's native polarity, \emph{lower = more anomalous}. This is the unmodified output of \code{predict(object, newdata, quantiles = FALSE)}. Use it to cross-reference against raw varPro output. diff --git a/man/gg_ivarpro.Rd b/man/gg_ivarpro.Rd index be7d0807..eb6ff7f3 100644 --- a/man/gg_ivarpro.Rd +++ b/man/gg_ivarpro.Rd @@ -63,15 +63,27 @@ path errors with a message naming v3.1.0 as the tracker. } \section{What this is doing}{ -\code{ivarpro()} walks the varPro forest's rules and, for each -(observation, variable) pair, computes a scaled per-rule -contribution to predicting that observation. Per-rule LOO removes -the observation from its own rule before scoring. Per-region -scaling (\code{scale = "local"}, default) standardises the contribution -by the rule's local response standard deviation so values are -comparable across rules of different size. Aggregating those -per-rule scores into one number per (obs, variable) pair gives the -\code{local_imp} cell. +The varPro framework builds importance from release rules: for a given +rule region, it compares a local estimator inside that region to what +the estimator becomes after the constraint on the tested variable is +removed ("released"). That contrast is summed over many rules and trees +to get a global z-score: the quantity \code{\link[=gg_varpro]{gg_varpro()}} shows. What +\code{ivarpro()} adds is a per-observation view of the same mechanism. + +Concretely: \code{ivarpro()} walks the forest's rules and, for each +(observation, variable) pair, computes a scaled per-rule contribution +to predicting that observation. Per-rule LOO removes the observation +from its own rule before scoring, so the contribution is not inflated +by the observation having helped define the region. Per-region scaling +(\code{scale = "local"}, default) standardises the contribution by the +rule's local response standard deviation so values are comparable +across rules of different size. Aggregating those per-rule scores into +one number per (obs, variable) pair gives the \code{local_imp} cell. + +No permutation, no synthetic data: the contrast is always between real +subsets of the observed data, defined by the forest's own rules. This +is the same no-synthetic-features property that distinguishes +\code{\link[=gg_varpro]{gg_varpro()}} from \code{\link[=gg_vimp]{gg_vimp()}}'s Breiman-Cutler permutation importance. } \section{What \code{local_imp} actually is (pedantic)}{ @@ -174,5 +186,5 @@ if (requireNamespace("varPro", quietly = TRUE) && } \seealso{ -\code{\link[=gg_varpro]{gg_varpro()}}, \code{\link[=gg_beta_varpro]{gg_beta_varpro()}}, \code{\link[varPro:ivarpro]{varPro::ivarpro()}}. +\code{\link[=gg_varpro]{gg_varpro()}}, \code{\link[=gg_vimp]{gg_vimp()}}, \code{\link[=gg_beta_varpro]{gg_beta_varpro()}}, \code{\link[varPro:ivarpro]{varPro::ivarpro()}}. } diff --git a/man/gg_partial.Rd b/man/gg_partial.Rd index c391cc85..06f0d681 100644 --- a/man/gg_partial.Rd +++ b/man/gg_partial.Rd @@ -27,11 +27,24 @@ as a factor, for low-cardinality / categorical variables} } } \description{ -Takes the list returned by \code{rfsrc::plot.variable(partial = TRUE)} and -separates the variables into two data frames: one for continuous predictors -and one for categorical (factor-like) predictors. The split is controlled -by \code{cat_limit}: variables with more unique x-values than this threshold -are treated as continuous; all others are categorical. +A partial dependence curve answers a what-if question about a forest: hold +every other predictor at its observed value, sweep one of them across its +range, and watch how the ensemble prediction moves. Marginalized over the +joint distribution of the other variables, the resulting curve isolates the +average effect of the swept predictor alone. +} +\details{ +\code{gg_partial} handles the bookkeeping step after you've already called +\code{rfsrc::plot.variable(partial = TRUE)}: it takes the list that function +returns and separates the variables into two tidy data frames -- one for +continuous predictors (plotted as lines) and one for categorical predictors +(plotted as bar charts). The split is controlled by \code{cat_limit}: +variables with more unique x-values than this threshold are treated as +continuous; all others are categorical. + +If you'd rather skip the \code{plot.variable} step and pass the fitted +forest directly, see \code{\link{gg_partial_rfsrc}}, which calls +\code{partial.rfsrc} for you. } \note{ Partial-dependence extraction is \code{randomForestSRC}-only; @@ -45,7 +58,7 @@ airq <- na.omit(airquality) rf <- randomForestSRC::rfsrc(Ozone ~ ., data = airq, ntree = 50) ## Compute partial dependence via plot.variable (show.plots = FALSE to -## suppress the base-graphics output — we only want the data) +## suppress the base-graphics output, we only want the data) pv <- randomForestSRC::plot.variable(rf, partial = TRUE, show.plots = FALSE) diff --git a/man/gg_partial_rfsrc.Rd b/man/gg_partial_rfsrc.Rd index 39e54a23..29f73bf0 100644 --- a/man/gg_partial_rfsrc.Rd +++ b/man/gg_partial_rfsrc.Rd @@ -31,7 +31,7 @@ partial effects at. Defaults to the training data stored in \item{partial.time}{Numeric vector of desired time points for survival forests (ignored for regression/classification). Values are automatically -snapped to the nearest entry in \code{rf_model$time.interest} — see the +snapped to the nearest entry in \code{rf_model$time.interest}; see the \strong{Survival forests} section below. When \code{NULL} (default), three quartile points of \code{time.interest} are used.} @@ -64,15 +64,27 @@ only) for all continuous predictors.} } } \description{ -A partial dependence curve answers a what-if question: hold the rest of -the predictors as they are, sweep one of them across its range, and watch -how the forest's prediction moves. This function builds those curves for -one or more predictors by calling -\code{\link[randomForestSRC]{partial.rfsrc}} for you, then splits the -result into separate data frames for continuous and categorical -variables. Unlike \code{\link{gg_partial}}, there is no separate -\code{plot.variable} step -- pass the fitted \code{rfsrc} object straight -in. +A partial dependence curve marginalizes the forest's prediction over all +other predictors: for each evaluation point of the target variable, the +forest scores every training observation with that value substituted in, +then averages the result. What you get is the average effect of the target +variable after "integrating out" the rest -- a curve that would be flat if +the variable carried no signal. +} +\details{ +This function builds those curves for one or more predictors by calling +\code{\link[randomForestSRC]{partial.rfsrc}} and then tidy-stacking the +results into separate data frames for continuous and categorical variables. +Unlike \code{\link{gg_partial}} (which wraps \code{plot.variable}), you +pass the fitted \code{rfsrc} object directly -- no intermediate +\code{plot.variable} step. + +For survival forests, the marginalized quantity depends on +\code{partial.type}: survival probability (\code{"surv"}), cumulative +hazard function (\code{"chf"}), or expected mortality (\code{"mort"}). +You can request the curve at one or more time horizons via +\code{partial.time}; the resulting data have a \code{time} column so the +plot layers them as separate coloured lines. } \section{Survival forests and \code{partial.time}}{ diff --git a/man/gg_partial_varpro.Rd b/man/gg_partial_varpro.Rd index 1a3ad5f6..a991550c 100644 --- a/man/gg_partial_varpro.Rd +++ b/man/gg_partial_varpro.Rd @@ -136,9 +136,9 @@ usual rfsrc scale instead. \itemize{ \item read the marginal shape of a relationship the varpro model -found important — monotone, threshold, U-shape, flat; +found important: monotone, threshold, U-shape, flat; \item compare the three partialpro estimators on the same variable -and flag the ones where parametric and nonparametric disagree — +and flag the ones where parametric and nonparametric disagree, those are the candidates for closer inspection; \item report a survival partial dependence on the probability or cumulative-hazard scale (\code{scale = "surv"} or \code{"chf"}) @@ -181,6 +181,7 @@ Random survival forests. \emph{The Annals of Applied Statistics}, } \seealso{ \code{\link{plot.gg_partial_varpro}}, +\code{\link{gg_varpro}}, \code{\link{gg_vimp}}, \code{\link{gg_partialpro}} (deprecated), \code{\link{gg_partial_rfsrc}}, \code{\link{varpro_feature_names}} diff --git a/man/gg_rfsrc.rfsrc.Rd b/man/gg_rfsrc.rfsrc.Rd index 95c599d0..2927cfd5 100644 --- a/man/gg_rfsrc.rfsrc.Rd +++ b/man/gg_rfsrc.rfsrc.Rd @@ -55,11 +55,26 @@ The object carries class attributes for the forest family so that \code{\link{plot.gg_rfsrc}} dispatches correctly. } \description{ -Extracts the predicted response values from the -\code{\link[randomForestSRC]{rfsrc}} object, and formats data for plotting -the response using \code{\link{plot.gg_rfsrc}}. +Every tree in a random forest makes its own prediction, and the forest's +"ensemble" prediction is the average across all trees. The out-of-bag +(OOB) variant averages only over the trees that did not include a given +observation in their bootstrap sample -- a built-in cross-validation +estimate that requires no held-out test set. \code{gg_rfsrc} pulls those +ensemble predictions out of the fitted forest and arranges them for plotting +with \code{\link{plot.gg_rfsrc}}. } \details{ +The structure of the returned data depends on the forest family. For a +regression forest you get a scatter of OOB predicted values against the +observed response. For classification you get predicted class probabilities +alongside the observed class label. For a survival forest you get the +ensemble survival function (or cumulative hazard, or mortality, controlled +by \code{surv_type}) at each unique event time -- one curve per +observation -- which together trace the range of predicted risk in the +cohort. Pass \code{conf.int} to add pointwise bootstrap confidence bands +around the mean survival curve, or \code{by} to stratify all of the above +by a predictor group. + For survival forests, use the \code{surv_type} argument (\code{"surv"}, \code{"chf"}, or \code{"mortality"}) to select the predicted quantity. Bootstrap confidence bands are requested by passing diff --git a/man/gg_roc.rfsrc.Rd b/man/gg_roc.rfsrc.Rd index 10cce20c..ac05394f 100644 --- a/man/gg_roc.rfsrc.Rd +++ b/man/gg_roc.rfsrc.Rd @@ -47,7 +47,7 @@ threshold, with columns: \describe{ \item{sens}{Sensitivity (true positive rate) at the threshold.} \item{spec}{Specificity (true negative rate) at the threshold.} -\item{yvar}{The observed class label for each observation.} +\item{pct}{The probability threshold used for that row.} } Pass it to \code{\link{calc_auc}} for the area under the curve. } diff --git a/man/gg_survival.Rd b/man/gg_survival.Rd index b7a57300..54470314 100644 --- a/man/gg_survival.Rd +++ b/man/gg_survival.Rd @@ -70,16 +70,28 @@ and optionally \code{groups} when \code{by} is supplied. Nonparametric survival estimates. } \details{ -\code{gg_survival} is an S3 generic for generating nonparametric -survival estimates. It dispatches on the class of its first argument: +Comparing the forest's ensemble survival curve to the marginal +Kaplan-Meier baseline is a quick sanity check: if they diverge the forest +has found structure the predictors carry; if they track each other closely +the predictors may add little. \code{gg_survival} computes +the nonparametric baseline -- the Kaplan-Meier or Nelson-Aalen estimate -- +so you can place it on the same canvas as the forest predictions from +\code{\link{gg_rfsrc}}. + +\code{gg_survival} is an S3 generic that dispatches on the class of its +first argument: \describe{ -\item{\code{rfsrc}}{Extracts the response data from the fitted forest and -delegates to \code{\link{kaplan}}. Use the \code{by} argument to -stratify on a predictor stored in the model's \code{xvar} slot.} -\item{default}{Accepts raw survival data columns via the \code{interval}, -\code{censor}, \code{by}, and \code{data} arguments, delegating to -either \code{\link{kaplan}} (default) or \code{\link{nelson}}.} +\item{\code{rfsrc}}{Extracts the outcome columns from the fitted +forest's \code{$yvar} slot (time in column 1, event indicator in +column 2) and delegates to \code{\link{kaplan}}. Use \code{by} to +stratify on a predictor from \code{$xvar}: you get one +Kaplan-Meier curve per group, ready to compare against the forest's +group-specific ensemble curves.} +\item{default}{Accepts raw survival columns directly via +\code{interval}, \code{censor}, and \code{data}. Delegates to +\code{\link{kaplan}} (the default) or \code{\link{nelson}} depending +on \code{type}.} } } \note{ @@ -87,7 +99,7 @@ Survival estimation is \code{randomForestSRC}-only; \code{randomForest} has no survival forest, so no \code{randomForest} method exists. } \examples{ -## -------- pbc data (default method — raw data columns) +## -------- pbc data (default method, raw data columns) data(pbc, package = "randomForestSRC") pbc$time <- pbc$days / 364.25 diff --git a/man/gg_udependent.Rd b/man/gg_udependent.Rd index 1ed082b5..feda7f61 100644 --- a/man/gg_udependent.Rd +++ b/man/gg_udependent.Rd @@ -61,8 +61,8 @@ the unsupervised setting: grow a forest without a response, then use the same region-release contrasts varpro uses for supervised importance to ask, "which variables explain the structure in the data?" The lasso-driven variant frames each region-release contrast -as a classification task — does an observation belong to the region -or to its release? — and fits a lasso logistic regression with the +as a classification task (does an observation belong to the region +or to its release?) and fits a lasso logistic regression with the other variables as predictors. The coefficient on variable \eqn{j} in the model for variable \eqn{i}'s region-release contrast is the entry \eqn{I[i, j]} of the matrix \code{varPro::get.beta.entropy()} @@ -72,7 +72,7 @@ Read that entry as "how much does knowing \eqn{j} help separate \eqn{i}'s region from its release". A large \eqn{I[i, j]} says \eqn{j} carries information about the structure varpro picked up in \eqn{i}. \code{varPro::sdependent} thresholds that matrix at a -user-chosen cut and returns the set of "signal" variables — the +user-chosen cut and returns the set of "signal" variables: the nodes with high enough out-degree to be worth keeping. We pass the threshold through to \code{sdependent} and use the same matrix to weight the edges of the resulting graph. @@ -102,7 +102,7 @@ without recomputing anything. \itemize{ \item screen a wide unsupervised dataset for the small set of -variables UVarPro thinks are carrying the signal — the nodes +variables UVarPro thinks are carrying the signal: the nodes with high degree, or those flagged \code{selected = TRUE}; \item spot clusters of mutually dependent variables (hubs and the spokes around them) that may be measuring the same underlying @@ -111,7 +111,7 @@ construct; looking at how their dependency graphs change. } An edge in this graph is a statistical dependency in the unsupervised -decomposition of the data — it is not a causal arrow. A high +decomposition of the data. It is not a causal arrow. A high \eqn{I[i, j]} says \eqn{j} predicts \eqn{i}'s region membership, not that \eqn{j} causes \eqn{i}. } diff --git a/man/gg_varpro.Rd b/man/gg_varpro.Rd index c2578c26..21d56f33 100644 --- a/man/gg_varpro.Rd +++ b/man/gg_varpro.Rd @@ -74,8 +74,8 @@ importances. \section{What varpro is doing}{ Permutation importance asks "what happens to OOB accuracy when I scramble -this variable?" That works, but it leans on artificial data — the -permuted column — and the answer can be unstable when variables are +this variable?" That works, but it leans on artificial data (the +permuted column) and the answer can be unstable when variables are correlated. The varpro framework (Lu and Ishwaran, 2024) replaces permutation with \emph{release rules}. The forest is grown with guided splitting; from a subset of trees varpro samples a collection of @@ -83,7 +83,7 @@ decision-rule branches; for each variable it then compares the response inside the rule's region to the response after the rule's constraint on that variable is "released". The size of that change, aggregated over many rules and trees, is the variable's importance. -No synthetic covariates, no permutation — the contrast is between two +No synthetic covariates, no permutation: the contrast is between two real subsets of the data. Because varpro builds importance from rules sampled over trees, every @@ -123,7 +123,7 @@ classification. above varpro's z cutoff; \item see, via the boxplot's spread and the per-tree points (\code{faithful = TRUE}), how stable each variable's importance -is across trees — a high median with a wide box is a different +is across trees: a high median with a wide box is a different story from a high median with a tight box; \item for a classification forest, ask which variables drive which class (\code{conditional = TRUE}) rather than just which diff --git a/man/gg_vimp.Rd b/man/gg_vimp.Rd index 6fcf811b..20009d67 100644 --- a/man/gg_vimp.Rd +++ b/man/gg_vimp.Rd @@ -33,6 +33,29 @@ reproducible. \code{\link[randomForestSRC]{rfsrc}} or \code{\link[randomForest]{randomForest}} object and reshapes it into a tidy data set. } +\details{ +\code{gg_vimp()} shows \strong{permutation (Breiman-Cutler) variable +importance}: the forest permutes a variable's observed values across the +out-of-bag (OOB) cases, runs those perturbed cases down the already-grown +trees, and measures how much the OOB prediction error climbs. That +perturbation is synthetic (the variable's link to the response is broken +on purpose) so a large increase means the variable was carrying genuine +signal; near-zero or negative values mean it added noise or nothing at all. + +\code{\link{gg_varpro}()} takes the opposite route, comparing local +estimators on real observed data through varPro's release rules, with no +permutation and no synthetic features. The two approaches answer "which +variables matter?" by opposite mechanisms, so a variable can rank +differently under each, and that disagreement is itself informative: it +often signals interaction structure or non-monotone effects that one +mechanism surfaces and the other obscures. + +For survival forests, VIMP is measured against the ensemble cumulative +hazard function (CHF); the error metric is one minus the concordance index +(C-statistic). Variables with non-positive VIMP are flagged in the +\code{positive} column and colored differently by +\code{\link{plot.gg_vimp}}. +} \examples{ ## ------------------------------------------------------------ ## classification example @@ -161,5 +184,5 @@ forests, \emph{Electronic J. Statist.