From 0229933bc1673791b5c0dd61ad4721d8d8125648 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Florian=20G=C3=A4rber?= <florian.gaerber@uni-hamburg.de>
Date: Mon, 25 Sep 2023 11:57:58 +0200
Subject: [PATCH] doc: Fix mixed use of Rd and Md doc blocks

---
 DESCRIPTION              |  1 -
 R/addLayer.R             |  2 ++
 R/addSurrogates.R        |  5 +++++
 R/getTreeranger.R        |  2 ++
 man/var.relations.Rd     |  4 ++--
 man/var.relations.mfi.Rd |  4 ++--
 man/var.select.md.Rd     |  6 +++---
 man/var.select.mir.Rd    |  6 +++---
 man/var.select.smd.Rd    | 10 +++++-----
 9 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 53a808b..5b2267a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -36,5 +36,4 @@ LinkingTo:
 Config/testthat/edition: 3
 Encoding: UTF-8
 LazyData: true
-Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
diff --git a/R/addLayer.R b/R/addLayer.R
index 03b7065..78c91c4 100644
--- a/R/addLayer.R
+++ b/R/addLayer.R
@@ -18,6 +18,7 @@
 #' * `layer`: Tree layer depth information, starting at 0 (root node) and incremented for each layer.
 #'
 #' @export
+#' @md
 addLayer <- function(trees, num.threads = 1) {
   if (!inherits(trees, "RangerTrees")) {
     stop("`trees` must be a `getTreeranger` `RangerTrees` object.")
@@ -42,6 +43,7 @@ addLayer <- function(trees, num.threads = 1) {
 #' @seealso [addLayer()]
 #'
 #' @keywords internal
+#' @md
 add_layer_to_tree <- function(tree) {
   layer <- rep(NA, nrow(tree))
   layer[1] <- 0
diff --git a/R/addSurrogates.R b/R/addSurrogates.R
index bea7a07..a95ef5e 100644
--- a/R/addSurrogates.R
+++ b/R/addSurrogates.R
@@ -21,6 +21,7 @@
 #' * `adj_i`: adjusted agreement of variable i
 #'
 #' @export
+#' @md
 addSurrogates <- function(RF, trees, s, Xdata, num.threads = parallel::detectCores()) {
   if (!inherits(RF, "ranger")) {
     stop("`RF` must be a ranger object.")
@@ -69,6 +70,7 @@ addSurrogates <- function(RF, trees, s, Xdata, num.threads = parallel::detectCor
 #' This is an internal function
 #'
 #' @keywords internal
+#' @md
 getSurrogate <- function(surr.par, k = 1, maxsurr) {
   # weights and trees are extracted
   tree <- surr.par$trees[[k]]
@@ -92,6 +94,7 @@ getSurrogate <- function(surr.par, k = 1, maxsurr) {
 #' @useDynLib RFSurrogates, .registration = TRUE
 #'
 #' @keywords internal
+#' @md
 SurrTree <- function(j, wt, Xdata, controls, column.names, tree, maxsurr, ncat) {
   node <- tree[j, ]
   # for non-terminal nodes get surrogates
@@ -145,6 +148,7 @@ SurrTree <- function(j, wt, Xdata, controls, column.names, tree, maxsurr, ncat)
 #' This is an internal function
 #'
 #' @keywords internal
+#' @md
 name.surr <- function(i, surrogate.names) {
   surrogate.names <- c(surrogate.names, paste0("surrogate_", i))
   return(surrogate.names)
@@ -155,6 +159,7 @@ name.surr <- function(i, surrogate.names) {
 #' This is an internal function
 #'
 #' @keywords internal
+#' @md
 name.adj <- function(i, adj.names) {
   adj.names <- c(adj.names, paste0("adj_", i))
   return(adj.names)
diff --git a/R/getTreeranger.R b/R/getTreeranger.R
index 5dc21dd..5f5dffd 100644
--- a/R/getTreeranger.R
+++ b/R/getTreeranger.R
@@ -19,6 +19,7 @@
 #' * `layer`: If `add_layer` is `TRUE`, see [addLayer()]
 #'
 #' @export
+#' @md
 getTreeranger <- function(RF, num.trees = RF$num.trees, add_layer = FALSE, num.threads = 1) {
   trees <- parallel::mclapply(1:num.trees, getsingletree,
     mc.cores = num.threads,
@@ -53,6 +54,7 @@ getTreeranger <- function(RF, num.trees = RF$num.trees, add_layer = FALSE, num.t
 #' * `status`: `0` for terminal (`splitpoint` is `NA`) and `1` for non-terminal.
 #'
 #' @keywords internal
+#' @md
 getsingletree <- function(RF, k = 1, add_layer = FALSE) {
   # here we use the treeInfo function of the ranger package to create extract the trees, in an earlier version this was done with a self implemented function
   tree.ranger <- ranger::treeInfo(RF, tree = k)
diff --git a/man/var.relations.Rd b/man/var.relations.Rd
index cae2c0b..9e700f5 100644
--- a/man/var.relations.Rd
+++ b/man/var.relations.Rd
@@ -36,7 +36,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{type}{mode of prediction ("regression", "classification" or "survival"). Default is regression.}
 
