docs: address PR #95 Copilot review

- plot.gg_udependent: clarify that truly isolated nodes are dropped by
  gg_udependent() before plotting; reword 'Isolated' as 'low-degree'.
- gg_partial_varpro: fix varpro::partialpro -> varPro::partialpro (six
  instances) so \link{} renders correctly.
- plot.gg_varpro: clarify the cutoff line lives in z-units on the
  default axis and in raw-importance units when type='raw'; the
  numeric is the same, the scale is not.
- gg_varpro reference: complete the dangling 'arXiv 2409.' with the
  full arXiv:2409.09003 identifier and an https://arxiv.org link.

Author: ehrlinger

R/gg_partial_varpro.R

@@ -1,7 +1,7 @@
 ##=============================================================================
 #' Partial dependence data from a varPro model
 #'
-#' \code{varpro::partialpro} returns one list, with continuous and
+#' \code{varPro::partialpro} returns one list, with continuous and
 #' categorical predictors mixed together. This function splits that list into
 #' two tidy data frames, one for each kind, and resolves the y-axis label the
 #' plot method will use.
@@ -10,7 +10,7 @@
 #' A partial dependence curve answers the question, "if I hold a single
 #' variable at a grid of values and average out everything else, how does
 #' the model's prediction move?" That is the same question \code{rfsrc}
-#' partial dependence answers. What \code{varpro::partialpro} adds is two
+#' partial dependence answers. What \code{varPro::partialpro} adds is two
 #' wrinkles that are worth understanding before you read the curves.
 #'
 #' First, \code{partialpro} filters the partial grid through an isolation
@@ -65,14 +65,14 @@
 #' estimator, not a structural causal claim about the data-generating
 #' process.
 #'
-#' @param part_dta Partial plot data from \code{varpro::partialpro}.  Each
+#' @param part_dta Partial plot data from \code{varPro::partialpro}.  Each
 #'   element must contain \code{xvirtual}, \code{xorg}, \code{yhat.par},
 #'   \code{yhat.nonpar}, and \code{yhat.causal}.  Supply at least one of
 #'   \code{part_dta} or \code{object}.
 #' @param object A fitted \code{varpro} object, the forest the partial data
 #'   came from.  When supplied it provides the provenance metadata, and when
 #'   \code{part_dta} is \code{NULL} it is passed to
-#'   \code{varpro::partialpro(object)} for you.  Required when
+#'   \code{varPro::partialpro(object)} for you.  Required when
 #'   \code{scale \%in\% c("surv","chf")}.
 #' @param scale Character; sets the y-axis label and, for survival forests,
 #'   the output type.  One of \code{"auto"} (default), \code{"mortality"},

R/gg_varpro.R

@@ -69,7 +69,8 @@
 #'
 #' @references
 #' Lu, M. and Ishwaran, H. (2024). Model-independent variable selection
-#' via the rule-based variable priority. \emph{arXiv} 2409.
+#' via the rule-based variable priority framework. \emph{arXiv preprint}
+#' \href{https://arxiv.org/abs/2409.09003}{arXiv:2409.09003}.
 #'
 #' @param object A fitted \code{varpro} object (required).
 #' @param local.std Logical; default \code{TRUE}.  When \code{TRUE} the

R/plot.gg_udependent.R

@@ -18,8 +18,10 @@
 #' stronger dependencies; thin, faint edges sit near the threshold and
 #' are the ones that would disappear first if you raised it.
 #'
-#' Isolated, grey, low-degree nodes are the ones UVarPro thinks are
-#' not contributing much to the structure. A cluster of mutually
+#' 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
+#' see is the connected component.) A cluster of mutually
 #' connected variables is worth checking for redundancy — they may be
 #' several views of the same underlying quantity.
 #'

R/plot.gg_varpro.R

@@ -13,9 +13,13 @@
 #' 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
-#' whiskers. The dashed vertical line is the selection cutoff (default
-#' \code{z = 0.79}); boxes whose aggregate z sits above it are coloured
-#' blue and flagged \code{selected = TRUE}, the rest are grey. A
+#' 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
+#' axis (\code{local.std = FALSE}, \code{type = "raw"}) it is the same
+#' numeric value but in raw-importance units. Boxes whose aggregate
+#' value sits above the line are coloured blue and flagged
+#' \code{selected = TRUE}, the rest are grey. A
 #' selected variable with a tight, high box is a variable the forest
 #' agrees on across trees. A selected variable with a wide box that
 #' straddles the cutoff is one to look at twice before relying on it.