}, 1:519-537. \seealso{ \code{\link{plot.gg_vimp}} \code{\link[randomForestSRC]{rfsrc}} -\code{\link[randomForestSRC]{vimp}} +\code{\link[randomForestSRC]{vimp}} \code{\link{gg_varpro}} } diff --git a/man/plot.gg_beta_varpro.Rd b/man/plot.gg_beta_varpro.Rd index 2da331f9..d34a4135 100644 --- a/man/plot.gg_beta_varpro.Rd +++ b/man/plot.gg_beta_varpro.Rd @@ -24,8 +24,8 @@ selection cutoff, grey otherwise. Dashed red line marks the cutoff. \section{Reading the chart}{ Each bar is the average magnitude of a per-rule lasso coefficient for -that variable. \strong{The numeric scale carries the predictor's units} — -if "age" is in years and "creatinine" is in mg/dL, a longer bar for +that variable. \strong{The numeric scale carries the predictor's units.} +If "age" is in years and "creatinine" is in mg/dL, a longer bar for age does not mean age is "more important" in any unit-free sense. Comparisons across data sets or across variables with very different units require keeping the units context in mind. Within one data set, @@ -34,7 +34,7 @@ bars are comparable up to that unit caveat. Variables above the cutoff are coloured blue and flagged \code{selected}; variables below are grey. Lasso shrinkage can drive a rule's \eqn{\hat{\beta}}{beta hat} to -exactly zero — those rules are kept in the average, so a variable +exactly zero; those rules are kept in the average, so a variable with many shrunk-to-zero rules will sit lower in the ranking than one whose released coefficients are consistently non-zero. diff --git a/man/plot.gg_brier.Rd b/man/plot.gg_brier.Rd index c3b8fcca..40290a3a 100644 --- a/man/plot.gg_brier.Rd +++ b/man/plot.gg_brier.Rd @@ -23,11 +23,21 @@ contributions at each time, around the overall line. When \code{FALSE} A \code{ggplot} object. } \description{ -Draws the time-resolved Brier score (the default) or the running CRPS as -a curve against time. Lower means more accurate probabilistic predictions. -Turn \code{envelope = TRUE} on and the overall line picks up a ribbon -spanning the 15th to 85th percentile of the per-subject contributions, -which shows how much the score varies across subjects at each time. +Draws the time-resolved Brier score or the running CRPS from a +\code{\link{gg_brier}} object. The curve moves across the event-time +grid on the x-axis; lower values mean the forest's predicted survival +probabilities are closer to what actually happened. Think of +\code{0} as "perfect" and roughly \code{0.25} as "uninformative" -- a +forest that predicts \code{0.5} for every subject regardless of +prognosis would sit near that ceiling. +} +\details{ +Set \code{envelope = TRUE} to add a ribbon around the overall curve +spanning the 15th to 85th percentile of the per-subject Brier +contributions at each time. The ribbon shows how heterogeneous the +scoring is across subjects: a narrow ribbon means most subjects are +predicted equally well (or equally poorly); a wide ribbon means a +minority of subjects are driving the average. } \examples{ \donttest{ diff --git a/man/plot.gg_isopro.Rd b/man/plot.gg_isopro.Rd index 20a76555..4efa8a97 100644 --- a/man/plot.gg_isopro.Rd +++ b/man/plot.gg_isopro.Rd @@ -15,7 +15,7 @@ \arguments{ \item{x}{A \code{gg_isopro} object from \code{\link{gg_isopro}}.} -\item{panel}{One of \code{"both"} (default — a \code{patchwork} of +\item{panel}{One of \code{"both"} (default: a \code{patchwork} of elbow + density), \code{"elbow"}, or \code{"density"} (each returns a single \code{ggplot}).} @@ -43,8 +43,8 @@ Optionally annotates a threshold either in score-space \section{Reading the elbow}{ The elbow plot is the canonical anomaly-detection picture. The x-axis -is observation rank — observations sorted from most ordinary to most -anomalous — and the y-axis is the \code{howbad} score. For a clean +is observation rank (observations sorted from most ordinary to most +anomalous) and the y-axis is the \code{howbad} score. For a clean population the curve sits flat near zero across the bulk of the data and then bends sharply upward in the right tail; that bend is where the anomalous observations live. The point of the plot is not to read @@ -58,7 +58,7 @@ reference line so you can record the choice you made. The density panel is the same scores viewed as a distribution. A single tight mode near zero with a long thin right tail is the -picture you hope for — bulk of the data ordinary, a few clear +picture you hope for: bulk of the data ordinary, a few clear anomalies. A bimodal density says you may have two populations rather than one clean cluster plus outliers, and the cutoff question becomes harder. Either way, this panel is a sanity check on what the elbow @@ -71,7 +71,7 @@ When the input object carries a \code{method} column (because you bound several \code{\link{gg_isopro}} calls together), both panels colour by method automatically. The point of comparing \code{"rnd"}, \code{"unsupv"}, and \code{"auto"} is not to pick a winner from the -figure alone — it is to see whether the methods agree on which +figure alone, it is to see whether the methods agree on which observations are anomalous. Curves that overlap in the right tail and elbow at roughly the same rank are telling you the same story three ways. Curves that diverge are telling you the score is diff --git a/man/plot.gg_ivarpro.Rd b/man/plot.gg_ivarpro.Rd index 6378a57f..e900c53b 100644 --- a/man/plot.gg_ivarpro.Rd +++ b/man/plot.gg_ivarpro.Rd @@ -37,8 +37,9 @@ visual comparison. Each facet has its own cutoff line. The per-observation view (\code{which_obs}) is a horizontal bar chart of one observation's local importances; bars below the cutoff are grey, -above are blue. Think of it as a SHAP-style waterfall, but the -values are scaled per-rule contributions, not Shapley values. +above are blue. The visual resembles a SHAP waterfall, but the values +are release-rule contributions: scaled per-rule contrasts on observed +data, not Shapley values and not permutation-based. } \examples{ diff --git a/man/plot.gg_partial.Rd b/man/plot.gg_partial.Rd index f7ef8bd0..b082834b 100644 --- a/man/plot.gg_partial.Rd +++ b/man/plot.gg_partial.Rd @@ -19,10 +19,18 @@ combined vertically via \code{patchwork::wrap_plots()}, which also satisfies \code{inherits(p, "ggplot")}. } \description{ -Produces ggplot2 partial dependence curves from the named list returned by -\code{\link{gg_partial}}. Continuous predictors are shown as line plots; -categorical predictors are shown as bar charts. Both panels are faceted by -variable name so multiple predictors can be compared at a glance. +Turns a \code{\link{gg_partial}} object into a ggplot2 figure. Each curve +is a partial dependence trace -- the forest's average prediction as one +predictor is swept across its range while the rest are marginalized over the +training data. Continuous predictors appear as line plots; categorical +predictors appear as bar charts. Both panels are faceted by variable name +so you can compare the shape and scale of each variable's effect at a +glance. +} +\details{ +When a \code{model} label was attached in \code{gg_partial()}, lines are +coloured by model -- handy for overlaying results from two forests (e.g., +one tuned, one default) in the same figure. } \examples{ set.seed(42) diff --git a/man/plot.gg_partial_rfsrc.Rd b/man/plot.gg_partial_rfsrc.Rd index be4ca70e..a2e396ae 100644 --- a/man/plot.gg_partial_rfsrc.Rd +++ b/man/plot.gg_partial_rfsrc.Rd @@ -17,21 +17,27 @@ and categorical variables are present the two panels are combined vertically via \code{patchwork::wrap_plots()}. } \description{ -Produces ggplot2 partial dependence curves from the named list returned by -\code{\link{gg_partial_rfsrc}}. +Renders the partial dependence curves from \code{\link{gg_partial_rfsrc}} +as a ggplot2 figure. The layout adapts automatically to what the object +contains. } \details{ -For standard (non-survival) forests: continuous predictors are line plots, -categorical predictors are bar charts, both faceted by variable name. +For a standard regression or classification forest, continuous predictors +are drawn as line plots and categorical predictors as bar charts, both +faceted by variable name -- the same arrangement as +\code{\link{plot.gg_partial}}. -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 a survival forest, each call to \code{partial.rfsrc} returns a predicted +quantity (survival probability, cumulative hazard function, or mortality) at +one or more chosen time horizons. When a \code{time} column is present in +the data, each horizon becomes a separate coloured curve over the predictor's +value, still faceted by variable. The y-axis label (\dQuote{Predicted +Survival}, \dQuote{Predicted CHF}, or \dQuote{Predicted Mortality}) tracks +the \code{partial.type} attribute set by \code{gg_partial_rfsrc()}. -For two-variable surface plots (when a \code{grp} column is present): -each group level is a separate line, faceted by primary predictor name. +For a two-variable interaction surface (when \code{xvar2.name} was supplied +to \code{gg_partial_rfsrc}), the secondary variable's levels become +separate coloured lines, faceted by the primary predictor. } \examples{ ## ------------------------------------------------------------ diff --git a/man/plot.gg_partial_varpro.Rd b/man/plot.gg_partial_varpro.Rd index 26c0a773..910de5d6 100644 --- a/man/plot.gg_partial_varpro.Rd +++ b/man/plot.gg_partial_varpro.Rd @@ -52,8 +52,8 @@ eye is looking at level-to-level shifts in the centre of each box. Where the three effect types track each other, the parametric story is a fair summary of what the forest is doing. Where they fan -apart — typically the parametric curve smoother than the -nonparametric, or the causal curve flatter than either — the +apart (typically the parametric curve smoother than the +nonparametric, or the causal curve flatter than either) the variable is one to inspect more carefully before reading a single effect off the plot. } diff --git a/man/plot.gg_rfsrc.Rd b/man/plot.gg_rfsrc.Rd index b877be43..b1ec3962 100644 --- a/man/plot.gg_rfsrc.Rd +++ b/man/plot.gg_rfsrc.Rd @@ -47,11 +47,22 @@ coloured by group.} } } \description{ -Plot the predicted response from a \code{\link{gg_rfsrc}} object, the -\code{\link[randomForestSRC]{rfsrc}} prediction, using the OOB prediction -from the forest. The plot type adapts automatically to the forest family: -jitter + boxplot for regression and classification, step curves for -survival. +Visualizes the ensemble predictions extracted by \code{\link{gg_rfsrc}}. +By default those are out-of-bag (OOB) predictions -- the forest's built-in +cross-validation estimate, averaging only over the trees that left a given +observation out of their bootstrap sample. +} +\details{ +The geometry adapts to the forest family. For regression or +classification, you get a jitter-and-boxplot: every observation is a dot +placed at its OOB predicted value, coloured by observed response, with a +notched boxplot overlaid to show the central tendency and spread. For a +survival forest, each observation contributes one ensemble survival curve +(or CHF / mortality, whichever \code{surv_type} was chosen in +\code{gg_rfsrc}); the bundle of step functions shows the spread of +predicted risk across the cohort. Pass \code{by} to \code{gg_rfsrc} +beforehand to colour curves by a predictor group, or \code{conf.int} to +replace the individual curves with a mean curve and bootstrap ribbon. } \examples{ ## ------------------------------------------------------------ diff --git a/man/plot.gg_survival.Rd b/man/plot.gg_survival.Rd index b106e052..881bf9f9 100644 --- a/man/plot.gg_survival.Rd +++ b/man/plot.gg_survival.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/plot.gg_survival.R \name{plot.gg_survival} \alias{plot.gg_survival} -\title{Plot a \code{\link{gg_survival}} object.} +\title{Plot a \code{\link{gg_survival}} object.} \usage{ \method{plot}{gg_survival}( x, @@ -33,7 +33,19 @@ Confidence shading, bars, or lines are added when the input object carries confidence-interval columns. } \description{ -Plot a \code{\link{gg_survival}} object. +Draws a Kaplan-Meier (or Nelson-Aalen) survival curve from a +\code{\link{gg_survival}} object. You can overlay a confidence envelope +around the curve using \code{error}: \code{"shade"} fills the area between +the pointwise confidence limits, \code{"lines"} draws them as dashed step +functions, and \code{"bars"} shows them as error bars. When \code{gg_survival} +was called with a \code{by} argument, each group gets its own step function +and the \code{label} argument renames the legend. +} +\details{ +The \code{type} argument selects which quantity to plot on the y-axis -- +survival probability (\code{"surv"}) is the default, but cumulative hazard, +density, and several transformed scales are available for the cases where a +linear scale reveals more about the tails. } \examples{ ## -------- pbc data diff --git a/man/plot.gg_udependent.Rd b/man/plot.gg_udependent.Rd index 3655f730..2a1cd8f8 100644 --- a/man/plot.gg_udependent.Rd +++ b/man/plot.gg_udependent.Rd @@ -42,7 +42,7 @@ that cleared the threshold passed to \code{gg_udependent}. The Fruchterman-Reingold layout (the default) places mutually connected variables near each other, so the picture tends to show hubs and the clusters around them rather than a tidy ring. The eye usually -goes first to the largest blue node — a variable that is both in +goes first to the largest blue node: a variable that is both in the signal set and connects to many others is a hub of the dependency structure. Edges with wider, more opaque strokes are stronger dependencies; thin, faint edges sit near the threshold and @@ -50,9 +50,9 @@ are the ones that would disappear first if you raised it. Grey, low-degree nodes are the ones UVarPro thinks are not contributing much to the structure. (Truly isolated nodes are -dropped by \code{gg_udependent()} before the graph is drawn — what you +dropped by \code{gg_udependent()} before the graph is drawn; what you see is the connected component.) A cluster of mutually -connected variables is worth checking for redundancy — they may be +connected variables is worth checking for redundancy; they may be several views of the same underlying quantity. } diff --git a/man/plot.gg_varpro.Rd b/man/plot.gg_varpro.Rd index afc1f18b..8201ea21 100644 --- a/man/plot.gg_varpro.Rd +++ b/man/plot.gg_varpro.Rd @@ -50,7 +50,7 @@ Variables are sorted top to bottom by descending median per-tree importance, so the eye lands on the most important variable first. For each variable the box spans the 15th to 85th percentile of the per-tree scores, the centre line is the median, and the whiskers run -out to the 5th and 95th percentile — not the usual Tukey 1.5 IQR +out to the 5th and 95th percentile, not the usual Tukey 1.5 IQR whiskers. The dashed vertical line is the selection \code{cutoff} (default \code{0.79}). On the default z-score axis (\code{local.std = TRUE}) that line is a z; on the raw-importance @@ -77,7 +77,7 @@ across to see which class a variable is informative for. \section{What this tells you}{ Take the variables above the cutoff as your candidate set. Use the -width of the box and the per-tree overlay to gauge confidence — a +width of the box and the per-tree overlay to gauge confidence: a narrow box well above the cutoff is a confident pick, a wide box that crosses it is a coin flip you should not lean on. For classification, conditional importance tells you which variables diff --git a/man/plot.gg_vimp.Rd b/man/plot.gg_vimp.Rd index 413908a1..d6642588 100644 --- a/man/plot.gg_vimp.Rd +++ b/man/plot.gg_vimp.Rd @@ -22,8 +22,19 @@ the same as the variable names.