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Fix links in R doc. (#9450)

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Jiaming Yuan 2023-08-10 02:38:14 +08:00 committed by GitHub
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2 changed files with 4 additions and 4 deletions
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4
R-package/R/xgb.plot.shap.R
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@ -193,7 +193,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
#' hence allows us to see which features have a negative / positive contribution #' hence allows us to see which features have a negative / positive contribution
#' on the model prediction, and whether the contribution is different for larger #' on the model prediction, and whether the contribution is different for larger
#' or smaller values of the feature. We effectively try to replicate the #' or smaller values of the feature. We effectively try to replicate the
#' \code{summary_plot} function from https://github.com/slundberg/shap. #' \code{summary_plot} function from https://github.com/shap/shap.
#' #'
#' @inheritParams xgb.plot.shap #' @inheritParams xgb.plot.shap
#' #'
@ -202,7 +202,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
#' #'
#' @examples # See \code{\link{xgb.plot.shap}}. #' @examples # See \code{\link{xgb.plot.shap}}.
#' @seealso \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}}, #' @seealso \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
#' \url{https://github.com/slundberg/shap} #' \url{https://github.com/shap/shap}
xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL, xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL,
trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) { trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) {
# Only ggplot implementation is available. # Only ggplot implementation is available.

4
R-package/man/xgb.plot.shap.summary.Rd
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@ -67,12 +67,12 @@ Each point (observation) is coloured based on its feature value. The plot
hence allows us to see which features have a negative / positive contribution hence allows us to see which features have a negative / positive contribution
on the model prediction, and whether the contribution is different for larger on the model prediction, and whether the contribution is different for larger
or smaller values of the feature. We effectively try to replicate the or smaller values of the feature. We effectively try to replicate the
\code{summary_plot} function from https://github.com/slundberg/shap. \code{summary_plot} function from https://github.com/shap/shap.
} }
\examples{ \examples{
# See \code{\link{xgb.plot.shap}}. # See \code{\link{xgb.plot.shap}}.
} }
\seealso{ \seealso{
\code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}}, \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
\url{https://github.com/slundberg/shap} \url{https://github.com/shap/shap}
} }