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[R] Fix for cran submission of xgboost 0.6 (#1875)

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fix cran check
This commit is contained in:
Tong He 2016-12-15 12:04:54 -08:00 committed by GitHub
parent d943720883
commit 674024c53a
6 changed files with 7 additions and 7 deletions
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2
R-package/R/xgb.create.features.R
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@ -18,7 +18,7 @@
#'
#' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
#'
#' \url{https://research.facebook.com/publications/758569837499391/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
#' \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
#'
#' Extract explaining the method:
#'

2
R-package/R/xgb.dump.R
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@ -14,7 +14,7 @@
#' When this option is on, the model dump comes with two additional statistics:
#' gain is the approximate loss function gain we get in each split;
#' cover is the sum of second order gradient in each node.
#' @param dump_fomat either 'text' or 'json' format could be specified.
#' @param dump_format either 'text' or 'json' format could be specified.
#' @param ... currently not used
#'
#' @return

2
R-package/R/xgb.train.R
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@ -119,7 +119,7 @@
#' \itemize{
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss}
#' \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss/}
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
#' Different threshold (e.g., 0.) could be specified as "error@0."

2
R-package/man/xgb.create.features.Rd
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@ -29,7 +29,7 @@ Joaquin Quinonero Candela)}
International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
\url{https://research.facebook.com/publications/758569837499391/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
\url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
Extract explaining the method:

4
R-package/man/xgb.dump.Rd
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@ -24,9 +24,9 @@ When this option is on, the model dump comes with two additional statistics:
gain is the approximate loss function gain we get in each split;
cover is the sum of second order gradient in each node.}
\item{...}{currently not used}
\item{dump_format}{either 'text' or 'json' format could be specified.}
\item{dump_fomat}{either 'text' or 'json' format could be specified.}
\item{...}{currently not used}
}
\value{
if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}.

2
R-package/man/xgb.train.Rd
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@ -174,7 +174,7 @@ The folloiwing is the list of built-in metrics for which Xgboost provides optimi
\itemize{
\item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss}
\item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss/}
\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
Different threshold (e.g., 0.) could be specified as "error@0."