Updated README and added new files

R/get_15prediction_plot.R

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+get_prediction_plot <- function(Data=NULL,
+                                path_db,
+                                rat_studies=FALSE,
+                                studyid_metadata=NULL,
+                                fake_study = FALSE,
+                                use_xpt_file = FALSE,
+                                Round = FALSE,
+                                Impute = FALSE,
+                                reps,
+                                holdback,
+                                Undersample = FALSE,
+                                hyperparameter_tuning = FALSE,
+                                error_correction_method,
+                                testReps){
+
+
+  if(is.null(Data)){
+    data_and_best.m <- get_Data_formatted_for_ml_and_best.m(path_db=path_db,
+                                                            rat_studies=rat_studies,
+                                                            studyid_metadata=studyid_metadata,
+                                                            fake_study = fake_study,
+                                                            use_xpt_file = use_xpt_file,
+                                                            Round =  Round,
+                                                            Impute = Impute,
+                                                            reps=reps,
+                                                            holdback=holdback,
+                                                            Undersample = Undersample,
+                                                            hyperparameter_tuning = hyperparameter_tuning,
+                                                            error_correction_method=error_correction_method) # = must be 'Flip' or "Prune' or 'None'
+
+  }
+
+  Data <- data_and_best.m[["Data"]]
+  best.m <- data_and_best.m[["best.m"]]
+
+
+
+
+  rfData <- Data
+  #---------------------------------------------------------------------
+  # Initialize model performance metric trackers------------------------
+  #---------------------------------------------------------------------
+
+  # custom function definition
+  `%ni%` <- Negate('%in%')
+
+  Sensitivity <- NULL
+  Specificity <- NULL
+  PPV <- NULL
+  NPV <- NULL
+  Prevalence <- NULL
+  Accuracy <- NULL
+  #nRemoved <- NULL
+
+
+  #-----------------doing cross-validation--------------------------
+  #-----------------------------------------------------------------
+  #------------------------------------------------------------------
+
+  #-----create and prepare "`rfTestData data` frame" for storing predictions----
+  rfTestData <- rfData
+
+  #replaces the existing column names with simple numeric identifiers
+  colnames(rfTestData) <- seq(ncol(rfTestData))
+
+  #emptying the data frame.
+  for (j in seq(ncol(rfTestData))) {
+    rfTestData[,j] <- NA
+  }
+
+  #prepares rfTestData to maintain a consistent structure with the necessary
+  #columns for storing predictions in subsequent iterations of the loop
+  rfTestData <- rfTestData[,1:2] # Keep structure for predictions
+
+  #remove 'gini' from the previous iteration
+  #if (exists('gini')) {rm(gini)}
+
+
+  #-------------------------------------------------------------------
+  # model building and testing----------------------------------------
+  #-------------------------------------------------------------------
+
+
+  # Iterate through test repetitions----------------------------------
+  for (i in seq(testReps)) {
+    if (i == 1) {
+      sampleIndicies <- seq(nrow(rfData))
+    }
+    if (i < testReps) {
+      ind <- sample(seq(nrow(rfData)), floor((nrow(rfData)/testReps)-1), replace = F)
+      sampleIndicies <- sampleIndicies[which(sampleIndicies %ni% ind)]
+    } else {
+      ind <- sampleIndicies
+    }
+
+    trainIndex <- which(seq(nrow(rfData)) %ni% ind)
+    testIndex <- ind
+
+    # ind <- sample(2, nrow(rfData), replace = T, prob = c((1- testHoldBack), testHoldBack))
+    train <- rfData[trainIndex,]
+
+    #train_data_two <- train
+
+    test <- rfData[testIndex,]
+
+    # rfAll <- randomForest::randomForest(Target_Organ ~ ., data=rfData, mytry = best.m,
+    #                                     importance = F, ntree = 500, proximity = T)
+
+
+    # Perform under sampling if enabled
+    if (Undersample == T) {
+      posIndex <- which(train[,1] == 1)
+      nPos <- length(posIndex)
+      # trainIndex <- c(posIndex, sample(which(train[,1] == 0), nPos, replace = F))
+      trainIndex <- c(posIndex, sample(which(train[,1] == 0), nPos, replace = T))
+      train <- train[trainIndex,]
+      test <- rbind(train[-trainIndex,], test)
+    }
+
+    #train_data_two <- train
+
+
+    #model building with current iteration train data
+    # Train Random Forest model--------------------------------------------
+    rf <- randomForest::randomForest(Target_Organ ~ ., data=train, mytry = best.m,
+                                     importance = T, ntree = 500, proximity = T)
+
+    print(rf)
+
+    #----------------------------------------------------------------------
+    #predictions with current model  with current test data
+    # @___________________this_line_has_problems_______
+    # Predict probabilities on test data
+    #----------------------------------------------------------------------
+
+    p2r <- stats::predict(rf, test, type = 'prob')[,1]
+
+    #Store these predictions in a structured data frame
+    rfTestData[names(p2r), i] <- as.numeric(p2r)
+
+    #Rounding the Predictions:
+    p2r <- round(p2r)
+  }
+
+
+  #-------------------------------------------------------
+  histoData <- as.data.frame(cbind(rowMeans(rfTestData, na.rm = T), rfData[,1]))
+  histoData[which(histoData[,2] == 1), 2] <- 'Y'
+  histoData[which(histoData[,2] == 2), 2] <- 'N'
+  colnames(histoData) <- c('Probability', 'LIVER')
+
+  H <- p <- histoData %>%
+    ggplot2::ggplot( ggplot2::aes(x=Probability, fill=LIVER)) +
+    ggplot2::geom_histogram( color="#e9ecef", alpha=0.6, position = 'identity') +
+    ggplot2::scale_fill_manual(values=c("#69b3a2", "#404080")) +
+    ggplot2::labs(fill = "LIVER", x = "Model Prediction P(LIVER)", y = "Count")
+
+print(H)
+
+ }