} \code{ggplot} object } \description{ -Plot a \code{\link{gg_vimp}} object, extracted variable importance of a -\code{\link[randomForestSRC]{rfsrc}} object +Draws a horizontal bar chart of the VIMP scores extracted by +\code{\link{gg_vimp}}. Each bar represents one predictor; bar length is +proportional to its permutation VIMP -- the average rise in OOB prediction +error when that predictor's OOB values are randomly shuffled. Predictors +are sorted in descending order of importance so the most influential +variables appear at the top. +} +\details{ +Bars are coloured by the \code{positive} flag: a bar at or below zero +(non-positive VIMP) is colour-coded differently to flag predictors that +\emph{hurt} OOB accuracy when their signal is removed -- usually a sign of +collinearity or a very noisy variable. In a well-behaved forest most bars +are positive; the colour distinction matters when a handful are not. } \examples{ ## ------------------------------------------------------------ diff --git a/tests/testthat/test_gg_vimp.R b/tests/testthat/test_gg_vimp.R index 647d1c4f..c44ffd04 100644 --- a/tests/testthat/test_gg_vimp.R +++ b/tests/testthat/test_gg_vimp.R @@ -331,6 +331,30 @@ test_that("gg_vimp regression", { }) +test_that("gg_vimp.rfsrc single-outcome: positive flag correctly uses the VIMP column", { + # Regression test for the bug where gg_dta$vimp was accessed but the column + # is named "VIMP" (uppercase) in single-outcome rfsrc fits, leaving positive + # always TRUE even for variables with non-positive VIMP. + data(airquality) + rf <- randomForestSRC::rfsrc(Ozone ~ ., data = na.omit(airquality), + ntree = 50, importance = TRUE) + gg_dta <- gg_vimp(rf) + + expect_s3_class(gg_dta, "gg_vimp") + expect_true("positive" %in% colnames(gg_dta)) + # The positive flag should correctly reflect the sign of VIMP: + # variables with vimp <= 0 must have positive == FALSE. + vimp_col <- intersect(c("vimp", "VIMP"), colnames(gg_dta))[1] + neg_mask <- gg_dta[[vimp_col]] <= 0 + if (any(neg_mask, na.rm = TRUE)) { + expect_true(all(!gg_dta$positive[which(neg_mask)]), + info = "Variables with non-positive VIMP must have positive == FALSE") + } + # Even if all VIMP happen to be positive here, the column must exist. + expect_true(length(unique(is.na(gg_dta$positive))) <= 1) + expect_s3_class(plot(gg_dta), "ggplot") +}) + test_that("gg_vimp.randomForest regression: vimp column present even when importance is IncNodePurity", { # Guard test: when randomForest stores importance as IncNodePurity (not X.IncMSE), # gg_vimp must still produce a 'vimp' column so plot.gg_vimp and the positive diff --git a/vignettes/ggRandomForests-regression.qmd b/vignettes/ggRandomForests-regression.qmd index 993e0417..04d6808a 100644 --- a/vignettes/ggRandomForests-regression.qmd +++ b/vignettes/ggRandomForests-regression.qmd @@ -174,9 +174,13 @@ check --- we are more interested in the *why* behind these predictions. ## Variable importance (VIMP) -VIMP measures the increase in prediction error when a variable is randomly -permuted [@Breiman:2001]. Large positive values mean the variable is essential; -negative values suggest noise is more informative. +VIMP, computed by [randomForestSRC](https://www.randomforestsrc.org), measures +the increase in OOB prediction error when a variable's values are randomly +permuted across the out-of-bag observations [@Breiman:2001]. Permutation severs +the variable's link to the response on purpose: if breaking that link hurts +accuracy, the variable is carrying real signal. Large positive values mean the +variable is essential; negative values suggest it is no more informative than +noise. ```{r vimp-plot, fig.cap="VIMP ranking. Longer blue bars indicate more important variables."} plot(gg_vimp(rfsrc_Boston), lbls = st_labs) @@ -186,6 +190,16 @@ plot(gg_vimp(rfsrc_Boston), lbls = st_labs) VIMP values are positive, indicating every predictor contributes at least marginally. +The permutation approach contrasts with varPro release-rule importance +[@Lu2024varpro], available through `gg_varpro()`. Rather than perturbing data +synthetically, varPro compares local estimators on the observed data directly: +no permutation, no manufactured feature values. Because the two methods measure +fundamentally different things, a variable can rank high under one and low under +the other. When they agree, the evidence is strong; when they disagree, that +disagreement itself is worth investigating, pointing either to a variable whose +effect is highly non-linear or to one that matters only in combination with +others. + ## Minimal depth Minimal depth [@Ishwaran:2010] ranks variables by how close to the root node diff --git a/vignettes/ggRandomForests-survival.qmd b/vignettes/ggRandomForests-survival.qmd index 648e552a..e0dc7a9b 100644 --- a/vignettes/ggRandomForests-survival.qmd +++ b/vignettes/ggRandomForests-survival.qmd @@ -42,8 +42,19 @@ extend the method to right-censored, time-to-event data by growing trees with a log-rank splitting rule and aggregating Kaplan--Meier estimates within terminal nodes. -The **randomForestSRC** package [@Ishwaran:RFSRC:2014] provides a unified -implementation for survival, regression, and classification forests. +The forest returns three ensemble quantities for each subject. First, the +survival function $\hat{S}(t)$: the probability of surviving past time $t$, +bounded between 0 and 1. Second, the cumulative hazard function (CHF): +$\hat{H}(t) = -\log \hat{S}(t)$, an unbounded, monotone-increasing summary of +accumulated risk. Third, mortality: the expected number of events a subject +would accumulate if followed indefinitely under their covariate profile, +computed by summing the CHF over the observed time grid. Mortality is an +unbounded relative-risk score, not a probability, and works well as a single +scalar for ranking patients by risk. + +The **[randomForestSRC](https://www.randomforestsrc.org)** package +[@Ishwaran:RFSRC:2014] provides a unified implementation for survival, +regression, and classification forests. **ggRandomForests** extracts tidy data objects from `rfsrc` fits and renders them with **ggplot2** [@Wickham:2009], making it straightforward to explore how a forest is constructed, which variables matter, and how the response depends on @@ -202,6 +213,11 @@ fall outside the biological range. ## Kaplan--Meier survival by treatment +The Kaplan--Meier estimate is the marginal survival curve with no covariate +adjustment. It serves as the no-covariate baseline: once we fit a forest, +comparing the KM curves to the forest's OOB predictions lets us judge how much +predictive signal the covariates add beyond the raw event rate. + We restrict to the 312 trial patients and construct KM curves with `gg_survival`. ```{r km-survival, fig.cap="KM survival estimates by treatment group with 95% confidence bands."} @@ -294,9 +310,13 @@ gg_rsf <- plot(gg_rfsrc(rfsrc_pbc), alpha = 0.2) + gg_rsf ``` -Each curve represents one patient's OOB prediction, extended to the last -follow-up time. Red (death event) curves generally fall faster, confirming the -forest discriminates between risk groups. +Each curve is one patient's OOB ensemble survival function $\hat{S}(t)$, +extended to the last follow-up time. The forest never saw that patient when +building its prediction, so these are genuine out-of-sample estimates. Red +(death event) curves generally fall faster and reach lower survival +probabilities, confirming the forest separates risk groups. Comparing this +spread to the marginal KM curves from the previous section shows how much the +covariates tighten risk stratification. ```{r rfsrc-by-treatment, fig.cap="Median predicted survival by treatment group with 95% confidence bands."} plot(gg_rfsrc(rfsrc_pbc, by = "treatment")) + @@ -334,9 +354,13 @@ examine variable importance (VIMP) and minimal depth. ## Variable importance (VIMP) -VIMP measures the increase in prediction error when a variable is randomly -permuted. Large positive values mean the variable is essential for accuracy; -negative values suggest noise is more informative than the variable. +VIMP measures the increase in prediction error when a variable's values are +randomly permuted across the out-of-bag sample. For survival forests the +prediction error is the Harrell C-statistic complement (1 − C), so permuting +an important variable hurts C and yields a large positive VIMP. A negative VIMP +means the variable is adding noise: the forest would predict better without it. +See the [randomForestSRC](https://www.randomforestsrc.org) documentation for +details on the VIMP calculation for censored outcomes [@Ishwaran:2007a]. ```{r vimp-plot, fig.width=6, fig.cap="Variable importance ranking. Blue = positive VIMP, red = negative."} plot(gg_vimp(rfsrc_pbc), lbls = st_labs) + @@ -376,9 +400,12 @@ xvar_all <- c(xvar, xvar_cat) ## Variable dependence plots -Variable dependence shows each patient's predicted survival at a given time -horizon plotted against a predictor of interest. Points are colored by event -status; a loess smooth indicates the trend. +Variable dependence shows each patient's predicted survival probability +$\hat{S}(t_0)$ at a fixed time horizon $t_0$, plotted against a predictor of +interest. Because $\hat{S}(t_0)$ is a probability, the y-axis is always bounded +to [0, 1]. Points are colored by event status; a loess smooth traces the +overall trend while the individual points show how much variance remains after +the forest accounts for all other covariates. ```{r vardep-bili, fig.height=4, fig.cap="Variable dependence of survival at 1 and 3 years on bilirubin."} gg_v <- gg_variable(rfsrc_pbc, time = c(1, 3), @@ -585,13 +612,25 @@ in the conditional plots. ## Brier Score and CRPS VIMP and minimal depth tell us which predictors matter, but they say nothing -about how good the forest's survival predictions actually are. The Brier -score answers that question. At each point on the event-time grid it compares -the predicted survival probability against what actually happened, with -inverse-probability-of-censoring weighting (IPCW) so that right-censored -subjects do not bias the result [@graf:1999]. `gg_brier()` wraps -`randomForestSRC::get.brier.survival()` and hands back a tidy data frame -ready to plot. +about how well the forest's survival predictions are calibrated. The Brier +score answers that question. At each point on the event-time grid it measures +the mean squared difference between the predicted survival probability +$\hat{S}(t)$ and the binary outcome (survived past $t$ or not). Because +right-censored subjects have unknown true outcomes, the calculation uses +inverse-probability-of-censoring weighting (IPCW): each subject's squared +error is upweighted by the inverse probability of being uncensored at that time, +so the estimate remains unbiased even under heavy censoring [@graf:1999]. + +Two reference values help interpret the curve. A Brier score of 0 is perfect +prediction. A score around 0.25 is the uninformative ceiling: a model that +assigns every subject a survival probability of 0.5, regardless of their +covariates or the time horizon, scores near 0.25 by construction. A forest +that beats 0.25 is doing better than that floor; one that exceeds it has been +made worse by its covariates, which is a sign of overfitting or a poorly +specified model. + +`gg_brier()` wraps `randomForestSRC::get.brier.survival()` and returns a tidy +data frame ready to plot. ```{r brier-overall, fig.width=7, fig.height=3.5, fig.cap="Time-resolved Brier score for the PBC survival forest."} gg_bs <- gg_brier(rfsrc_pbc) @@ -613,10 +652,12 @@ plot(gg_bs, envelope = TRUE) ``` The running CRPS (continuous ranked probability score) is the Brier score -integrated over time and divided by elapsed time, in other words the -time-average of the curve above. It collapses the whole trajectory into one -running number, which is handy when you want a single accuracy figure rather -than a curve to read. +integrated over time and divided by elapsed time — the time-average of the +curve above. It collapses the whole trajectory into one running number, which +is handy when you want a single calibration figure rather than a curve to read. +Like the Brier score, a CRPS near 0 is good and a value near 0.25 is the +uninformative ceiling (the same constant-predictor reference as above). The final (integrated) value at the right edge of the +plot is the one most often reported. ```{r brier-crps, fig.width=7, fig.height=3.5, fig.cap="Running CRPS for the PBC survival forest."} plot(gg_bs, type = "crps") diff --git a/vignettes/ggRandomForests.bib b/vignettes/ggRandomForests.bib index 7d142057..bbb75512 100644 --- a/vignettes/ggRandomForests.bib +++ b/vignettes/ggRandomForests.bib @@ -269,3 +269,14 @@ @article{Lu2024varpro year = {2024}, url = {https://arxiv.org/abs/2409.09003} } + +@article{Lee:2021, + author = {Lee, D. K. and Chen, N. and Ishwaran, H.}, + title = {Boosted nonparametric hazards with time-dependent covariates}, + journal = {The Annals of Statistics}, + year = {2021}, + volume = {49}, + number = {4}, + pages = {2101--2128}, + doi = {10.1214/20-AOS2028} +} diff --git a/vignettes/ggRandomForests.qmd b/vignettes/ggRandomForests.qmd index 85954c5e..aa113531 100644 --- a/vignettes/ggRandomForests.qmd +++ b/vignettes/ggRandomForests.qmd @@ -20,6 +20,9 @@ usually means digging through list structures that were never meant to be plotted directly. **ggRandomForests** does that digging for you: it pulls tidy data objects out of a `randomForestSRC` or `randomForest` fit, and those objects drop straight into the `ggplot2` workflows you already know. +A second engine, `varPro`, powers a parallel family of functions for +release-rule importance and related diagnostics; that family is covered in +the companion vignette referenced at the end. This vignette walks through the three objects you will reach for most often (`gg_error`, `gg_variable`, and `gg_vimp`), plus a small helper for cutting a predictor into evenly populated groups. @@ -88,7 +91,10 @@ head(vimp_df) plot(vimp_df) ``` -Variable importance is not always stored on the fitted object. If a +`gg_vimp()` measures permutation importance: each predictor is permuted in +turn, and the drop in OOB accuracy gives its score. This contrasts with the +`gg_varpro` family, which uses release-rule importance from the `varPro` +engine. Variable importance is not always stored on the fitted object. If a `randomForest` fit is missing its importance scores, `gg_vimp()` will try to compute them for you. When even that is not possible (the forest was grown with `importance = FALSE` and the predictors are no longer reachable), diff --git a/vignettes/varpro.qmd b/vignettes/varpro.qmd index ba328375..e3fc0533 100644 --- a/vignettes/varpro.qmd +++ b/vignettes/varpro.qmd @@ -49,27 +49,68 @@ if (requireNamespace("ggRandomForests", quietly = TRUE)) { Random-forest variable importance has, for two decades, mostly meant *permutation importance*: shuffle a column, measure the drop in OOB -accuracy, repeat. It works, but the score depends on artificial data — the -permuted column — and the answer is unstable when variables are correlated. - -varPro [@Lu2024varpro] replaces the shuffling with *release rules*. From a -guided-splitting forest, varPro samples a collection of decision-rule -branches; for each variable in each rule, it compares the response inside -the rule's region with the response after the constraint on that variable -is "released". The size of that change, aggregated over rules and trees, -is the variable's importance. No synthetic columns, no permutation — the -contrast is between two real subsets of the data. - -That core machinery feeds several views. **`gg_varpro()`** summarises the -per-tree importance distribution. **`gg_beta_varpro()`** refines those -release-rule contrasts with a per-rule lasso. **`gg_partial_varpro()`** +accuracy, repeat. It works, but the score is computed against +artificially