refine doc, with Rd

R-package/NAMESPACE

@@ -5,11 +5,11 @@ export(setinfo)
 export(slice)
 export(xgb.DMatrix)
 export(xgb.DMatrix.save)
+export(xgb.cv)
 export(xgb.dump)
 export(xgb.load)
 export(xgb.save)
 export(xgb.train)
-export(xgb.cv)
 export(xgboost)
 exportMethods(predict)
 import(methods)

R-package/R/getinfo.xgb.DMatrix.R

@@ -8,7 +8,10 @@ setClass('xgb.DMatrix')
 #' 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))
 #' @rdname getinfo
 #' @export
 #' 

R-package/R/setinfo.xgb.DMatrix.R

@@ -6,7 +6,10 @@
 #' 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))
 #' @rdname setinfo
 #' @export
 #' 
@@ -16,9 +19,10 @@ setinfo <- function(object, ...){
 
 #' @param object Object of class "xgb.DMatrix"
 #' @param name the name of the field to get
+#' @param info the specific field of information to set
 #' @param ... other parameters
-#' @rdname getinfo
-#' @method getinfo xgb.DMatrix
+#' @rdname setinfo
+#' @method setinfo xgb.DMatrix
 setMethod("setinfo", signature = "xgb.DMatrix", 
           definition = function(object, name, info) {
             xgb.setinfo(object, name, info)

R-package/R/xgb.cv.R

@@ -1,6 +1,6 @@
-#' eXtreme Gradient Boosting Training
+#' Cross Validation
 #' 
-#' The training function of xgboost
+#' The cross valudation function of xgboost
 #'
 #' @param params the list of parameters. Commonly used ones are:
 #' \itemize{
@@ -61,7 +61,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
   params <- append(params, list(silent=1))
   for (mc in metrics) {
     params <- append(params, list("eval_metric"=mc))
-  }    
+  }
 
   folds <- xgb.cv.mknfold(dtrain, nfold, params)
   history <- list()
@@ -70,7 +70,8 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,
     for (k in 1:nfold) {
       fd <- folds[[k]]
       succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)      
-      msg[[k]] <- strsplit(xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval), "\t")[[1]]
+      msg[[k]] <- strsplit(xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval), 
+                           "\t")[[1]]
     }
     ret <- xgb.cv.aggcv(msg, showsd)
     history <- append(history, ret)

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{

R-package/vignettes/xgboost.Rnw

@@ -73,9 +73,10 @@ and ranking. The package is made to be extendible, so that users are also allowe
 \end{enumerate}
 
 
-\section{Example with iris}
+\section{Example with Mushroom data}
 
-In this section, we will illustrate some common usage of \verb@xgboost@.
+In this section, we will illustrate some common usage of \verb@xgboost@. The 
+Mushroom data is cited from UCI Machine Learning Repository. \citep{Bache+Lichman:2013} 
 
 <<Training and prediction with iris>>=
 library(xgboost)

R-package/vignettes/xgboost.bib

@@ -18,3 +18,13 @@
   publisher={Institute of Mathematical Statistics}
 }
 
+
+@misc{
+    Bache+Lichman:2013 ,
+    author = "K. Bache and M. Lichman",
+    year = "2013",
+    title = "{UCI} Machine Learning Repository",
+    url = "http://archive.ics.uci.edu/ml",
+    institution = "University of California, Irvine, School of Information and Computer Sciences" 
+}
+