speed test for R, and refinement of item list in doc

R-package/R/xgb.train.R

@@ -3,12 +3,16 @@
 #' The training function of xgboost
 #'
 #' @param params the list of parameters. Commonly used ones are:
-#'   objective: objective function, common ones are
+#' \itemize{
-#'       - reg:linear  linear regression
+#'   \item \code{objective} objective function, common ones are
-#'       - binary:logistic logistic regression for classification
+#'   \itemize{
-#'   eta: step size of each boosting step
+#'     \item \code{reg:linear} linear regression
-#'   max_depth: maximum depth of the tree
+#'     \item \code{binary:logistic} logistic regression for classification
-#'   nthread: number of thread used in training, if not set, all threads are used
+#'   }
+#'   \item \code{eta} step size of each boosting step
+#'   \item \code{max_depth} maximum depth of the tree
+#'   \item \code{nthread} number of thread used in training, if not set, all threads are used
+#' }
 #'
 #'   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for 
 #'   further details. See also demo/demo.R for walkthrough example in R.

R-package/R/xgboost.R

@@ -7,12 +7,16 @@
 #' @param label the response variable. User should not set this field,
 #    if data is local data file or  \code{xgb.DMatrix}. 
 #' @param params the list of parameters. Commonly used ones are:
-#'   objective: objective function, common ones are
+#' \itemize{
-#'       - reg:linear  linear regression
+#'   \item \code{objective} objective function, common ones are
-#'       - binary:logistic logistic regression for classification
+#'   \itemize{
-#'   eta: step size of each boosting step
+#'     \item \code{reg:linear} linear regression
-#'   max_depth: maximum depth of the tree
+#'     \item \code{binary:logistic} logistic regression for classification
-#'   nthread: number of thread used in training, if not set, all threads are used
+#'   }
+#'   \item \code{eta} step size of each boosting step
+#'   \item \code{max_depth} maximum depth of the tree
+#'   \item \code{nthread} number of thread used in training, if not set, all threads are used
+#' }
 #'
 #'   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for 
 #'   further details. See also demo/demo.R for walkthrough example in R.

R-package/man/xgb.train.Rd

@@ -8,12 +8,16 @@ xgb.train(params = list(), dtrain, nrounds, watchlist = list(),
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
-  objective: objective function, common ones are
+\itemize{
-      - reg:linear  linear regression
+  \item \code{objective} objective function, common ones are
-      - binary:logistic logistic regression for classification
+  \itemize{
-  eta: step size of each boosting step
+    \item \code{reg:linear} linear regression
-  max_depth: maximum depth of the tree
+    \item \code{binary:logistic} logistic regression for classification
-  nthread: number of thread used in training, if not set, all threads are used
+  }
+  \item \code{eta} step size of each boosting step
+  \item \code{max_depth} maximum depth of the tree
+  \item \code{nthread} number of thread used in training, if not set, all threads are used
+}
 
   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
   further details. See also demo/demo.R for walkthrough example in R.}

R-package/man/xgboost.Rd

@@ -13,12 +13,16 @@ xgboost(data = NULL, label = NULL, params = list(), nrounds,
 \item{label}{the response variable. User should not set this field,}
 
 \item{params}{the list of parameters. Commonly used ones are:
-  objective: objective function, common ones are
+\itemize{
-      - reg:linear  linear regression
+  \item \code{objective} objective function, common ones are
-      - binary:logistic logistic regression for classification
+  \itemize{
-  eta: step size of each boosting step
+    \item \code{reg:linear} linear regression
-  max_depth: maximum depth of the tree
+    \item \code{binary:logistic} logistic regression for classification
-  nthread: number of thread used in training, if not set, all threads are used
+  }
+  \item \code{eta} step size of each boosting step
+  \item \code{max_depth} maximum depth of the tree
+  \item \code{nthread} number of thread used in training, if not set, all threads are used
+}
 
   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
   further details. See also demo/demo.R for walkthrough example in R.}