R/pretty.gbm.tree.R

In gbm-developers/gbm: Generalized Boosted Regression Models

#' Print gbm tree components
#' 
#' \code{gbm} stores the collection of trees used to construct the model in a
#' compact matrix structure. This function extracts the information from a
#' single tree and displays it in a slightly more readable form. This function
#' is mostly for debugging purposes and to satisfy some users' curiosity.
#' 
#' 
#' @param object a \code{\link{gbm.object}} initially fit using
#' \code{\link{gbm}}
#' @param i.tree the index of the tree component to extract from \code{object}
#' and display
#' @return \code{pretty.gbm.tree} returns a data frame. Each row corresponds to
#' a node in the tree. Columns indicate \item{SplitVar}{index of which variable
#' is used to split. -1 indicates a terminal node.} \item{SplitCodePred}{if the
#' split variable is continuous then this component is the split point. If the
#' split variable is categorical then this component contains the index of
#' \code{object$c.split} that describes the categorical split. If the node is a
#' terminal node then this is the prediction.} \item{LeftNode}{the index of the
#' row corresponding to the left node.} \item{RightNode}{the index of the row
#' corresponding to the right node.} \item{ErrorReduction}{the reduction in the
#' loss function as a result of splitting this node.} \item{Weight}{the total
#' weight of observations in the node. If weights are all equal to 1 then this
#' is the number of observations in the node.}
#' @author Greg Ridgeway \email{gregridgeway@@gmail.com}
#' @seealso \code{\link{gbm}}, \code{\link{gbm.object}}
#' @keywords print
#' @export pretty.gbm.tree
pretty.gbm.tree <- function(object,i.tree=1)
{
   if((i.tree<1) || (i.tree>length(object$trees)))
   {
      stop("i.tree is out of range. Must be less than ",length(object$trees))
   }
   else
   {
      temp <- data.frame(object$trees[[i.tree]])
      names(temp) <- c("SplitVar","SplitCodePred","LeftNode",
                       "RightNode","MissingNode","ErrorReduction",
                       "Weight","Prediction")
      row.names(temp) <- 0:(nrow(temp)-1)
   }
   return(temp)
}