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#' Get predictions summarized across trees for out-of-bag cases or all cases
#' for cases from new test data
#'
#' @param rf An object of class \code{mobforest.output}.
#' @param OOB a logical determining whether to return predictions from the
#' out-of-bag sample or the learning sample (not suggested).
#' @param newdata a logical determining whether to return predictions from
#' test data. If newdata = TRUE, then OOB argument is ignored.
#' @return matrix with three columns: 1) Mean Predictions across trees, 2)
#' Standard deviation of predictions across trees, and 3) Residual (mean
#' predicted - observed). The third column is applicable only when linear
#' regression is considered as the node model.
#'
#' @examples
#' \dontrun{
#' library(mlbench)
#' set.seed(1111)
#' # Random Forest analysis of model based recursive partitioning load data
#' data("BostonHousing", package = "mlbench")
#' BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
#'
#' # Recursive partitioning based on linear regression model medv ~ lstat with 3
#' # trees. 1 core/processor used.
#' rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
#' mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = TRUE,
#' alpha = 0.05, bonferroni = TRUE, minsplit = 25), data = BostonHousing,
#' processors = 1, model = linearModel, seed = 1111)
#'
#' # Obtain out-of-bag predicted values
#' OOB_pred_mat <- get.pred.values(rfout, OOB = TRUE)
#' OOB_pred = OOB_pred_mat[, 1]
#' }
#'
#' @export
get.pred.values <- function(rf, OOB = T, newdata = F){
rval <- c()
if (nrow(rf@new_data_predictions@pred_mat) == 0 && newdata == TRUE) {
cat("Predicted values were only computed on original data. Please set",
"newdata = FALSE and run the get.pred.values() again. Or you can",
"re-run the mobforest.analysis() with 'new_test_data' parameter not",
"missing and later use getPredictedValues() to get predicted values",
"on the new test data.")
stop()
}
if (newdata == F) {
if (OOB == T) {
rval <- (rf@oob_predictions)@pred_mat
} else {
rval <- (rf@general_predictions@pred_mat)
}
} else {
rval <- (rf@new_data_predictions)@pred_mat
}
return(rval)
}
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