modified data that never appeared in the training set, and +when predictors are correlated the shuffled column is an implausible +counterfactual, not a neutral baseline. Knockoff-style methods clean up +some of that, but they introduce their own synthetic features and rely on +explicit distributional assumptions about the predictors. + +varPro [@Lu2024varpro] takes a different path: one grounded entirely in +*observed* data. Think of each decision tree as a long chain of "if/then" +clauses. varPro harvests those clauses as *rules*, and for each rule it +identifies a specific region of the predictor space where a handful of +variables jointly constrain the response. To measure one variable's +contribution, it compares a *local estimator* (the response summary +restricted to that rule's region) against a "released" estimator where +the constraint on the tested variable has been lifted. That comparison, +averaged over rules and trees, is the variable's importance z-score. Every +observation used in the contrast is real training data; nothing is +permuted, nothing is synthesised. + +The "release" metaphor is the right one. Imagine holding a rope at several +points along its length (the rule constraints). Releasing your grip at one +point (while keeping the others in place) tells you how taut that +section was. Variables that go slack when released were carrying load; +variables that stay taut were redundant given the rest. + +Operationally, varPro's *guided splitting* steers tree growth so that +rules concentrate in informative regions, and the subsequent *rule +harvesting* step selects the rules that survived the importance +pre-filter. Variables below a noise threshold are dropped before +reporting (the importance z is pre-filtered), so the final ranking +contains only variables that earned a seat at the table +(see the [varPro tools reference](https://www.varprotools.org/articles/getstarted.html) +for worked implementation details). Split-weights encode each variable's +relative importance at each tree node, propagating that signal through the +release-rule contrasts and into the partial-dependence curves. + +Cross-validation via `cv.varpro()` returns three views of the +importance ranking: `$imp` (the default, point-estimate ranking), +`$imp.conserve` (one-SD rule: only variables whose lower confidence +bound is positive), and `$imp.liberal` (any variable with a positive +point estimate). The conservative cut is the right default when you want +to hand a short list to a collaborator; the liberal cut is better for +exploratory screening when you'd rather miss nothing. + +For classification, the importance decomposes into an `$unconditional` +score (averaged over all classes) and per-class `$conditional.z` scores. +When a variable separates *one* class from the others but is uninformative +overall, the unconditional z can be near zero while a single conditional z +is large; the conditional view catches that. One-hot consolidation via +`get.orgvimp()` and `get.topvars()` maps the per-level scores back to the +original factor, so downstream reporting stays on familiar ground. + +That core machinery feeds six ggRandomForests wrappers. **`gg_varpro()`** +summarises the per-tree importance distribution. **`gg_beta_varpro()`** +refines those release-rule contrasts with a per-rule lasso. **`gg_partial_varpro()`** turns the release machinery into partial-dependence curves. **`gg_udependent()`** reads cross-variable dependency off a `uvarpro()` fit. **`gg_isopro()`** scores observations for anomaly using an isolation-forest variant. **`gg_ivarpro()`** computes per-observation local importance. -This vignette walks all six wrappers on three worked examples — a +This vignette walks all six wrappers on three worked examples: a regression problem (Boston housing), a classification problem (iris, binary and multi-class), and a survival problem (PBC). The closing section is a one-page reference matrix mapping each wrapper to the @@ -87,14 +128,18 @@ v_boston <- varPro::varpro(medv ~ ., data = Boston, ntree = 50) v_boston ``` -`varpro()` builds the importance machinery — release rules, per-tree -scores, the splitweight vector — once. Every subsequent figure in this -section reads off that single fit. +`varpro()` builds the importance machinery (release rules, per-tree +scores, the splitweight vector) once. Every subsequent figure in this +section reads off that single fit. The formula interface is intentional: +varPro is model-independent in the sense that it doesn't assume a linear +or parametric relationship between predictors and response, but it does +require you to name a response so the guided splitting has something to +optimise toward. ## Per-tree importance with `gg_varpro()` The first question varPro answers is the same one permutation importance -answers — which variables matter, ranked. `gg_varpro()` extracts the +answers: which variables matter, ranked. `gg_varpro()` extracts the per-tree importance scores and draws their distribution as a horizontal boxplot, one row per variable, sorted by median z-score. Variables above a configurable cutoff (default 0.79) are highlighted. @@ -104,9 +149,14 @@ plot(gg_varpro(v_boston)) ``` The narrow boxes near the top are the variables varPro is confident -about — every tree agrees they matter. Wide boxes that straddle the +about: every tree agrees they matter. Wide boxes that straddle the cutoff line are the ones to look at twice; the forest disagrees with -itself. +itself. That disagreement isn't noise to suppress: it can mean the +variable matters in some rule regions but not others, which is exactly +the kind of structured heterogeneity that model-independent methods are +built to detect. Variables that fall entirely below the cutoff were +pre-filtered by the importance z threshold; they're gone before the plot +is drawn. ## Partial dependence with `gg_partial_varpro()` @@ -122,8 +172,20 @@ plot(gg_pd) ``` Each panel is a single predictor. The three curves correspond to the -three estimators varPro carries — read them as a sensitivity analysis. -Tight agreement is reassuring; divergence is information. +three estimators varPro carries (parametric, non-parametric, and causal); +read them as a sensitivity analysis. When all three agree, you have a +stable signal; when the causal curve diverges from the others, that's a +hint that the variable's observed relationship with the response may be +partly driven by its correlation with other predictors in the rule +regions, not by a direct effect. + +Because `partialpro()` operates within the *local* neighborhoods defined +by the release rules, these curves reflect what happens in the part of +the predictor space the forest actually visited, not a global +extrapolation from a fitted model. That is both a strength (no +out-of-distribution extrapolation) and a limit (sparse regions of the +predictor space will have wider effective intervals, even if the plot +doesn't always show them explicitly). ## Per-rule lasso refinement with `gg_beta_varpro()` @@ -131,13 +193,23 @@ Tight agreement is reassuring; divergence is information. `gg_beta_varpro()` re-asks the question variable by variable inside each rule: it fits a one-predictor lasso of the response on the released variable, restricted to the OOB observations in that rule's region. The -fitted β̂ is the variable's *local* effect inside that rule. Aggregating -`mean(|β̂|)` across rules gives one number per variable; that number is -a regression-coefficient-flavoured importance, not a VIMP score. +fitted coefficient (call it a "local β") captures the variable's +linear effect *within that rule's neighborhood*, not globally. Aggregating +`mean(|β|)` across rules gives one number per variable: a +regression-coefficient-flavoured importance, not a VIMP score, and not a +global slope. + +That distinction matters in practice. A variable with a strong nonlinear +global relationship may have locally small β values inside any single +rule (the local-standardisation step within each rule normalises the +scale), but many rules will fire on it, so the aggregated mean is still +large. Conversely, a variable with a nearly linear global effect will +concentrate most of its weight in a handful of rules, and the +between-rule variability in β will be low. Because `beta.varpro()` is expensive (a `glmnet` per rule), the wrapper -accepts a pre-computed `beta_fit` so you can iterate on selection -without re-fitting. +accepts a pre-computed `beta_fit` so you can iterate on selection, +cutoff, or class choice without re-fitting. ```{r boston-beta-fit, cache=TRUE} b_boston <- varPro::beta.varpro(v_boston) @@ -158,7 +230,7 @@ The three views so far take one variable at a time. `gg_udependent()` reads cross-variable structure off a `uvarpro()` fit and draws the result as a network: nodes are variables, edges are dependencies above a configurable threshold. The visual is built with `ggraph`, which is in -`Suggests` rather than `Imports` — install it if you want this view. +`Suggests` rather than `Imports`; install it if you want this view. ```{r boston-uvarpro, cache=TRUE} u_boston <- varPro::uvarpro(Boston[, setdiff(names(Boston), "medv")], @@ -170,13 +242,21 @@ plot(gg_udependent(u_boston)) ``` Clusters of mutually-connected variables are worth checking for -redundancy — they may be several views of the same underlying quantity. +redundancy: they may be several views of the same underlying quantity. +`uvarpro()` operates on the predictor matrix alone; there is no response +in the fit, which is what makes the network genuinely unsupervised. +Variables that cluster here are correlated in feature space regardless of +whether any of them matters for prediction. That information is +complementary to the ranking from `gg_varpro()`: a variable can be +important for prediction and still sit in a tight cluster with a +near-duplicate; in that situation, model parsimony may favour dropping +one of the cluster members without losing much predictive accuracy. ## Anomaly scoring with `gg_isopro()` Variable importance is one axis; *observation* outlierness is another. -`gg_isopro()` wraps `varPro::isopro()` — an isolation-forest variant -that scores how anomalous each training row looks — and renders the +`gg_isopro()` wraps `varPro::isopro()` (an isolation-forest variant +that scores how anomalous each training row looks) and renders the result as a ranked elbow plus a density of the scores. The score is on `[0, 1]`; the wrapper's convention is "higher = more anomalous" (opposite of varPro's native polarity; the wrapper flips it for @@ -194,8 +274,18 @@ plot(gg_isopro(iso_boston)) ``` The elbow flags rows that diverge from the bulk. Pair with the -domain — anomalous in feature space is not the same as wrong, but it's -often where the most interesting cases live. +domain: anomalous in feature space is not the same as wrong, but it's +often where the most interesting cases live. In a regression context, +a high-scoring row is one that the isolation forest isolated quickly +because its feature combination is rare; it may or may not be an outlier +in the response. Anomaly scoring and residual analysis answer different +questions, and it's worth doing both. + +Note that `isopro()` scores are not calibrated to a universal scale: a +score of 0.7 in one dataset is not comparable to 0.7 in another. What +matters is the relative ordering within a dataset and the shape of the +elbow: a sharp kink at a small number of observations is a cleaner +signal than a gradual slope that never levels off. ## Local importance with `gg_ivarpro()` @@ -223,16 +313,32 @@ plot(gg_ivarpro(v_boston, ivarpro_fit = iv_boston, which_obs = 1L)) The distribution view tells you which variables drive predictions *across* observations. The per-observation view answers the same -question for a specific case — useful for explaining one prediction +question for a specific case, useful for explaining one prediction back to whoever asked. +The per-observation importance is computed by replaying the release-rule +contrasts restricted to the rules that fire for that observation. It +is not a Shapley value, but it shares the local-attribution spirit: each +bar shows how much that variable's release shifted the local estimator +for this specific row, averaged over the rules that covered it. Cases +that are well-represented in the training data (dense rule coverage) will +have sharper attribution than cases in sparse regions of the predictor +space. + # Classification: iris -Iris is a small data set — 150 rows, four predictors, three response -classes — and that's a feature here, not a flaw: every figure renders in +Iris is a small data set (150 rows, four predictors, three response +classes), and that's a feature here, not a flaw: every figure renders in under a second, and the structure is well-understood enough that any -strange behaviour stands out. Two fits: a binary problem (drop *setosa*, -positive class = *virginica*) and the full three-class problem. +strange behaviour stands out. It is also a good stress-test for the +conditional importance path: petal length and petal width separate +*setosa* from everything else very cleanly, but the *versicolor*/*virginica* +boundary is much softer. A method that only reports unconditional +importance would lump both cases together; the conditional decomposition +should show the asymmetry. + +Two fits: a binary problem (drop *setosa*, positive class = *virginica*) +and the full three-class problem. ```{r iris-fit-binary} iris_binary <- iris[iris$Species != "setosa", ] @@ -250,9 +356,18 @@ v_iris_multi <- varPro::varpro(Species ~ ., data = iris, ntree = 50) For a classification forest, `gg_varpro()` can split the importance view into one facet per class. Variables keep the unconditional sort -order so rows line up across facets — read along a row to see which +order so rows line up across facets; read along a row to see which class a variable is informative for. +The conditional importance uses `$conditional.z` from the varPro fit: +per-class release-rule contrasts computed within the same rule regions +as the unconditional score, but with the local estimator replaced by a +per-class probability. A variable that separates one class from the rest +will have a high `$conditional.z` for that class and a near-zero or +even negative value for the others. The unconditional score in +`$unconditional` is the average, and can mask class-specific effects, +which is why the `conditional = TRUE` faceted view exists. + ```{r iris-multi-gg-varpro-conditional} plot(gg_varpro(v_iris_multi, conditional = TRUE)) ``` @@ -277,7 +392,7 @@ others) act as a sanity check on the forest. On a classification fit, `gg_beta_varpro()` returns one row per (variable, class) pair. For a binary fit, `which_class = NULL` defaults -to the last factor level — the positive class — so the headline view +to the last factor level (the positive class), so the headline view is a single panel of that class.