R/steprfAVIPredictors.R

In steprf: Stepwise Predictive Variable Selection for Random Forest

#' @title Extract names of the selected predictive variables by steprf
#'
#' @description This function is to extract names of the selected predictive variables by steprfAVI.
#'
#' @param steprf1 a list of output of 'steprf' function.
#' @param trainx a dataframe or matrix contains columns of predictor variables.
#'
#' @return A list with the following components:
#' 1) variables.most.accurate: a list of predictive variables contained in the
#'  most accurate RF model,  2) PABV: a list of predictive variables with positive
#'   contributions to the predictive accuracy of RF models, that is,
#'    predictive accuracy boosting variable (PABV), 3) PARV: a list of
#'    predictive variables with negative contributions to the predictive
#'    accuracy of RF models, that is, predictive accuracy reducing variable,
#'    and 4) max.predictive.accuracy: the predictive accuracy of the most
#'    accurate RF model.
#'
#' @references Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton,
#' Chapman and Hall/CRC.
#'
#' Li, J. (2019). "A critical review of spatial predictive modeling
#' process in environmental sciences with reproducible examples in R." Applied
#' Sciences 9: 2048.
#'
#' Li, J., Siwabessy, J., Huang, Z., and Nichol, S. (2019). "Developing an
#' optimal spatial predictive model for seabed sand content using machine
#' learning, geostatistics and their hybrid methods." Geosciences 9 (4):180.
#'
#' @author Jin Li
#' @examples
#' \donttest{
#' library(spm)
#' data(petrel)
#' set.seed(1234)
#' steprf1 <- steprfAVI(trainx = petrel[, c(1,2, 6:9)], trainy = petrel[, 5],
#'  nsim = 10, min.n.var = 2)
#'
#' steprfAVIPredictors(steprf1, trainx = petrel[, c(1,2, 6:9)])
#' }
#'
#' @export
steprfAVIPredictors <- function (steprf1, trainx) {
  steprf1 <- steprf1
  trainx <- trainx
  predacc <- colMeans(steprf1$predictive.accuracy2)
  maxn <- which(predacc == max(predacc), arr.ind = T )
  steprfPredictors <- setdiff(names(trainx), steprf1$variable.removed[(1:maxn)])

  delta.acc <- steprf1$delta.accuracy
  negative.n <- which(delta.acc <= 0, arr.ind = T )
  positivePredictors <- setdiff(names(trainx), steprf1$variable.removed[negative.n])
  negativePredictors <- steprf1$variable.removed[negative.n]
  max.predacc <- max(predacc)
  list(variables.most.accurate = steprfPredictors, PABV = positivePredictors, PARV = negativePredictors, max.predictive.accuracy = max.predacc)
}