MNLTrainTest doing 6 k-fold validations rather than breaking into testing and training.

[TWhiteleyAPHA]

The MNLTrainTest function is not doing as described. The function inputs dictates a percent_cross but then k-fold cross validation occurs- with 6 folds hard coded in.

If you would like my rewrite, I have rewritten the function to repeat lines 21-41 nboot times and save the return the mean output.

MNLTrainTest<-function(mnl_input="mnl_input_0.csv",percent_cross=0.7,nboot=100)
{
  library(nnet)  
  set.seed(123)  
  
  #Read in data
  data<-read.table(mnl_input,sep=",",header = TRUE)
  #data <- subset(data, select = -c(NA.) )
  data2<-subset(data,select=-1)
  
  accuracy=c()
  
  for(i in seq(1,nboot)){

    trainingRows <- sample(1:nrow(data2), percent_cross*nrow(data))
    training <- data2[trainingRows, ]
    testing <- data2[-trainingRows, ]

    multinomModel <- multinom(Source ~ ., data=training, maxit=1000) # multinom Model

    # predicted_scores <- predict (multinomModel, testing, "probs") 
    predicted_class <- predict (multinomModel, testing)
    # table(predicted_class, testing2$Source)
    
    # Calculate the number of correctly predicted sources.
    accuracy<-c(accuracy,mean(as.character(predicted_class) == as.character(testing$Source)))
  }
  
  return(mean(accuracy))
  
}