[^mortality] [^mortality]: The same code pattern applies to clinical binary outcomes @@ -293,7 +408,7 @@ plot(gg_beta_varpro(v_iris_binary, beta_fit = b_iris_binary)) ``` For a multi-class fit, the default view is faceted by class with each -class sharing the row order set by the unified ranking — same trick as +class sharing the row order set by the unified ranking, same trick as `gg_varpro(conditional = TRUE)`. ```{r iris-multi-beta-fit, cache=TRUE} @@ -309,11 +424,28 @@ when you want it. # Survival: PBC -Survival is the family where the varPro toolchain shows its limits. The -forest-fitting and the family-agnostic wrappers all work; the -lasso-refined and individual-importance wrappers don't, because the -underlying `varPro::beta.varpro()` and `varPro::ivarpro()` calls don't -support survival fits in the current release. +Survival is the family where the varPro toolchain shows its limits, +and being explicit about those limits is more useful than pretending they +don't exist. The forest-fitting and the family-agnostic wrappers all +work; the lasso-refined and individual-importance wrappers don't, because +the underlying `varPro::beta.varpro()` and `varPro::ivarpro()` calls +don't yet extend to right-censored outcomes. A per-rule local lasso for a +censored response requires a local partial-likelihood or Nelson-Aalen +estimator in place of the regression/classification local estimator; +the design work for that is tracked but not yet landed. + +What does work is the core release-rule importance, the C-path partial +dependence (routed through the embedded `$rf` random survival forest), +and anomaly scoring on the predictor matrix. For many applied problems, +those three views cover the questions you actually want to answer. + +The PBC (primary biliary cirrhosis) dataset from `randomForestSRC` has +418 patients, seven predictors, and a Surv-encoded outcome of days to +event (death or transplant, status ∈ {0, 1, 2}). We use a small +seven-variable subset so the vignette fits quickly. For a full +analysis including time-dependent covariates, @Lee:2021 demonstrates the +boosted nonparametric hazard framework that varPro's survival path draws +on. ```{r pbc-data} library(survival) @@ -338,10 +470,14 @@ plot(gg_varpro(v_pbc)) For survival, `gg_partial_varpro()` dispatches into the `randomForestSRC`-backed partial-dependence machinery via the underlying -`$rf` survival forest — what the package calls the *C-path*. The output -shape is the same as the regression case (parametric / non-parametric / -causal panels); the y-axis carries an `ensemble mortality` interpretation -(Ishwaran 2008), not a survival probability. +`$rf` survival forest (what the package calls the *C-path* because it +routes through the `randomForestSRC` C-code layer rather than the varPro +release-rule engine). The output shape is the same as the regression case +(parametric / non-parametric / causal panels); the y-axis carries an +"ensemble mortality" interpretation [@Ishwaran:2007a], not a survival +probability. Higher values mean higher predicted mortality at a given +predictor value, integrated over the event-time horizon in the training +data. ```{r pbc-gg-partial-varpro} plot(gg_partial_varpro(object = v_pbc)) @@ -368,7 +504,7 @@ story. Both are tracked for v3.1.0. If you call either on a survival fit you'll get a clear error message pointing at the deferred work, not a silent miscalculation. The -family-support matrix in §6 records this — and the rest of the toolkit +family-support matrix in §6 records this; the rest of the toolkit that *does* work on survival (`gg_varpro`, `gg_partial_varpro`, `gg_isopro` above; `gg_udependent` shown in §3 on the X-matrix). @@ -406,7 +542,7 @@ This convention will be propagated to `gg_vimp` and calls. Both wrappers accept a pre-computed fit (`beta_fit`, `ivarpro_fit`) so you can iterate on selection, observation index, or cutoff without re-fitting the lasso or the local-importance machinery. -The vignette uses this throughout — every section computes the heavy +The vignette uses this throughout: every section computes the heavy fit once in a `cache: true` chunk and re-uses it for every figure. Provenance carries `precomputed = TRUE` when the cached path was used, @@ -426,11 +562,20 @@ This contract was established in v2.7.3.9012 (PR #98). # Further reading -The release-rule framework is laid out in @Lu2024varpro. The ensemble -mortality scale used by survival partial dependence is introduced in -@Ishwaran:2007a (the Annals of Applied Statistics methods paper for -random survival forests); the R-package side is described in -@Ishwaran:2008. Each wrapper's help page carries a "What this is -doing" section that goes one level deeper than this vignette. +The release-rule framework is laid out in @Lu2024varpro. Worked +implementation examples and the full API are at the +[varPro tools reference site](https://www.varprotools.org/articles/getstarted.html). + +The ensemble mortality scale used by survival partial dependence is +introduced in @Ishwaran:2007a (the Annals of Applied Statistics methods +paper for random survival forests); the R-package side is described in +@Ishwaran:2008. The boosted nonparametric hazard framework that informs +varPro's survival path (including the treatment of time-dependent +covariates) is in @Lee:2021. + +Each wrapper's help page carries a "What this is doing" section that +goes one level deeper than this vignette. The cross-cutting reference +in §6 maps each wrapper to the forest families it supports and notes +which capabilities are deferred to v3.1.0. # References From db71424563079bf5a27efe929cd5eec01d26ace4 Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Wed, 10 Jun 2026 11:01:20 -0400 Subject: [PATCH 02/10] fix(vignettes): static PD surfaces + 96-dpi figures to cut install size (#110) The regression and survival partial-dependence surfaces were interactive plotly widgets; self-contained quarto inlined plotly.js (~3.5 MB) into each vignette HTML, and figures rendered at retina 2x. Installed size was 17.1 MB (doc 16.3 MB), well over CRAN's 5 MB guideline. Replace both surfaces with static ggplot2 heat maps, set fig-format png / fig-dpi 96 in all four vignettes, and drop the now-unused plotly Suggests. Installed size drops to ~5.5 MB (doc 4.7 MB); source tarball 9.0 -> 3.7 MB. R CMD check --as-cran (with manual, ggraph present): pending confirmation. Co-authored-by: Claude Opus 4.8 --- DESCRIPTION | 1 - NEWS.md | 5 +++ vignettes/ggRandomForests-regression.qmd | 48 ++++++------------------ vignettes/ggRandomForests-survival.qmd | 45 +++++----------------- vignettes/ggRandomForests.qmd | 2 + vignettes/varpro.qmd | 2 + 6 files changed, 31 insertions(+), 72 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 0b19ec4c..8a25f12c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -44,7 +44,6 @@ Suggests: pkgdown, pkgload, knitr, - plotly, ggraph, callr VignetteBuilder: quarto diff --git a/NEWS.md b/NEWS.md index d982a256..ea002a0e 100644 --- a/NEWS.md +++ b/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 ====================== diff --git a/vignettes/ggRandomForests-regression.qmd b/vignettes/ggRandomForests-regression.qmd index 04d6808a..60639279 100644 --- a/vignettes/ggRandomForests-regression.qmd +++ b/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 diff --git a/vignettes/ggRandomForests-survival.qmd b/vignettes/ggRandomForests-survival.qmd index e0dc7a9b..a42f2bdd 100644 --- a/vignettes/ggRandomForests-survival.qmd +++ b/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. diff --git a/vignettes/ggRandomForests.qmd b/vignettes/ggRandomForests.qmd index aa113531..cfac0415 100644 --- a/vignettes/ggRandomForests.qmd +++ b/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: diff --git a/vignettes/varpro.qmd b/vignettes/varpro.qmd index e3fc0533..389dd194 100644 --- a/vignettes/varpro.qmd +++ b/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 From 18156a8ba77d3474576a7ec62c7b0d4d558d5f82 Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Wed, 10 Jun 2026 15:07:42 -0400 Subject: [PATCH 03/10] docs(examples): \donttest the slow plot.gg_variable example sections (#113) win-builder R-oldrelease flagged the plot.gg_variable example at 10.33s elapsed (just over CRAN's 10s; under 10s on release/devel). Wrap the loess-heavy regression panel plot and the full survival section (veteran forest + multi-time variable/panel plots) in \donttest so they are excluded from the timed example run; the fast classification + basic regression plots still run. No behaviour change. R CMD check --as-cran: examples [14s] OK, examples --run-donttest [28s] OK, Status: OK (0/0/0). Co-authored-by: Claude Opus 4.8 --- R/plot.gg_variable.R | 10 +++++++--- man/plot.gg_variable.Rd | 10 +++++++--- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/R/plot.gg_variable.R b/R/plot.gg_variable.R index f7245973..b9a272c7 100644 --- a/R/plot.gg_variable.R +++ b/R/plot.gg_variable.R @@ -84,16 +84,19 @@ #' plot(gg_dta, xvar = "Temp") #' plot(gg_dta, xvar = "Solar.R") #' -#' # Panel plot across continuous predictors -#' plot(gg_dta, xvar = c("Solar.R", "Wind", "Temp", "Day"), panel = TRUE) -#' #' # Factor variable uses notched boxplots #' plot(gg_dta, xvar = "Month", notch = TRUE) #' +#' \donttest{ +#' # Panel plot across continuous predictors (loess smooths; slower) +#' plot(gg_dta, xvar = c("Solar.R", "Wind", "Temp", "Day"), panel = TRUE) +#' } +#' #' ## ------------------------------------------------------------ #' ## survival examples #' ## ------------------------------------------------------------ #' ## -------- veteran data +#' \donttest{ #' data(veteran, package = "randomForestSRC") #' set.seed(42) #' rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., veteran, @@ -119,6 +122,7 @@ #' #' # Panel coplot across two predictors and three time points #' plot(gg_dta, xvar = c("age", "diagtime"), panel = TRUE) +#' } #' #' @export plot.gg_variable <- function(x, # nolint: cyclocomp_linter diff --git a/man/plot.gg_variable.Rd b/man/plot.gg_variable.Rd index ae08a072..ee8b8333 100644 --- a/man/plot.gg_variable.Rd +++ b/man/plot.gg_variable.Rd @@ -84,16 +84,19 @@ plot(gg_dta, xvar = "Wind") plot(gg_dta, xvar = "Temp") plot(gg_dta, xvar = "Solar.R") -# Panel plot across continuous predictors -plot(gg_dta, xvar = c("Solar.R", "Wind", "Temp", "Day"), panel = TRUE) - # Factor variable uses notched boxplots plot(gg_dta, xvar = "Month", notch = TRUE) +\donttest{ +# Panel plot across continuous predictors (loess smooths; slower) +plot(gg_dta, xvar = c("Solar.R", "Wind", "Temp", "Day"), panel = TRUE) +} + ## ------------------------------------------------------------ ## survival examples ## ------------------------------------------------------------ ## -------- veteran data +\donttest{ data(veteran, package = "randomForestSRC") set.seed(42) rfsrc_veteran <- randomForestSRC::rfsrc(Surv(time, status) ~ ., veteran, @@ -119,6 +122,7 @@ plot(gg_dta, xvar = "age") # Panel coplot across two predictors and three time points plot(gg_dta, xvar = c("age", "diagtime"), panel = TRUE) +} } \references{ From a7d805290e69ae517d04846bf13fae6a01062fce Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Thu, 11 Jun 2026 09:54:35 -0400 Subject: [PATCH 04/10] Cut CRAN overall check time below 10 min (#114) * perf(check): cut CRAN overall check time below 10 min CRAN flagged the 3.1.0 submission's overall check time (13 min > 10 min), driven by the vignette rebuild (331s) and tests (209s). Reduce both per Uwe Ligges' suggested levers (toy data / fewer iterations / precomputed results), with no change to test coverage or vignette content. Vignettes: - regression: Boston forest ntree 200, PD-surface grid 25 -> 10 - survival: impute ntree 100, forest ntree 150, PD-surface grid 25 -> 8 - varpro: the three gg_partial_varpro() calls (11-17s each) and the Boston beta.varpro() fit (~3s) -- the bulk of that vignette -- are precomputed offline by precompute_varpro.R and loaded from varpro_precomputed.rds (167 KB, xz), with an automatic live-computation fallback if absent. Tests: - test_gg_udependent memoised varPro::get.beta.entropy() (~1.5s, a pure function of the fit) per argument signature instead of recomputing it once per test (this file was ~24s of the suite, now ~9s). Verified: R CMD check --as-cran with manual is OK (0/0/0); local vignette rebuild 33s and tests 28s (were 331s/209s on CRAN's r-devel-windows). Co-Authored-By: Claude Opus 4.8 * review: address Copilot feedback on the check-time PR - varpro.qmd: load the precompute via tryCatch(readRDS) so a missing OR unreadable .rds falls back to live computation instead of erroring. - precompute_varpro.R: mirror the vignette's requireNamespace/pkgload fallback instead of bare library(ggRandomForests), so the script runs in a fresh clone before the package is installed. - test_gg_udependent.R: make make_ggu() warning suppression opt-in (.quiet = FALSE by default); pass .quiet = TRUE only to the empty-graph (threshold = 999) cases that legitimately warn, so an unexpected warning on any other call still fails the test. Verified: test_gg_udependent 19 tests, 0 fail / 0 warn; varpro vignette renders in 20s with 0 errors (precompute still loaded). Co-Authored-By: Claude Opus 4.8 * review(#114): search both paths for varpro_precomputed.rds The precomputed-load chunk read only the cwd-relative 'varpro_precomputed.rds'; depending on how Quarto sets the working directory during R CMD check this could miss the file and silently fall back to (slower) live computation. Search both the vignette-dir and package-root locations before the live fallback. Addresses Copilot review. Co-Authored-By: Claude Opus 4.8 --------- Co-authored-by: Claude Opus 4.8 --- NEWS.md | 12 ++++ cran-comments.md | 45 ++++++++++----- tests/testthat/test_gg_udependent.R | 70 ++++++++++++----------- vignettes/ggRandomForests-regression.qmd | 6 +- vignettes/ggRandomForests-survival.qmd | 7 ++- vignettes/precompute_varpro.R | 64 +++++++++++++++++++++ vignettes/varpro.qmd | 49 ++++++++++++++-- vignettes/varpro_precomputed.rds | Bin 0 -> 171228 bytes 8 files changed, 197 insertions(+), 56 deletions(-) create mode 100644 vignettes/precompute_varpro.R create mode 100644 vignettes/varpro_precomputed.rds diff --git a/NEWS.md b/NEWS.md index ea002a0e..fea94c14 100644 --- a/NEWS.md +++ b/NEWS.md @@ -22,6 +22,18 @@ ggRandomForests v3.1.0 `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`. +* Check time: reduced the `R CMD check` vignette-rebuild and test timings to + bring the overall CRAN check comfortably under budget (CRAN flagged the + overall check time on the 3.1.0 submission). The regression and survival + vignettes use lighter forests (`ntree` 200 / 150, imputation `ntree` 100) + and coarser partial-dependence grids. The varpro vignette's three + `gg_partial_varpro()` calls and the Boston `beta.varpro()` fit (~34 s + combined) are precomputed offline by `vignettes/precompute_varpro.R` and + loaded from `vignettes/varpro_precomputed.rds`, with an automatic + live-computation fallback if the file is absent. The `gg_udependent()` + tests memoise the per-fit entropy matrix (`varPro::get.beta.entropy()`, + ~1.5 s and a pure function of the fit) instead of recomputing it once per + test. No user-facing behaviour change. ggRandomForests v3.0.0 ====================== diff --git a/cran-comments.md b/cran-comments.md index 72d1f721..561003d6 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,19 +1,36 @@ ## v3.1.0 — varPro integration + bug fix (major release) -This is a major release. v2.7.3 is the version currently published on -CRAN. A v3.0.0 submission (2026-05-28) cleared the incoming pretests on -Windows and Debian (0/0/0) but was held by the automated service and did -not complete the review cycle; no changes were ever requested. This -v3.1.0 release supersedes it: the submission moves CRAN from v2.7.3 to -v3.1.0 and carries the full v3.0.0 feature layer plus the v3.1.0 bug fix -and documentation work. The version is in 3.x territory because it adds a -substantial new feature layer and soft-deprecates one user-facing -function. - -The automated hold on the v3.0.0 submission appeared to be a heuristic -flag (the major version jump from 2.7.3 plus the Depends-to-Imports move), -not a package defect: both incoming pretests were clean. We note it here -in case the same heuristic flags v3.1.0. +### Resubmission + +This resubmission addresses the one issue raised on the previous 3.1.0 +submission: the overall check time exceeded 10 minutes on +r-devel-windows-x86_64, driven by the vignette rebuild (~331 s) and the +tests (~209 s). We have reduced both, per the three levers suggested +(small toy data, fewer iterations, precomputed results for the lengthiest +parts): + +- The regression and survival vignettes use lighter forests (`ntree` + 200 / 150, imputation `ntree` 100) and coarser partial-dependence grids. +- The varpro vignette's three `gg_partial_varpro()` calls and the Boston + `beta.varpro()` fit — together ~34 s, the bulk of that vignette's + rebuild — are now precomputed offline (`vignettes/precompute_varpro.R`) + and loaded from a 167 KB `vignettes/varpro_precomputed.rds`, with an + automatic live-computation fallback if the file is absent. +- The `gg_udependent()` tests previously recomputed the same per-fit + entropy matrix (~1.5 s) once per test; they now memoise it. + +Locally the full vignette rebuild drops from ~80 s to ~38 s and the test +suite from ~44 s to ~36 s (R 4.6.0, aarch64-apple-darwin23), with no +change in test coverage or vignette content. + +### Release context + +v2.7.3 is the version currently published on CRAN. A v3.0.0 submission +(2026-05-28) cleared the Windows and Debian pretests (0/0/0) but did not +complete the review cycle; this 3.1.0 release supersedes it, carrying the +full v3.0.0 feature layer plus the v3.1.0 bug fix and documentation work. +The version is in 3.x territory because it adds a substantial new feature +layer and soft-deprecates one user-facing function. ### Changes in v3.1.0 diff --git a/tests/testthat/test_gg_udependent.R b/tests/testthat/test_gg_udependent.R index 3ad41567..39a2f4c2 100644 --- a/tests/testthat/test_gg_udependent.R +++ b/tests/testthat/test_gg_udependent.R @@ -7,6 +7,28 @@ make_uvp <- function(ntree = 25L) { varPro::uvarpro(iris[, -5L], ntree = ntree) } +# gg_udependent() recomputes varPro::get.beta.entropy() on every call (~1.5s, +# the only slow step, and a pure function of the fit). The tests below exercise +# the same fit under several argument combinations, so memoise the result per +# argument signature: identical coverage, but one entropy computation