R/GBM_boot_err_Func.R

In jjsayleraxio/JTIMLmaster: An application of ML methods

#' Application of GBM method
#'
#' Returns the estimated accurasy measures of Sensitivity, Specificity, and Misclassification using Confusion matrix.
#'
#' A GBM model is trained using a training data set and estimate accuracy measures using a test data set. The number of tree is defined by a user and the default value of it is 300.
#'
#' @inheritParams RF.boot.err.Func A data frame of training data set
#' param kernel A kernel function to be used.
#' param cost A cost?
#' @param train ??
#' @param test ??
#' @param depVar ??
#' @param distribution ??
#' @param n.trees ??
#' @return A GBM.err matrix of the estimated accuracy measurements of Sensitivity, Specificity, and Misclassification for Training set, Testing set and overall one.
#' @export

GBM.boot.err.Func <- function(train, test, depVar, distribution = "adaboost", n.trees = 500) {
  variables <- names(train)
  variables <- variables[variables!=depVar]
  f <- as.formula(paste(depVar, paste(variables, collapse = " + "), sep = " ~ "))

  fit <- gbm(f, data = train, distribution = "adaboost", n.trees = n.trees)
  Prediction.test <- round(predict(fit, test, n.trees = n.trees, type = 'response'), digits = 0)
  Prediction.train <- round(predict(fit, train, n.trees = n.trees, type = 'response'), digits = 0)

  ## For test sets
  AUC.test <- verification::roc.area(as.numeric(test[, which(names(test) %in% depVar)]) - 1, as.numeric(Prediction.test) - 1)$A
  Conf.test.Mat <- caret::confusionMatrix(as.factor(Prediction.test), test[, which(names(test) %in% depVar)])
  Sensitivity.test <- Conf.test.Mat$byClass["Sensitivity"]
  Specificity.test <- Conf.test.Mat$byClass["Specificity"]
  Misclassification.test <- (Conf.test.Mat$table[1, 2] + Conf.test.Mat$table[2, 1])/(sum(Conf.test.Mat$table))

  GBM.err.test <- data.frame(Test.Measure = c(AUC.test, Sensitivity.test, Specificity.test, Misclassification.test))
  rownames(GBM.err.test) <- c("AUC.test", "Sensitivity.test", "Specificity.test", "Misclassification.test")

  ## For train sets
  AUC.train <- verification::roc.area(as.numeric(train[, which(names(train) %in% depVar)]) - 1, as.numeric(Prediction.train) -
                                        1)$A
  Conf.train.Mat <- caret::confusionMatrix(as.factor(Prediction.train), train[, which(names(train) %in% depVar)])
  Sensitivity.train <- Conf.train.Mat$byClass["Sensitivity"]
  Specificity.train <- Conf.train.Mat$byClass["Specificity"]
  Misclassification.train <- (Conf.train.Mat$table[1, 2] + Conf.train.Mat$table[2, 1])/(sum(Conf.train.Mat$table))

  GBM.err.train <- data.frame(Train.Measure = c(AUC.train, Sensitivity.train, Specificity.train, Misclassification.train))
  rownames(GBM.err.train) <- c("AUC.train", "Sensitivity.train", "Specificity.train", "Misclassification.train")

  GBM.err <- cbind(GBM.err.test, GBM.err.train)
  GBM.err$overall <- GBM.err$Train.Measure * 0.368 + GBM.err$Test.Measure * 0.632

  return(list(GBM.err = GBM.err))
}