refine doc, with Rd

R-package/man/getinfo.Rd

@@ -23,6 +23,9 @@ Get information of an xgb.DMatrix object
 data(iris)
 iris[,5] <- as.numeric(iris[,5]=='setosa')
 dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
-labels <- getinfo(dtrain, "label")
+labels <- getinfo(dtrain, 'label')
+setinfo(dtrain, 'label', 1-labels)
+labels2 <- getinfo(dtrain, 'label')
+stopifnot(all(labels2 == 1-labels))
 }
 

R-package/man/setinfo.Rd

@@ -0,0 +1,33 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\docType{methods}
+\name{setinfo}
+\alias{setinfo}
+\alias{setinfo,xgb.DMatrix-method}
+\title{Set information of an xgb.DMatrix object}
+\usage{
+setinfo(object, ...)
+
+\S4method{setinfo}{xgb.DMatrix}(object, name, info)
+}
+\arguments{
+\item{object}{Object of class "xgb.DMatrix"}
+
+\item{name}{the name of the field to get}
+
+\item{info}{the specific field of information to set}
+
+\item{...}{other parameters}
+}
+\description{
+Set information of an xgb.DMatrix object
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5]=='setosa')
+dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+labels <- getinfo(dtrain, 'label')
+setinfo(dtrain, 'label', 1-labels)
+labels2 <- getinfo(dtrain, 'label')
+stopifnot(all(labels2 == 1-labels))
+}
+

R-package/man/xgb.cv.Rd

@@ -0,0 +1,66 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.cv}
+\alias{xgb.cv}
+\title{Cross Validation}
+\usage{
+xgb.cv(params = list(), data, nrounds, nfold, label = NULL, showsd = TRUE,
+  metrics = list(), obj = NULL, feval = NULL, ...)
+}
+\arguments{
+\item{params}{the list of parameters. Commonly used ones are:
+\itemize{
+  \item \code{objective} objective function, common ones are
+  \itemize{
+    \item \code{reg:linear} linear regression
+    \item \code{binary:logistic} logistic regression for classification
+  }
+  \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 inst/examples/demo.R for walkthrough example in R.}
+
+\item{data}{takes an \code{xgb.DMatrix} as the input.}
+
+\item{nrounds}{the max number of iterations}
+
+\item{nfold}{number of folds used}
+
+\item{label}{option field, when data is Matrix}
+
+\item{showd}{boolean, whether show standard deviation of cross validation}
+
+\item{metrics,}{list of evaluation metrics to be used in corss validation,
+  when it is not specified, the evaluation metric is chosen according to objective function.
+  Possible options are:
+\itemize{
+  \item \code{error} binary classification error rate
+  \item \code{rmse} Rooted mean square error
+  \item \code{logloss} negative log-likelihood function
+  \item \code{auc} Area under curve
+  \item \code{merror} Exact matching error, used to evaluate multi-class classification
+}}
+
+\item{obj}{customized objective function. Returns gradient and second order
+gradient with given prediction and dtrain,}
+
+\item{feval}{custimized evaluation function. Returns
+\code{list(metric='metric-name', value='metric-value')} with given
+prediction and dtrain,}
+
+\item{...}{other parameters to pass to \code{params}.}
+}
+\description{
+The cross valudation function of xgboost
+}
+\details{
+This is the cross validation function for xgboost
+
+Parallelization is automatically enabled if OpenMP is present.
+Number of threads can also be manually specified via "nthread" parameter.
+
+This function only accepts an \code{xgb.DMatrix} object as the input.
+}
+

R-package/man/xgb.train.Rd

@@ -3,8 +3,8 @@
 \alias{xgb.train}
 \title{eXtreme Gradient Boosting Training}
 \usage{
-xgb.train(params = list(), dtrain, nrounds, watchlist = list(),
-  obj = NULL, feval = NULL, ...)
+xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
+  feval = NULL, verbose = 1, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
@@ -22,7 +22,7 @@ xgb.train(params = list(), dtrain, nrounds, watchlist = list(),
   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
   further details. See also inst/examples/demo.R for walkthrough example in R.}
 
-\item{dtrain}{takes an \code{xgb.DMatrix} as the input.}
+\item{data}{takes an \code{xgb.DMatrix} as the input.}
 
 \item{nrounds}{the max number of iterations}
 
@@ -39,6 +39,9 @@ gradient with given prediction and dtrain,}
 \code{list(metric='metric-name', value='metric-value')} with given
 prediction and dtrain,}
 
+\item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
+  information of performance. If 2, xgboost will print information of both}
+
 \item{...}{other parameters to pass to \code{params}.}
 }
 \description{