R/predict.covregrf.R

In CovRegRF: Covariance Regression with Random Forests

#' Predict method for covregrf objects
#'
#' Obtain predicted covariance matrices using a covregrf forest for training or
#'   new data.
#'
#' @param object An object of class \code{(covregrf, grow)} created by the
#'   function \code{covregrf}.
#' @param newdata Test data of the set of covariates. A data.frame with numeric
#'  values and factors. If missing, the out-of-bag predictions in \code{object}
#'  is returned.
#' @param ... Optional arguments to be passed to other methods.
#'
#' @return An object of class \code{(covregrf, predict)} which is a list with the
#'   following components:
#'
#'   \item{predicted}{Predicted covariance matrices for test data. If
#'     \code{newdata} is missing, OOB predictions for training observations.}
#'   \item{bop}{Bag of Observations for Prediction. An \eqn{nxn} matrix of
#'     counts.}
#'   \item{n}{Sample size of the test data (\code{NA}'s are omitted). If
#'     \code{newdata} is missing, sample size of the training set.}
#'   \item{xvar.names}{A character vector of the covariate names.}
#'   \item{yvar.names}{A character vector of the response variable names.}
#'
#' @examples
#' options(rf.cores=2, mc.cores=2)
#'
#' ## load generated example data
#' data(data, package = "CovRegRF")
#' xvar.names <- colnames(data$X)
#' yvar.names <- colnames(data$Y)
#' data1 <- data.frame(data$X, data$Y)
#'
#' ## define train/test split
#' set.seed(2345)
#' smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE)
#' traindata <- data1[smp,,drop=FALSE]
#' testdata <- data1[-smp, xvar.names, drop=FALSE]
#'
#' ## formula object
#' formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ "))
#'
#' ## train covregrf
#' covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50))
#'
#' ## predict without new data (OOB predictions will be returned)
#' pred.obj <- predict(covregrf.obj)
#' pred.oob <- pred.obj$predicted
#'
#' ## predict with new test data
#' pred.obj2 <- predict(covregrf.obj, newdata = testdata)
#' pred <- pred.obj2$predicted
#'
#'
#' @seealso
#'   \code{\link{covregrf}}
#'   \code{\link{vimp.covregrf}}
#'   \code{\link{print.covregrf}}

predict.covregrf <- function(object,
                           newdata,
                           ...)
{
  result.predict <- generic.predict.covregrf(object,
                                           newdata,
                                           ...)
  return(result.predict)
}