#' MegaR validation
#'
#' This function conducts 10 fold cross validation on N set of data
#' @param Num No. of sets to run for validation
#' @param modelclas one of the model, randomforest, supportvector machine or
#' generalized linear model
#' @param mytable3 processed input file with features
#' @param classid the column number in metadata file in which the class of
#' input data is stored
#' @param ruleout the class which is to be removed from classification model
#' @param sampleid the column number of metadata file which contain sample ids
#' that match with input data
#' @param psd the percentage of data to be split into training set
#' @param metadat the metadata path
#'
#' @export
validation<-function(Num,modelclas,mytable3,classid,sampleid,ruleout,psd,
metadat, optparam){
otu_table_scaled <- mytable3
otu_table_scaled_state <- data.frame(t(otu_table_scaled))
otu_table_scaled_state$country <- metadat[,classid][match(
rownames (otu_table_scaled_state),
metadat[,sampleid])]
otu_table_scaled_state <- stats::na.omit(otu_table_scaled_state)
otu_table_scaled_state$country <- factor(otu_table_scaled_state$country, levels = ruleout) # add this lin
otu_table_scaled_state1 <- stats::na.omit(droplevels( otu_table_scaled_state))
fit_control <- caret::trainControl(method = "LOOCV")
progress <- shiny::Progress$new(style = 'notification')
progress$set(message = "Working...",value = 0)
Acc3<- NULL
Kpp3 <- NULL
for (i in 1:Num) {
smp_size <- floor((psd/100) * nrow(otu_table_scaled_state1))
train_ind <- sample(seq_len(nrow(otu_table_scaled_state1)),
size = smp_size)
mtrain <- otu_table_scaled_state1[train_ind, ]
#mtrain<-droplevels(mtrain)
test <- otu_table_scaled_state1[-train_ind,]
if(modelclas == "RF"){
#RF_state_classify <- randomForest::randomForest(
# as.factor(country)~. ,data =train,importance = T,proximities=T)
RF_state_classify_loocv <- caret::train(
as.factor(country)~. , data = mtrain,method="rf",ntree= 501,
.mtry = optparam, trControl=fit_control)
Acc3[i] <- RF_state_classify_loocv$results$Accuracy
Kpp3[i] <- RF_state_classify_loocv$results$Kappa
#optparam
}
else if(modelclas == "svmmodel"){
RF_state_classify_loocv <- caret::train(as.factor(country)~. ,
data =mtrain , method="svmLinear",
trControl=fit_control, .C=optparam)
Acc3[i] <- RF_state_classify_loocv$results$Accuracy
Kpp3[i] <- RF_state_classify_loocv$results$Kappa
}
else {
RF_state_classify_loocv <- caret::train(as.factor(country)~. ,
data =mtrain , method="glm" ,
trControl=fit_control)
Acc3[i] <- RF_state_classify_loocv$results$Accuracy
Kpp3[i] <- RF_state_classify_loocv$results$Kappa
}
progress$inc(i/Num, detail=paste("Validation set", i+1))
}
on.exit(progress$close())
sprintf("The 10 fold cross validated obtained from the average of %i
independent run is %f. ", Num , sum(Acc3)/Num )
}
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