Fix links in R doc. (#9450)
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@ -193,7 +193,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
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#' hence allows us to see which features have a negative / positive contribution
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#' on the model prediction, and whether the contribution is different for larger
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#' or smaller values of the feature. We effectively try to replicate the
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#' \code{summary_plot} function from https://github.com/slundberg/shap.
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#' \code{summary_plot} function from https://github.com/shap/shap.
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#'
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#' @inheritParams xgb.plot.shap
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#'
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@ -202,7 +202,7 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
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#'
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#' @examples # See \code{\link{xgb.plot.shap}}.
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#' @seealso \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
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#' \url{https://github.com/slundberg/shap}
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#' \url{https://github.com/shap/shap}
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xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL,
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trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) {
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# Only ggplot implementation is available.
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@ -67,12 +67,12 @@ Each point (observation) is coloured based on its feature value. The plot
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hence allows us to see which features have a negative / positive contribution
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on the model prediction, and whether the contribution is different for larger
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or smaller values of the feature. We effectively try to replicate the
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\code{summary_plot} function from https://github.com/slundberg/shap.
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\code{summary_plot} function from https://github.com/shap/shap.
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}
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\examples{
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# See \code{\link{xgb.plot.shap}}.
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}
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\seealso{
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\code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
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\url{https://github.com/slundberg/shap}
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\url{https://github.com/shap/shap}
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}
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