-\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \\% of no. of variables.}
+\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \% of no. of variables.}
 
 \item{mtry}{number of variables to possibly split at in each node. Default is no. of variables^(3/4) ("^3/4") as recommended by (Ishwaran 2011). Also possible is "sqrt" and "0.5" to use the square root or half of the no. of variables.}
 
@@ -48,7 +48,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{save.ranger}{set TRUE if ranger object should be saved. Default is that ranger object is not saved (FALSE).}
 
-\item{create.forest}{Default: TRUE if \code{forest} is NULL, FALSE otherwise. Whether to create or use an existing forest.}
+\item{create.forest}{Default: TRUE if `forest` is NULL, FALSE otherwise. Whether to create or use an existing forest.}
 
 \item{forest}{the random forest that should be analyzed}
 
diff --git a/man/var.relations.mfi.Rd b/man/var.relations.mfi.Rd
index 42dd9b5..0128835 100644
--- a/man/var.relations.mfi.Rd
+++ b/man/var.relations.mfi.Rd
@@ -37,7 +37,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{type}{mode of prediction ("regression", "classification" or "survival"). Default is regression.}
 
-\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \\% of no. of variables.}
+\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \% of no. of variables.}
 
 \item{mtry}{number of variables to possibly split at in each node. Default is no. of variables^(3/4) ("^3/4") as recommended by (Ishwaran 2011). Also possible is "sqrt" and "0.5" to use the square root or half of the no. of variables.}
 
@@ -49,7 +49,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{save.ranger}{set TRUE if ranger object should be saved. Default is that ranger object is not saved (FALSE).}
 
-\item{create.forest}{Default: TRUE if \code{forest} is NULL, FALSE otherwise. Whether to create or use an existing forest.}
+\item{create.forest}{Default: TRUE if `forest` is NULL, FALSE otherwise. Whether to create or use an existing forest.}
 
 \item{forest}{the random forest that should be analyzed}
 
diff --git a/man/var.select.md.Rd b/man/var.select.md.Rd
index 42ae1a1..15d6a9d 100644
--- a/man/var.select.md.Rd
+++ b/man/var.select.md.Rd
@@ -41,7 +41,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{save.ranger}{Set TRUE if ranger object should be saved. Default is that ranger object is not saved (FALSE).}
 
-\item{create.forest}{Default: TRUE if \code{forest} is NULL, FALSE otherwise. Whether to create or use an existing forest.}
+\item{create.forest}{Default: TRUE if `forest` is NULL, FALSE otherwise. Whether to create or use an existing forest.}
 
 \item{forest}{the random forest that should be analyzed.}
 
@@ -90,7 +90,7 @@ res$var
 }
 \references{
 \itemize{
-\item Ishwaran, H. et al. (2011) Random survival forests for high-dimensional data. Stat Anal Data Min, 4, 115–132. \url{https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.10103}
-\item Ishwaran, H. et al. (2010) High-Dimensional Variable Selection for Survival Data. J. Am. Stat. Assoc., 105, 205–217. \url{http://www.ccs.miami.edu/~hishwaran/papers/IKGML.JASA.2010.pdf}
+  \item Ishwaran, H. et al. (2011) Random survival forests for high-dimensional data. Stat Anal Data Min, 4, 115–132. \url{https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.10103}
+  \item Ishwaran, H. et al. (2010) High-Dimensional Variable Selection for Survival Data. J. Am. Stat. Assoc., 105, 205–217. \url{http://www.ccs.miami.edu/~hishwaran/papers/IKGML.JASA.2010.pdf}
 }
 }
diff --git a/man/var.select.mir.Rd b/man/var.select.mir.Rd
index 90cc19f..aa42325 100644
--- a/man/var.select.mir.Rd
+++ b/man/var.select.mir.Rd
@@ -40,7 +40,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{type}{mode of prediction ("regression", "classification" or "survival"). Default is regression.}
 