per +# distinct call instead of one per test (this file was ~24s of the suite). +.ggu_cache <- new.env(parent = emptyenv()) + +# .quiet = TRUE suppresses warnings only for callers that legitimately warn +# (the empty-graph threshold cases); every other call leaves warnings live so +# an unexpected new warning still surfaces as a test failure. +make_ggu <- function(..., .quiet = FALSE) { + key <- paste(deparse(list(...)), collapse = "") + if (is.null(.ggu_cache[[key]])) { + .ggu_cache[[key]] <- if (.quiet) { + suppressWarnings(gg_udependent(make_uvp(), ...)) + } else { + gg_udependent(make_uvp(), ...) + } + } + .ggu_cache[[key]] +} + ## ── Input validation ───────────────────────────────────────────────────────── test_that("gg_udependent: missing object -> stop", { @@ -26,20 +48,17 @@ test_that("gg_udependent: non-positive threshold -> stop", { ## ── Class & structure ──────────────────────────────────────────────────────── test_that("gg_udependent returns gg_udependent class", { - uv <- make_uvp() - expect_s3_class(gg_udependent(uv), "gg_udependent") + expect_s3_class(make_ggu(), "gg_udependent") }) test_that("gg_udependent$edges has required columns", { - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() expect_true(all(c("variable_from", "variable_to", "weight") %in% names(gg$edges))) expect_type(gg$edges$weight, "double") }) test_that("gg_udependent$nodes has required columns", { - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() expect_true(all(c("variable", "degree", "selected") %in% names(gg$nodes))) expect_s3_class(gg$nodes$variable, "factor") expect_type(gg$nodes$degree, "integer") @@ -48,37 +67,32 @@ test_that("gg_udependent$nodes has required columns", { test_that("gg_udependent$graph is an igraph", { skip_if_not_installed("igraph") - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() expect_true(igraph::is_igraph(gg$graph)) }) test_that("gg_udependent directed=TRUE returns directed igraph", { skip_if_not_installed("igraph") - uv <- make_uvp() - gg <- gg_udependent(uv, directed = TRUE) + gg <- make_ggu(directed = TRUE) expect_true(igraph::is_directed(gg$graph)) }) test_that("gg_udependent directed=FALSE returns undirected igraph", { skip_if_not_installed("igraph") - uv <- make_uvp() - gg <- gg_udependent(uv, directed = FALSE) + gg <- make_ggu(directed = FALSE) expect_false(igraph::is_directed(gg$graph)) }) test_that("gg_udependent$edges is empty data frame (not NULL) for empty graph", { - uv <- make_uvp() # threshold=999 -> no edges -> empty graph - gg <- suppressWarnings(gg_udependent(uv, threshold = 999)) + gg <- make_ggu(threshold = 999, .quiet = TRUE) expect_false(is.null(gg$edges)) expect_s3_class(gg$edges, "data.frame") expect_equal(nrow(gg$edges), 0L) }) test_that("gg_udependent$nodes is empty data frame for empty graph", { - uv <- make_uvp() - gg <- suppressWarnings(gg_udependent(uv, threshold = 999)) + gg <- make_ggu(threshold = 999, .quiet = TRUE) expect_false(is.null(gg$nodes)) expect_equal(nrow(gg$nodes), 0L) }) @@ -86,8 +100,7 @@ test_that("gg_udependent$nodes is empty data frame for empty graph", { ## ── Provenance ─────────────────────────────────────────────────────────────── test_that("gg_udependent provenance has all expected fields", { - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() prov <- attr(gg, "provenance") expect_type(prov, "list") expect_true(all(c("threshold", "q.signal", "directed", "min.degree", @@ -95,32 +108,28 @@ test_that("gg_udependent provenance has all expected fields", { }) test_that("gg_udependent provenance threshold matches argument", { - uv <- make_uvp() - gg <- gg_udependent(uv, threshold = 0.5) + gg <- make_ggu(threshold = 0.5) expect_equal(attr(gg, "provenance")$threshold, 0.5) }) ## ── S3 companions ──────────────────────────────────────────────────────────── test_that("print.gg_udependent returns object invisibly", { - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() out <- capture.output(ret <- print(gg)) expect_identical(ret, gg) expect_true(any(grepl("gg_udependent", out))) }) test_that("summary.gg_udependent returns summary.gg_udependent class", { - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() s <- summary(gg) expect_s3_class(s, "summary.gg_udependent") }) test_that("autoplot.gg_udependent returns a ggplot", { skip_if_not_installed("ggraph") - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() expect_s3_class(ggplot2::autoplot(gg), "ggplot") }) @@ -128,23 +137,20 @@ test_that("autoplot.gg_udependent returns a ggplot", { test_that("plot.gg_udependent default returns a ggplot", { skip_if_not_installed("ggraph") - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() p <- plot(gg) expect_s3_class(p, "ggplot") }) test_that("plot.gg_udependent layout='kk' returns a ggplot", { skip_if_not_installed("ggraph") - uv <- make_uvp() - gg <- gg_udependent(uv) + gg <- make_ggu() p <- plot(gg, layout = "kk") expect_s3_class(p, "ggplot") }) test_that("plot.gg_udependent empty graph -> stop with informative message", { - uv <- make_uvp() - gg <- suppressWarnings(gg_udependent(uv, threshold = 999)) + gg <- make_ggu(threshold = 999, .quiet = TRUE) expect_error(plot(gg), regexp = "no edges") }) diff --git a/vignettes/ggRandomForests-regression.qmd b/vignettes/ggRandomForests-regression.qmd index 60639279..6b8215c3 100644 --- a/vignettes/ggRandomForests-regression.qmd +++ b/vignettes/ggRandomForests-regression.qmd @@ -142,7 +142,7 @@ detects the regression family from the continuous response. ```{r rfsrc-fit} rfsrc_Boston <- rfsrc(medv ~ ., data = Boston, # nolint: object_name_linter - importance = TRUE, err.block = 5) + ntree = 200, importance = TRUE, err.block = 5) rfsrc_Boston ``` @@ -351,11 +351,11 @@ nearly flat in high-`lstat` tracts, confirming a meaningful interaction. # 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 +compute partial dependence on a grid: 10 values of `rm`, each evaluated at 25 points along `lstat`. ```{r surface-data, cache=TRUE} -rm_grid <- quantile_pts(rfsrc_Boston$xvar$rm, groups = 25) +rm_grid <- quantile_pts(rfsrc_Boston$xvar$rm, groups = 10) surface_list <- lapply(rm_grid, function(rm_val) { newx <- rfsrc_Boston$xvar diff --git a/vignettes/ggRandomForests-survival.qmd b/vignettes/ggRandomForests-survival.qmd index a42f2bdd..446ebfc0 100644 --- a/vignettes/ggRandomForests-survival.qmd +++ b/vignettes/ggRandomForests-survival.qmd @@ -277,12 +277,13 @@ state, which is required for `partial.rfsrc()` to work correctly. # Step 1: impute missing values via random forest proximity pbc_imputed <- impute.rfsrc(Surv(years, status) ~ ., data = pbc_trial, - ntree = 500, + ntree = 100, nimpute = 2) # Step 2: grow the survival forest on the complete imputed data rfsrc_pbc <- rfsrc(Surv(years, status) ~ ., data = pbc_imputed, + ntree = 150, nsplit = 10, tree.err = TRUE, importance = TRUE) @@ -545,12 +546,12 @@ interaction between these two liver function markers. # Partial Dependence Surfaces For a richer view of the interaction between bilirubin and albumin, we construct -a partial dependence surface. We compute partial dependence on a grid of 25 +a partial dependence surface. We compute partial dependence on a grid of 8 albumin values, each evaluated at 25 bilirubin points. ```{r surface-data, error=TRUE, cache=FALSE} # Create grid of albumin values -alb_grid <- quantile_pts(pbc_trial$albumin, groups = 25) +alb_grid <- quantile_pts(pbc_trial$albumin, groups = 8) # For each albumin value, compute partial dependence on bili at ~1 year surface_list <- lapply(alb_grid, function(alb_val) { diff --git a/vignettes/precompute_varpro.R b/vignettes/precompute_varpro.R new file mode 100644 index 00000000..e7b9dfbd --- /dev/null +++ b/vignettes/precompute_varpro.R @@ -0,0 +1,64 @@ +# Precompute the expensive varPro objects used by the varpro vignette. +# +# The three gg_partial_varpro() calls (~31s) and the Boston beta.varpro() +# fit (~3s) dominate the vignette rebuild time. Computing them once here and +# loading the result in varpro.qmd keeps the R CMD check vignette rebuild +# fast (CRAN flagged the overall check time). Every other varPro call in the +# vignette is sub-second and stays live. +# +# Run from the package root after changing any of the varpro vignette fits: +# Rscript vignettes/precompute_varpro.R +# +# Produces vignettes/varpro_precomputed.rds. The vignette falls back to live +# computation if that file is absent, so the result is reproducible either way. + +suppressMessages({ + library(varPro) + library(survival) +}) +# Mirror the vignette's loader: prefer the installed package, fall back to +# pkgload::load_all() so this runs in a fresh clone before installation. +if (requireNamespace("ggRandomForests", quietly = TRUE)) { + suppressMessages(library(ggRandomForests)) +} else if (requireNamespace("pkgload", quietly = TRUE)) { + pkgload::load_all(export_all = FALSE, helpers = FALSE, + attach_testthat = FALSE) +} else { + stop("Install ggRandomForests (or pkgload for dev builds) to run this script.") +} +options(mc.cores = 1, rf.cores = 1) + +# --- Regression: Boston housing ------------------------------------------ +data("Boston", package = "MASS") +set.seed(20260527L) +v_boston <- varPro::varpro(medv ~ ., data = Boston, ntree = 50) +pd_boston <- gg_partial_varpro(object = v_boston) +b_boston <- varPro::beta.varpro(v_boston) + +# --- Classification: iris (multi-class) ---------------------------------- +set.seed(20260527L) +v_iris_multi <- varPro::varpro(Species ~ ., data = iris, ntree = 50) +pd_iris_multi <- gg_partial_varpro(object = v_iris_multi) + +# --- Survival: PBC (C-path partial dependence) --------------------------- +data(pbc, package = "randomForestSRC") +pbc_small <- na.omit(pbc[, c("days", "status", "age", "albumin", "bili", + "edema", "platelet")]) +set.seed(20260527L) +v_pbc <- varPro::varpro(Surv(days, status) ~ ., data = pbc_small, ntree = 50) +pd_pbc <- gg_partial_varpro(object = v_pbc) + +saveRDS( + list( + pd_boston = pd_boston, + b_boston = b_boston, + pd_iris_multi = pd_iris_multi, + pd_pbc = pd_pbc + ), + file = "vignettes/varpro_precomputed.rds", + version = 2, + compress = "xz" +) + +cat("Wrote vignettes/varpro_precomputed.rds (", + round(file.size("vignettes/varpro_precomputed.rds") / 1024, 1), "KB )\n") diff --git a/vignettes/varpro.qmd b/vignettes/varpro.qmd index 389dd194..b63f4d48 100644 --- a/vignettes/varpro.qmd +++ b/vignettes/varpro.qmd @@ -47,6 +47,25 @@ if (requireNamespace("ggRandomForests", quietly = TRUE)) { } ``` +```{r precomputed, include=FALSE, cache=FALSE} +# The three gg_partial_varpro() calls and the Boston beta.varpro() fit are +# the only expensive parts of this vignette (~34s combined). They are +# precomputed by precompute_varpro.R and loaded here so the R CMD check +# vignette rebuild stays fast. If the file is absent (e.g. a fresh clone +# that hasn't run the script), each chunk falls back to live computation, +# so the vignette is reproducible either way. +.vp <- tryCatch({ + # R CMD check rebuilds the vignette in a copy whose working directory is the + # vignette dir; an interactive knit from the package root sees vignettes/ + # instead. Try both locations, and fall back to live computation below if + # neither is present or readRDS() fails. + .cand <- c("varpro_precomputed.rds", + file.path("vignettes", "varpro_precomputed.rds")) + .hit <- .cand[file.exists(.cand)] + if (length(.hit)) readRDS(.hit[[1]]) else list() +}, error = function(e) list()) # missing or unreadable -> live fallback below +``` + # What varPro is Random-forest variable importance has, for two decades, mostly meant @@ -169,7 +188,12 @@ holding the others fixed? `gg_partial_varpro()` wraps non-parametric, and causal partial-dependence curves. ```{r boston-gg-partial-varpro} -gg_pd <- gg_partial_varpro(object = v_boston) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +gg_pd <- if (is.null(.vp$pd_boston)) { + gg_partial_varpro(object = v_boston) +} else { + .vp$pd_boston +} plot(gg_pd) ``` @@ -214,7 +238,12 @@ accepts a pre-computed `beta_fit` so you can iterate on selection, cutoff, or class choice without re-fitting. ```{r boston-beta-fit, cache=TRUE} -b_boston <- varPro::beta.varpro(v_boston) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +b_boston <- if (is.null(.vp$b_boston)) { + varPro::beta.varpro(v_boston) +} else { + .vp$b_boston +} ``` ```{r boston-gg-beta-varpro} @@ -382,7 +411,13 @@ panels are organised by predictor with class encoded as colour or facet (the wrapper defaults handle this). ```{r iris-multi-gg-partial-varpro} -plot(gg_partial_varpro(object = v_iris_multi)) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +gg_pd_iris <- if (is.null(.vp$pd_iris_multi)) { + gg_partial_varpro(object = v_iris_multi) +} else { + .vp$pd_iris_multi +} +plot(gg_pd_iris) ``` Read each panel as: "as predictor X changes from low to high, how does @@ -482,7 +517,13 @@ predictor value, integrated over the event-time horizon in the training data. ```{r pbc-gg-partial-varpro} -plot(gg_partial_varpro(object = v_pbc)) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +gg_pd_pbc <- if (is.null(.vp$pd_pbc)) { + gg_partial_varpro(object = v_pbc) +} else { + .vp$pd_pbc +} +plot(gg_pd_pbc) ``` ## Anomaly scoring: `gg_isopro()` on the X-matrix diff --git a/vignettes/varpro_precomputed.rds b/vignettes/varpro_precomputed.rds new file mode 100644 index 0000000000000000000000000000000000000000..d9694604ca771e6ae2a49547832748ef4e6e15e9 GIT binary patch literal 171228 zcmV(hK={A?H+ooF0004LBHlIv03iVu0001VFXf}vZsd>Q$l_#IA|Elu{Q zp%wrkLP^?9?=rqW3Na;NNhYkuMeMsm@~-Fgw8 zFAB8C@^H6GL{-eo;^B%UlA&7=uwYJD8M>CWh|m47x$8EJg9zmxm+Edv{e=kpkw`1> zUNxBwh9prjfVamW3Dy+ht=!$-Tc^T%eI<80*VXpUiKs!#0;FuTH2f;P-G&^83{8Uk zHq&@J$z{%+(aqED4}dz0<b&v$aTP@Zy1@OiQIUG#g`Co#QCrKL^!-d~W ztsNARxTBa{1TKT%u>4q}WDRjziRPE%mFIv_5L=E;4Mj3$e1{A;%|z6W1rzF=?S0GN zHYVq(;0kysiPfaDru50Y^Z!ROhk!9j1kY^aRS$+>OW-F=4WmMgX z^RO;eyj_Rc4*W2x!AEglYmdmLVo1fof|`Y6-*|b0nle)tK1#5DAWPS#93PGUIokUD zIqOWw-Z z2#c<$W2hle7`+Z)v@S?*jbx*yi3peKM*3<|rxI)Hr7;S1MoC^miry|f6MezUP&dEe 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zSg|ENaQ5-=U$7IRlCuT_1pn4Tk!vqIUO;K-H&D;uoy54I+NLx?EKCt%(Dq60fP&7P zHGR1zGdx&wx&HJeKkk&jQPVWy34zIR!XR z4Tv$rx)pDvC+FbV{*veW0JH@6ZPw>={$7R%BB#EvL(5px zQC=L6+ip=}+Az7TS*4GbFjJ#||5nMo%D}j<#Tjq8R`@07@qTzx;@PY;*twA#OJpoe zAq_%**S7lix@^$iOK=IX_n#i&xeA5$mH_|&0A|u&x0~ZpKmz~(00045T94`% A0{{R3 literal 0 HcmV?d00001 From 08021431a46a3b6cbaaf7e32f69a370aafd549c9 Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Thu, 11 Jun 2026 13:15:36 -0400 Subject: [PATCH 05/10] chore: open 3.1.0.9000 dev cycle after the CRAN release (#115) v3.1.0 accepted to CRAN (2026-06-11). Bump main to the post-release .9000 dev version (DESCRIPTION + NEWS, dual update so the news-version test sees the DESCRIPTION version), and record the release submission in CRAN-SUBMISSION (SHA a7d80529). Co-authored-by: Claude Opus 4.8 --- CRAN-SUBMISSION | 6 +++--- DESCRIPTION | 4 ++-- NEWS.md | 6 +++++- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION index 11bf8e24..6d2dd745 100644 --- a/CRAN-SUBMISSION +++ b/CRAN-SUBMISSION @@ -1,3 +1,3 @@ -Version: 2.7.3 -Date: 2026-05-12 13:23:24 UTC -SHA: dd8e66f248a91e943c1c6dd1ffc2356058ac652b +Version: 3.1.0 +Date: 2026-06-11 15:26:24 UTC +SHA: a7d805290e69ae517d04846bf13fae6a01062fce diff --git a/DESCRIPTION b/DESCRIPTION index 8a25f12c..4db36866 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: ggRandomForests Type: Package Title: Visually Exploring Random Forests -Version: 3.1.0 -Date: 2026-06-10 +Version: 3.1.0.9000 +Date: 2026-06-11 Authors@R: person("John", "Ehrlinger", role = c("aut", "cre"), email = "john.ehrlinger@gmail.com") diff --git a/NEWS.md b/NEWS.md index fea94c14..987c98ff 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,9 @@ Package: ggRandomForests -Version: 3.1.0 +Version: 3.1.0.9000 + +ggRandomForests v3.1.0.9000 (development) +========================================= +* Development version opened after the v3.1.0 CRAN release. ggRandomForests v3.1.0 ====================== From 168bd7c9217eb969f2616e08b2ec562f13088145 Mon Sep 17 00:00:00 2001 From: John Ehrlinger Date: Fri, 12 Jun 2026 10:49:47 -0400 Subject: [PATCH 06/10] =?UTF-8?q?fix(cran):=20v3.1.1=20=E2=80=94=20clear?= =?UTF-8?q?=20gcc-UBSAN=20additional=20issue=20(varPro=20test=20skip=20+?= =?UTF-8?q?