-\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \\% of no. of variables.}
+\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \% of no. of variables.}
 
 \item{mtry}{number of variables to possibly split at in each node. Default is no. of variables^(3/4) ("^3/4") as recommended by (Ishwaran 2011). Also possible is "sqrt" and "0.5" to use the square root or half of the no. of variables.}
 
@@ -112,7 +112,7 @@ res$var
 }
 \references{
 \itemize{
-\item Nembrini, S. et al. (2018) The revival of the Gini importance? Bioinformatics, 34, 3711–3718. \url{https://academic.oup.com/bioinformatics/article/34/21/3711/4994791}
-\item Seifert, S. et al. (2019) Surrogate minimal depth as an importance measure for variables in random forests. Bioinformatics, 35, 3663–3671. \url{https://academic.oup.com/bioinformatics/article/35/19/3663/5368013}
+  \item Nembrini, S. et al. (2018) The revival of the Gini importance? Bioinformatics, 34, 3711–3718. \url{https://academic.oup.com/bioinformatics/article/34/21/3711/4994791}
+  \item Seifert, S. et al. (2019) Surrogate minimal depth as an importance measure for variables in random forests. Bioinformatics, 35, 3663–3671. \url{https://academic.oup.com/bioinformatics/article/35/19/3663/5368013}
 }
 }
diff --git a/man/var.select.smd.Rd b/man/var.select.smd.Rd
index d596496..062095a 100644
--- a/man/var.select.smd.Rd
+++ b/man/var.select.smd.Rd
@@ -32,7 +32,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{type}{mode of prediction ("regression", "classification" or "survival"). Default is regression.}
 
-\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \\% of no. of variables.}
+\item{s}{predefined number of surrogate splits (it may happen that the actual number of surrogate splits differs in individual nodes). Default is 1 \% of no. of variables.}
 
 \item{mtry}{number of variables to possibly split at in each node. Default is no. of variables^(3/4) ("^3/4") as recommended by (Ishwaran 2011). Also possible is "sqrt" and "0.5" to use the square root or half of the no. of variables.}
 
@@ -44,7 +44,7 @@ classification mode is used). For survival forests this is the time variable.}
 
 \item{save.ranger}{set TRUE if ranger object should be saved. Default is that ranger object is not saved (FALSE).}
 
-\item{create.forest}{Default: TRUE if \code{forest} is NULL, FALSE otherwise. Whether to create or use an existing forest.}
+\item{create.forest}{Default: TRUE if `forest` is NULL, FALSE otherwise. Whether to create or use an existing forest.}
 
 \item{forest}{the random forest that should be analyzed}
 
@@ -96,8 +96,8 @@ res$var
 }
 \references{
 \itemize{
-\item Seifert, S. et al. (2019) Surrogate minimal depth as an importance measure for variables in random forests. Bioinformatics, 35, 3663–3671. \url{https://academic.oup.com/bioinformatics/article/35/19/3663/5368013}
-\item Ishwaran, H. et al. (2011) Random survival forests for high-dimensional data. Stat Anal Data Min, 4, 115–132. \url{https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.10103}
-\item Ishwaran, H. et al. (2010) High-Dimensional Variable Selection for Survival Data. J. Am. Stat. Assoc., 105, 205–217. \url{http://www.ccs.miami.edu/~hishwaran/papers/IKGML.JASA.2010.pdf}
+  \item Seifert, S. et al. (2019) Surrogate minimal depth as an importance measure for variables in random forests. Bioinformatics, 35, 3663–3671. \url{https://academic.oup.com/bioinformatics/article/35/19/3663/5368013}
+  \item Ishwaran, H. et al. (2011) Random survival forests for high-dimensional data. Stat Anal Data Min, 4, 115–132. \url{https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.10103}
+  \item Ishwaran, H. et al. (2010) High-Dimensional Variable Selection for Survival Data. J. Am. Stat. Assoc., 105, 205–217. \url{http://www.ccs.miami.edu/~hishwaran/papers/IKGML.JASA.2010.pdf}
 }
 }
-- 
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