=20vignette=20precompute)=20(#119)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix(cran): skip_on_cran the varPro tests to avoid upstream UBSAN (v3.1.1) CRAN's gcc-UBSAN additional check flagged 3.1.0 with a 0-length array access in randomForestSRC's compiled rfsrcGrow (entry.c:184), reached when varPro::varpro()/beta.varpro() grow a forest in our tests. ggRandomForests is pure R (NeedsCompilation: no) — the overflow is in a dependency, surfaced by our tests. Gate every varPro forest-growing test fixture/builder with testthat::skip_on_cran() so CRAN's machines (incl. gcc-UBSAN) never run them. No package code changed; the tests still run on CI and locally. Bump to 3.1.1; NEWS + cran-comments explain the fix. Upstream reported. Co-Authored-By: Claude Opus 4.8 * docs(cran-comments): note the benign 'days since last update' NOTE Co-Authored-By: Claude Opus 4.8 * fix(cran): precompute all varPro vignette fits to avoid upstream UBSAN The varpro vignette grew ~10 varPro forests live (varpro/uvarpro/isopro/ ivarpro/beta.varpro), each reaching randomForestSRC's rfsrcGrow rule-grow path that trips the gcc-UBSAN "0-length array" report (entry.c:184, a length-0 yvar.wt decremented to an out-of-bounds pointer). Cache every fit in vignettes/varpro_precomputed.rds and load it with a live fallback, so R CMD check performs no live varPro grow. Strip the unused embedded forests ($rf, $isoforest, redundant ivarpro attrs) before saving — validated that every gg_* wrapper call returns output identical to the un-stripped object — keeping the file at 414 KB (tarball 4.13 MB, under CRAN's 5 MB limit). R CMD check --as-cran (with manual): 0 errors, 0 warnings, 1 NOTE (days-since-update, expected). Overall check time 2:43. dev/randomForestSRC-ubsan-report.md records the upstream root cause + suggested patch (maintainers are aware and staging a fix). Co-Authored-By: Claude Opus 4.8 * style(vignette): satisfy lintr (brace/semicolon) in fallback chunks Lint CI flagged 16 brace_linter/semicolon_linter issues in the vignette hardening: - varpro.qmd: the load-with-fallback chunks braced the `if` branch but not `else` (`} else .vp$x`); brace both branches to match the existing precomputed chunks. - precompute_varpro.R: rewrite the one-line .strip_* helpers (compound semicolons + inline braces) as multi-line. Behaviour-preserving; lint_package() now returns 0. The shipped varpro_precomputed.rds is unchanged. Co-Authored-By: Claude Opus 4.8 * docs: address Copilot review — accurate CI claims, scoped strip comments Copilot flagged that skip_on_cran() also skips under R CMD check in CI (NOT_CRAN unset in the workflows), so the varPro tests run nowhere in CI: - NEWS.md / cran-comments.md no longer claim the tests "run on CI"; they state the tests run locally (devtools::test()) and are skipped under R CMD check, including the CI check jobs. (Restoring CI coverage via NOT_CRAN=true is deferred to 3.1.2, coupled with the issue #118 fix so the intermittent survival gg_varpro test doesn't flake CI.) - precompute_varpro.R: scope the "forests unused" comments — the survival C-path gg_partial_varpro() and gg_isopro(newdata=) DO use $rf/$isoforest, but the vignette never invokes those on a stripped object (pd_pbc cached, gg_isopro training-path only). No behaviour change; lint_package() = 0. Co-Authored-By: Claude Opus 4.8 * ci: set NOT_CRAN=true so varPro tests run in CI (Copilot review) Copilot noted skip_on_cran() also skips the varPro tests under R CMD check in CI (NOT_CRAN unset), so they ran nowhere. Set NOT_CRAN=true in all check/coverage workflows to restore that coverage. Safe: CI is not a sanitizer build, so the upstream randomForestSRC UBSAN path is harmless here; only CRAN's own check machines (NOT_CRAN unset) skip them, which is what avoids the gcc-UBSAN additional issue. The CI-run varPro tests cover regression + classification only (no survival), so issue #118 cannot flake CI. Verified locally under NOT_CRAN=true: 0 failures across the varPro suite. NEWS.md / cran-comments.md restore the accurate "runs in CI and locally; skipped only on CRAN" claim. Co-Authored-By: Claude Opus 4.8 --------- Co-authored-by: Claude Opus 4.8 --- .github/workflows/R-CMD-check.yaml | 4 + .github/workflows/check-release.yaml | 4 + .github/workflows/check-standard.yaml | 4 + .github/workflows/test-coverage.yaml | 4 + DESCRIPTION | 4 +- NEWS.md | 24 ++++- cran-comments.md | 114 +++++++++--------------- dev/randomForestSRC-ubsan-report.md | 90 +++++++++++++++++++ tests/testthat/helper-varpro-fixtures.R | 12 +++ tests/testthat/test_gg_udependent.R | 1 + tests/testthat/test_gg_varpro.R | 2 + vignettes/precompute_varpro.R | 95 +++++++++++++++++--- vignettes/varpro.qmd | 81 +++++++++++++---- vignettes/varpro_precomputed.rds | Bin 171228 -> 423844 bytes 14 files changed, 327 insertions(+), 112 deletions(-) create mode 100644 dev/randomForestSRC-ubsan-report.md diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index b9dde14c..5cae051e 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -30,6 +30,10 @@ jobs: env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + # Run skip_on_cran() tests in CI (incl. the varPro grow tests): CI is + # not a sanitizer build, so the upstream UBSAN path is harmless here; + # only CRAN's own check machines (NOT_CRAN unset) skip them. + NOT_CRAN: "true" R_KEEP_PKG_SOURCE: yes # Skip vdiffr visual-regression tests in CI until reference SVGs are # committed. To regenerate: run testthat::snapshot_accept() locally, diff --git a/.github/workflows/check-release.yaml b/.github/workflows/check-release.yaml index b2217c66..c7944d19 100644 --- a/.github/workflows/check-release.yaml +++ b/.github/workflows/check-release.yaml @@ -13,6 +13,10 @@ jobs: runs-on: ubuntu-latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + # Run skip_on_cran() tests in CI (incl. the varPro grow tests): CI is + # not a sanitizer build, so the upstream UBSAN path is harmless here; + # only CRAN's own check machines (NOT_CRAN unset) skip them. + NOT_CRAN: "true" R_KEEP_PKG_SOURCE: yes VDIFFR_RUN_TESTS: "false" steps: diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml index d6ea388d..a44f74c9 100644 --- a/.github/workflows/check-standard.yaml +++ b/.github/workflows/check-standard.yaml @@ -29,6 +29,10 @@ jobs: env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + # Run skip_on_cran() tests in CI (incl. the varPro grow tests): CI is + # not a sanitizer build, so the upstream UBSAN path is harmless here; + # only CRAN's own check machines (NOT_CRAN unset) skip them. + NOT_CRAN: "true" R_KEEP_PKG_SOURCE: yes VDIFFR_RUN_TESTS: "false" diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index 592acc2e..d8507ba7 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -14,6 +14,10 @@ jobs: runs-on: ubuntu-latest env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + # Run skip_on_cran() tests in CI (incl. the varPro grow tests): CI is + # not a sanitizer build, so the upstream UBSAN path is harmless here; + # only CRAN's own check machines (NOT_CRAN unset) skip them. + NOT_CRAN: "true" VDIFFR_RUN_TESTS: "false" steps: diff --git a/DESCRIPTION b/DESCRIPTION index 4db36866..65152ae1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: ggRandomForests Type: Package Title: Visually Exploring Random Forests -Version: 3.1.0.9000 -Date: 2026-06-11 +Version: 3.1.1 +Date: 2026-06-12 Authors@R: person("John", "Ehrlinger", role = c("aut", "cre"), email = "john.ehrlinger@gmail.com") diff --git a/NEWS.md b/NEWS.md index 987c98ff..0c2366cb 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,9 +1,25 @@ Package: ggRandomForests -Version: 3.1.0.9000 +Version: 3.1.1 -ggRandomForests v3.1.0.9000 (development) -========================================= -* Development version opened after the v3.1.0 CRAN release. +ggRandomForests v3.1.1 +====================== +* CRAN fix: the varPro tests now call `skip_on_cran()` so they do not run + on CRAN's check machines, including the gcc-UBSAN additional check. They + were triggering an upstream `randomForestSRC` sanitizer issue (a 0-length + array access in `rfsrcGrow`, `entry.c:184`) that surfaces when any + `varPro` grow (`varpro()`, `beta.varpro()`, `uvarpro()`, `isopro()`, + `ivarpro()`) builds a forest. ggRandomForests is pure R and its code is + unchanged; the varPro tests still run in our CI (the workflows set + `NOT_CRAN=true`) and locally; they are skipped only on CRAN's check + machines, including the gcc-UBSAN check. The upstream issue has been + reported to the randomForestSRC maintainers. +* The `varpro` vignette now loads every varPro fit from a precomputed + file (`vignettes/varpro_precomputed.rds`, built by + `vignettes/precompute_varpro.R`), so the vignette performs no live + varPro grow during `R CMD check`. This removes the same upstream + sanitizer path from the vignette build and trims check time. Each chunk + falls back to a live fit if the precomputed object is absent, so the + vignette remains reproducible from source. ggRandomForests v3.1.0 ====================== diff --git a/cran-comments.md b/cran-comments.md index 561003d6..5a415c50 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,88 +1,56 @@ -## v3.1.0 — varPro integration + bug fix (major release) +## v3.1.1 — CRAN fix (gcc-UBSAN additional check) -### Resubmission +This patch addresses the gcc-UBSAN "additional issue" flagged for 3.1.0 +(email from Prof Ripley, 2026-06-12; correct before 2026-06-29). -This resubmission addresses the one issue raised on the previous 3.1.0 -submission: the overall check time exceeded 10 minutes on -r-devel-windows-x86_64, driven by the vignette rebuild (~331 s) and the -tests (~209 s). We have reduced both, per the three levers suggested -(small toy data, fewer iterations, precomputed results for the lengthiest -parts): +### The issue -- The regression and survival vignettes use lighter forests (`ntree` - 200 / 150, imputation `ntree` 100) and coarser partial-dependence grids. -- The varpro vignette's three `gg_partial_varpro()` calls and the Boston - `beta.varpro()` fit — together ~34 s, the bulk of that vignette's - rebuild — are now precomputed offline (`vignettes/precompute_varpro.R`) - and loaded from a 167 KB `vignettes/varpro_precomputed.rds`, with an - automatic live-computation fallback if the file is absent. -- The `gg_udependent()` tests previously recomputed the same per-fit - entropy matrix (~1.5 s) once per test; they now memoise it. +The sanitizer reports, once, during the package tests: -Locally the full vignette rebuild drops from ~80 s to ~38 s and the test -suite from ~44 s to ~36 s (R 4.6.0, aarch64-apple-darwin23), with no -change in test coverage or vignette content. +``` +entry.c:184:55: runtime error: pointer index expression with base +0x000000000001 overflowed to 0xfffffffffffffff9 + #0 rfsrcGrow .../randomForestSRC/src/entry.c:184 +``` -### Release context +This is a 0-length array access in the compiled code of the +`randomForestSRC` package (`rfsrcGrow`, `entry.c:184`), reached when +`varPro::varpro()` / `varPro::beta.varpro()` grow a forest in our tests. +ggRandomForests is a pure-R package (`NeedsCompilation: no`); it contains +no C/C++/Fortran of its own, so the overflow is not in our code. It is +surfaced because our test suite exercises that code path. -v2.7.3 is the version currently published on CRAN. A v3.0.0 submission -(2026-05-28) cleared the Windows and Debian pretests (0/0/0) but did not -complete the review cycle; this 3.1.0 release supersedes it, carrying the -full v3.0.0 feature layer plus the v3.1.0 bug fix and documentation work. -The version is in 3.x territory because it adds a substantial new feature -layer and soft-deprecates one user-facing function. +### The fix -### Changes in v3.1.0 +Two changes, neither touching package R code (ggRandomForests remains +`NeedsCompilation: no`): -- **Bug fix.** `gg_vimp()` on single-outcome `rfsrc` forests now correctly - flags variables with non-positive VIMP in the `positive` column (used for - plot colouring). The single-outcome fit names the column `VIMP` - (uppercase) while the flag check read `$vimp` (lowercase), so `positive` - stayed `TRUE` for every variable. Added a regression test. -- **Documentation.** Deepened the varPro-family and rfsrc importance / - partial / survival help pages, made the `gg_vimp()` (permutation - importance) vs `gg_varpro()` (release-rule importance) distinction - explicit and cross-linked, and fixed a stale `@return` in `gg_roc()`. No - user-facing behaviour change beyond the bug fix above. +1. The tests that grow a varPro forest now call `testthat::skip_on_cran()`, + so they do not run on CRAN's check machines (including the gcc-UBSAN + check). They still run in our CI (the workflows set `NOT_CRAN=true`) + and locally for users who run `devtools::test()`; only CRAN's check + machines, where `NOT_CRAN` is unset, skip them. +2. The `varpro` vignette now loads every varPro fit from a precomputed + file (`vignettes/varpro_precomputed.rds`) instead of growing forests + live, so the vignette build performs no varPro grow under `R CMD check` + and cannot surface the same upstream path. Each chunk falls back to a + live fit if the file is absent, so the vignette is still reproducible + from source. -### Major changes carried from v3.0.0 +The upstream issue has been reported to the randomForestSRC maintainers. -- **New varPro wrapper family.** Tidy extractors and `plot()` methods for - the `varPro` package: `gg_partial_varpro()` (partial dependence), - `gg_varpro()` (variable importance), `gg_udependent()` (cross-variable - dependency graph), `gg_isopro()` (isolation-forest anomaly scores), - `gg_beta_varpro()` (per-rule beta importance), and `gg_ivarpro()` - (individual / local importance), each with `print` / `summary` / - `autoplot` companions and a dedicated "varpro" vignette. -- **Soft-deprecation.** `gg_partialpro()` now warns and forwards to - `gg_partial_varpro()`; it will be removed in a future release. -- **randomForest engine fixes.** `gg_variable.randomForest()` - classification, `gg_roc()` / `calc_roc()` for `randomForest` (true - probability-based, macro-averaged ROC), per-class one-vs-rest ROC - (`per_class = TRUE`), and `plot.gg_variable()` now returns a single - `ggplot` / `patchwork` object rather than a bare list. -- **Importance-plot ordering.** All importance plots now place the - most-important variable at the top, matching the `gg_vimp` convention. - -### Dependency change (reverse-dependency safe) - -`randomForestSRC` and `randomForest` move from `Depends:` to `Imports:`, -and `varPro` is added to `Imports:`. `library(ggRandomForests)` no longer -attaches the forest packages to the search path. There are **0 reverse -dependencies** on CRAN, so no downstream packages are affected. - -## Test environments +### Test environments * **Local:** R 4.6.0 on macOS (aarch64-apple-darwin23). - `R CMD check --as-cran` returns 0 errors, 0 warnings, 0 notes. + `R CMD check --as-cran` returns 0 errors, 0 warnings, 0 notes; the + varPro tests skip under the CRAN check environment as intended. * **GitHub Actions matrix:** ubuntu-latest (R-devel / R-release / - R-oldrel-1), windows-latest (R-release), macos-latest (R-release), - all green on the head commit. -* **Reverse-dependency check:** `tools::package_dependencies(reverse = - TRUE)` returns 0. -* **URLs:** `urlchecker::url_check()` clean. + R-oldrel-1), windows-latest (R-release), macos-latest (R-release). +* **Reverse-dependency check:** 0 reverse dependencies on CRAN. -## NOTE disposition +### NOTE disposition -No notes in local `R CMD check --as-cran`. Examples that fit survival -forests are wrapped in `\donttest` and run in a few seconds each. +One NOTE on the incoming feasibility check, "Days since last update: 1". +This is expected: 3.1.0 was published 2026-06-11 and this patch is the +gcc-UBSAN fix the CRAN team requested (2026-06-12 email, correct before +2026-06-29), so the short interval is unavoidable. No other notes. diff --git a/dev/randomForestSRC-ubsan-report.md b/dev/randomForestSRC-ubsan-report.md new file mode 100644 index 00000000..c489f44b --- /dev/null +++ b/dev/randomForestSRC-ubsan-report.md @@ -0,0 +1,90 @@ +# UBSAN: 0-length-array pointer arithmetic in `rfsrcGrow` (entry.c:184) + +**Package:** randomForestSRC (observed in 3.6.2, latent in earlier versions +incl. 3.3.5) +**Reporter:** John Ehrlinger (ggRandomForests maintainer) +**Severity:** Undefined behaviour flagged by gcc-UBSAN; numerically benign in +practice, but CRAN treats it as an "Additional issue." + +## What CRAN reports + +gcc-UBSAN, surfaced via the ggRandomForests test suite (which grows varPro +forests): + +``` +entry.c:184:55: runtime error: pointer index expression with base +0x000000000001 overflowed to 0xfffffffffffffff9 + #0 rfsrcGrow .../randomForestSRC/src/entry.c:184 +``` + +## Root cause + +`rfsrcGrow()` builds 1-offset working pointers from incoming R weight vectors: + +```c +// src/entry.c (3.6.2), lines 183-185 +RF_xWeightStat = REAL(xWeightStat); RF_xWeightStat--; // 183 +RF_yWeight = REAL(yWeight); RF_yWeight--; // 184 <-- UBSAN +RF_xWeight = REAL(xWeight); RF_xWeight--; // 185 +``` + +When the corresponding R vector has **length 0**, `REAL()` returns R's +empty-vector sentinel address `0x1`. The unconditional `ptr--` then forms +`0x1 - sizeof(double) = 0x1 - 8 = 0xfffffffffffffff9`. *Forming* this +out-of-bounds pointer is undefined behaviour (UBSAN, column 55 = the `--`), +even though the package never dereferences `RF_yWeight[i]` when the weight is +empty — so the result is numerically correct and the bug is otherwise silent. + +`yWeight` (R-side `yvar.wt`) is length 0 whenever the y-weight vector is +empty. The R glue produces exactly that for the **unsupervised** family: + +```r +# R/rfsrc.R +if (family == "unspv") { + yvar.wt <- NULL # -> as.double(NULL) -> numeric(0) -> length-0 yWeight +} else { + yvar.wt <- get.weight(yvar.wt, length(yvar.types)) +} +``` + +So every unsupervised forest already forms this pointer; varPro reaches it +because its rule-grow machinery fits forests through that path. + +### Minimal reproduction (no varPro needed) + +```r +library(randomForestSRC) +d <- data.frame(y = rnorm(60), x1 = rnorm(60), x2 = rnorm(60)) +rfsrc(Unsupervised() ~ ., d, ntree = 10) # yvar.wt is numeric(0) -> entry.c:184 +``` + +Confirmed by tracing `randomForestSRC:::get.weight`: the unsupervised path +returns a length-0 `yvar.wt` (`n=0, outlen=0`); regression returns length 1. + +Note the sibling pointers `RF_subjWeight`, `RF_eventType`, etc. are guarded +with `if (VECTOR_ELT(...) != R_NilValue)`, but these three are not — and +`as.double(numeric(0))` is never `R_NilValue`, so nothing catches the empty +case. + +## Suggested fix + +Only decrement when the vector is non-empty (mirrors the existing +NULL-guarded idiom; the empty pointer is never indexed downstream): + +```c +RF_xWeightStat = REAL(xWeightStat); if (LENGTH(xWeightStat) > 0) RF_xWeightStat--; +RF_yWeight = REAL(yWeight); if (LENGTH(yWeight) > 0) RF_yWeight--; +RF_xWeight = REAL(xWeight); if (LENGTH(xWeight) > 0) RF_xWeight--; +``` + +(In this repo's amalgamated `src/randomForestSRC.c` the same three lines are +at ~36965-36967.) The identical guard should be applied anywhere else +`rfsrcGrow`/`rfsrcPredict` does `REAL(w); w--` / `INTEGER(w); w--` on a vector +that can arrive length 0. + +## Downstream impact + +ggRandomForests 3.1.1 works around this by not exercising the path under +`R CMD check` (varPro tests `skip_on_cran()`; the varpro vignette loads +precomputed fits). A guard in randomForestSRC would remove the UB at the +source for all callers. diff --git a/tests/testthat/helper-varpro-fixtures.R b/tests/testthat/helper-varpro-fixtures.R index 2f3a3c8f..87f251da 100644 --- a/tests/testthat/helper-varpro-fixtures.R +++ b/tests/testthat/helper-varpro-fixtures.R @@ -5,6 +5,7 @@ .varpro_cache <- new.env(parent = emptyenv()) .varpro_mtcars <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$v)) { if (!requireNamespace("varPro", quietly = TRUE)) { testthat::skip("varPro not installed") @@ -16,6 +17,7 @@ } .beta_fit_mtcars <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$b)) { v <- .varpro_mtcars() set.seed(20260526L) @@ -25,6 +27,7 @@ } .varpro_iris_binary <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$vb)) { if (!requireNamespace("varPro", quietly = TRUE)) testthat::skip("varPro not installed") set.seed(20260526L) @@ -36,6 +39,7 @@ } .beta_fit_iris_binary <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$bb)) { set.seed(20260526L) .varpro_cache$bb <- varPro::beta.varpro(.varpro_iris_binary()) @@ -44,6 +48,7 @@ } .varpro_iris_multiclass <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$vm)) { if (!requireNamespace("varPro", quietly = TRUE)) testthat::skip("varPro not installed") set.seed(20260526L) @@ -53,6 +58,7 @@ } .beta_fit_iris_multiclass <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$bm)) { set.seed(20260526L) .varpro_cache$bm <- varPro::beta.varpro(.varpro_iris_multiclass()) @@ -61,6 +67,7 @@ } .ivarpro_boston <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$iv_boston)) { if (!requireNamespace("varPro", quietly = TRUE)) testthat::skip("varPro not installed") if (!requireNamespace("MASS", quietly = TRUE)) testthat::skip("MASS not installed") @@ -73,6 +80,7 @@ } .varpro_boston <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$v_boston)) { invisible(.ivarpro_boston()) # populates v_boston as a side-effect } @@ -80,6 +88,7 @@ } .ivarpro_iris_binary <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$iv_iris_binary)) { if (!requireNamespace("varPro", quietly = TRUE)) testthat::skip("varPro not installed") set.seed(20260526L) @@ -93,11 +102,13 @@ } .varpro_iris_binary_for_ivarpro <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$v_iris_binary)) invisible(.ivarpro_iris_binary()) .varpro_cache$v_iris_binary } .ivarpro_iris_multiclass <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$iv_iris_multi)) { if (!requireNamespace("varPro", quietly = TRUE)) testthat::skip("varPro not installed") set.seed(20260526L) @@ -109,6 +120,7 @@ } .varpro_iris_multiclass_for_ivarpro <- function() { + testthat::skip_on_cran() if (is.null(.varpro_cache$v_iris_multi)) invisible(.ivarpro_iris_multiclass()) .varpro_cache$v_iris_multi } diff --git a/tests/testthat/test_gg_udependent.R b/tests/testthat/test_gg_udependent.R index 39a2f4c2..440ca6d6 100644 --- a/tests/testthat/test_gg_udependent.R +++ b/tests/testthat/test_gg_udependent.R @@ -3,6 +3,7 @@ ## ── Helpers ────────────────────────────────────────────────────────────────── make_uvp <- function(ntree = 25L) { + testthat::skip_on_cran() set.seed(42L) varPro::uvarpro(iris[, -5L], ntree = ntree) } diff --git a/tests/testthat/test_gg_varpro.R b/tests/testthat/test_gg_varpro.R index 68cba93a..ef99ab63 100644 --- a/tests/testthat/test_gg_varpro.R +++ b/tests/testthat/test_gg_varpro.R @@ -4,12 +4,14 @@ # Regression fit — fast, always available (mtcars is base R) make_vp_regr <- function(ntree = 25L) { + testthat::skip_on_cran() set.seed(42L) varPro::varpro(mpg ~ ., data = mtcars, ntree = ntree) } # Classification fit — iris, always available make_vp_class <- function(ntree = 25L) { + testthat::skip_on_cran() set.seed(42L) varPro::varpro(Species ~ ., data = iris, ntree = ntree) } diff --git a/vignettes/precompute_varpro.R b/vignettes/precompute_varpro.R index e7b9dfbd..47b83377 100644 --- a/vignettes/precompute_varpro.R +++ b/vignettes/precompute_varpro.R @@ -1,16 +1,23 @@ -# Precompute the expensive varPro objects used by the varpro vignette. +# Precompute the varPro objects used by the varpro vignette. # -# The three gg_partial_varpro() calls (~31s) and the Boston beta.varpro() -# fit (~3s) dominate the vignette rebuild time. Computing them once here and -# loading the result in varpro.qmd keeps the R CMD check vignette rebuild -# fast (CRAN flagged the overall check time). Every other varPro call in the -# vignette is sub-second and stays live. +# Two reasons this file exists: +# +# 1. Speed. The three gg_partial_varpro() calls (~31s) and the beta.varpro() +# fits dominate the vignette rebuild time (CRAN flagged the overall check +# time). +# 2. Safety. Every varPro grow (varpro(), uvarpro(), isopro(), ivarpro(), +# beta.varpro()) reaches randomForestSRC's compiled rule-grow path, which +# trips a gcc-UBSAN "0-length array" report (rfsrcGrow, entry.c:184). By +# loading these objects from disk, the vignette performs NO live varPro +# grow during R CMD check, so it cannot surface that upstream sanitizer +# issue even if a CRAN flavour builds the vignette under UBSAN. # # Run from the package root after changing any of the varpro vignette fits: # Rscript vignettes/precompute_varpro.R # # Produces vignettes/varpro_precomputed.rds. The vignette falls back to live -# computation if that file is absent, so the result is reproducible either way. +# computation for any object missing from that file, so the result is +# reproducible either way. suppressMessages({ library(varPro) @@ -30,30 +37,90 @@ options(mc.cores = 1, rf.cores = 1) # --- Regression: Boston housing ------------------------------------------ data("Boston", package = "MASS") +boston_x <- Boston[, setdiff(names(Boston), "medv")] +set.seed(20260527L) +v_boston <- varPro::varpro(medv ~ ., data = Boston, ntree = 50) +pd_boston <- gg_partial_varpro(object = v_boston) +b_boston <- varPro::beta.varpro(v_boston) +iv_boston <- varPro::ivarpro(v_boston) +set.seed(20260527L) +u_boston <- varPro::uvarpro(boston_x, ntree = 50) set.seed(20260527L) -v_boston <- varPro::varpro(medv ~ ., data = Boston, ntree = 50) -pd_boston <- gg_partial_varpro(object = v_boston) -b_boston <- varPro::beta.varpro(v_boston) +iso_boston <- varPro::isopro(data = boston_x, method = "rnd", + sampsize = 256, ntree = 50) -# --- Classification: iris (multi-class) ---------------------------------- +# --- Classification: iris ------------------------------------------------ +iris_binary <- iris[iris$Species != "setosa", ] +iris_binary$Species <- droplevels(iris_binary$Species) +set.seed(20260527L) +v_iris_binary <- varPro::varpro(Species ~ ., data = iris_binary, ntree = 50) +b_iris_binary <- varPro::beta.varpro(v_iris_binary) set.seed(20260527L) v_iris_multi <- varPro::varpro(Species ~ ., data = iris, ntree = 50) pd_iris_multi <- gg_partial_varpro(object = v_iris_multi) +b_iris_multi <- varPro::beta.varpro(v_iris_multi) # --- Survival: PBC (C-path partial dependence) --------------------------- data(pbc, package = "randomForestSRC") pbc_small <- na.omit(pbc[, c("days", "status", "age", "albumin", "bili", "edema", "platelet")]) set.seed(20260527L) -v_pbc <- varPro::varpro(Surv(days, status) ~ ., data = pbc_small, ntree = 50) -pd_pbc <- gg_partial_varpro(object = v_pbc) +v_pbc <- varPro::varpro(Surv(days, status) ~ ., data = pbc_small, ntree = 50) +pd_pbc <- gg_partial_varpro(object = v_pbc) +set.seed(20260527L) +iso_pbc <- varPro::isopro(data = pbc_small[, c("age", "albumin", "bili", + "platelet")], + method = "rnd", sampsize = 256, ntree = 50) + +# --- Trim to keep the shipped .rds (and the source tarball) small -------- +# The gg_* calls this vignette makes (on its cached path) read only +# importance/rule summaries, not the embedded forests. The survival C-path +# gg_partial_varpro() and gg_isopro(newdata=) DO use the forest, but the +# vignette never invokes those on a stripped object (pd_pbc is cached and +# gg_isopro() is training-path only). Dropping those heavy +# slots takes the file from ~1.6 MB to ~0.4 MB (validated: every vignette +# wrapper call returns output identical to the un-stripped object). Two +# exceptions keep their forest: v_boston is printed in the vignette +# (print.varpro reads $rf), and u_boston feeds gg_udependent(), which uses it. +.strip_varpro <- function(v) { # $rf: unused on the cached path + v$rf <- NULL + v +} +.strip_isopro <- function(o) { # training-path gg_isopro reads only $ntree + o$isoforest <- list(ntree = o$isoforest$ntree) + o +} +.strip_ivarpro <- function(iv) { # drop redundant model/path attrs + for (a in c("ivarpro.path", "model", "data")) attr(iv, a) <- NULL + iv +} +b_boston <- .strip_varpro(b_boston) +v_pbc <- .strip_varpro(v_pbc) +v_iris_binary <- .strip_varpro(v_iris_binary) +v_iris_multi <- .strip_varpro(v_iris_multi) +iso_boston <- .strip_isopro(iso_boston) +iso_pbc <- .strip_isopro(iso_pbc) +iv_boston <- .strip_ivarpro(iv_boston) saveRDS( list( + # Boston (regr) + v_boston = v_boston, pd_boston = pd_boston, b_boston = b_boston, + iv_boston = iv_boston, + u_boston = u_boston, + iso_boston = iso_boston, + # iris (class) + v_iris_binary = v_iris_binary, + b_iris_binary = b_iris_binary, + v_iris_multi = v_iris_multi, pd_iris_multi = pd_iris_multi, - pd_pbc = pd_pbc + b_iris_multi = b_iris_multi, + # PBC (surv) + v_pbc = v_pbc, + pd_pbc = pd_pbc, + iso_pbc = iso_pbc ), file = "vignettes/varpro_precomputed.rds", version = 2, diff --git a/vignettes/varpro.qmd b/vignettes/varpro.qmd index b63f4d48..0bd7bb44 100644 --- a/vignettes/varpro.qmd +++ b/vignettes/varpro.qmd @@ -145,7 +145,12 @@ predictors, median home value as the response. ```{r boston-fit} data("Boston", package = "MASS") set.seed(20260527L) -v_boston <- varPro::varpro(medv ~ ., data = Boston, ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +v_boston <- if (is.null(.vp$v_boston)) { + varPro::varpro(medv ~ ., data = Boston, ntree = 50) +} else { + .vp$v_boston +} v_boston ``` @@ -264,8 +269,12 @@ configurable threshold. The visual is built with `ggraph`, which is in `Suggests` rather than `Imports`; install it if you want this view. ```{r boston-uvarpro, cache=TRUE} -u_boston <- varPro::uvarpro(Boston[, setdiff(names(Boston), "medv")], - ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +u_boston <- if (is.null(.vp$u_boston)) { + varPro::uvarpro(Boston[, setdiff(names(Boston), "medv")], ntree = 50) +} else { + .vp$u_boston +} ``` ```{r boston-gg-udependent, eval = requireNamespace("ggraph", quietly = TRUE)} @@ -294,10 +303,13 @@ on `[0, 1]`; the wrapper's convention is "higher = more anomalous" consistency). ```{r boston-isopro, cache=TRUE} -iso_boston <- varPro::isopro(data = Boston[, setdiff(names(Boston), - "medv")], - method = "rnd", sampsize = 256, - ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +iso_boston <- if (is.null(.vp$iso_boston)) { + varPro::isopro(data = Boston[, setdiff(names(Boston), "medv")], + method = "rnd", sampsize = 256, ntree = 50) +} else { + .vp$iso_boston +} ``` ```{r boston-gg-isopro} @@ -331,7 +343,12 @@ local importances across variables. accepts a pre-computed `ivarpro_fit` for reuse across views. ```{r boston-ivarpro-fit, cache=TRUE} -iv_boston <- varPro::ivarpro(v_boston) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +iv_boston <- if (is.null(.vp$iv_boston)) { + varPro::ivarpro(v_boston) +} else { + .vp$iv_boston +} ``` ```{r boston-gg-ivarpro-distribution} @@ -375,12 +392,22 @@ and the full three-class problem. iris_binary <- iris[iris$Species != "setosa", ] iris_binary$Species <- droplevels(iris_binary$Species) set.seed(20260527L) -v_iris_binary <- varPro::varpro(Species ~ ., data = iris_binary, ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +v_iris_binary <- if (is.null(.vp$v_iris_binary)) { + varPro::varpro(Species ~ ., data = iris_binary, ntree = 50) +} else { + .vp$v_iris_binary +} ``` ```{r iris-fit-multi} set.seed(20260527L) -v_iris_multi <- varPro::varpro(Species ~ ., data = iris, ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +v_iris_multi <- if (is.null(.vp$v_iris_multi)) { + varPro::varpro(Species ~ ., data = iris, ntree = 50) +} else { + .vp$v_iris_multi +} ``` ## Class-conditional importance with `gg_varpro(conditional = TRUE)` @@ -437,7 +464,12 @@ such as 30-day mortality: drop the negative class, set the event class as the last factor level, and read the figure for the event panel. ```{r iris-binary-beta-fit, cache=TRUE} -b_iris_binary <- varPro::beta.varpro(v_iris_binary) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +b_iris_binary <- if (is.null(.vp$b_iris_binary)) { + varPro::beta.varpro(v_iris_binary) +} else { + .vp$b_iris_binary +} ``` ```{r iris-binary-gg-beta-varpro} @@ -449,7 +481,12 @@ class sharing the row order set by the unified ranking, same trick as `gg_varpro(conditional = TRUE)`. ```{r iris-multi-beta-fit, cache=TRUE} -b_iris_multi <- varPro::beta.varpro(v_iris_multi) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +b_iris_multi <- if (is.null(.vp$b_iris_multi)) { + varPro::beta.varpro(v_iris_multi) +} else { + .vp$b_iris_multi +} ``` ```{r iris-multi-gg-beta-varpro} @@ -491,10 +528,12 @@ pbc_small <- pbc[, c("days", "status", "age", "albumin", "bili", "edema", "platelet")] pbc_small <- na.omit(pbc_small) set.seed(20260527L) -v_pbc <- varPro::varpro( - Surv(days, status) ~ ., - data = pbc_small, ntree = 50 -) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +v_pbc <- if (is.null(.vp$v_pbc)) { + varPro::varpro(Surv(days, status) ~ ., data = pbc_small, ntree = 50) +} else { + .vp$v_pbc +} ``` ## Variable importance: `gg_varpro()` @@ -532,9 +571,13 @@ Because `isopro()` only sees the predictor matrix, it doesn't care about the family. The same call from section 3 works here. ```{r pbc-isopro, cache=TRUE} -iso_pbc <- varPro::isopro(data = pbc_small[, c("age", "albumin", "bili", - "platelet")], - method = "rnd", sampsize = 256, ntree = 50) +# Precomputed offline (see precompute_varpro.R); falls back to a live fit. +iso_pbc <- if (is.null(.vp$iso_pbc)) { + varPro::isopro(data = pbc_small[, c("age", "albumin", "bili", "platelet")], + method = "rnd", sampsize = 256, ntree = 50) +} else { + .vp$iso_pbc +} plot(gg_isopro(iso_pbc)) ``` diff --git a/vignettes/varpro_precomputed.rds b/vignettes/varpro_precomputed.rds index d9694604ca771e6ae2a49547832748ef4e6e15e9..82eea83b705dca728065ae523642abc2665960ff 100644 GIT binary patch literal 423844 zcmV(pK=8l)H+ooF0004LBHlIv03iVu0001VFXf}=ZHDjwT>vZsd>Q$l_#IA|Elu{Q zp%x8T;~`S(+K)lqU=G7V58DS_74TkkdF%V!w;){a=)2uku`N0*zy6U*kKQE(k2dKd zA5wtTTl(s+R7MFa9l=0RM;b#kLO**hAby){;}`PDpcNaC4Cf`^Vy430=DZvt@#f!C z!EOk@)ESzY3HC4TBPsQJfZZ(P;j@`purbmX)j;dgBlIsB26>3{d+N7fi@Ifqj;*`5 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