R/gettrainingdoneglm.R

In BioHPC/megaR: An interactive R package for Metagenomic Data Analysis for Disease Prediction

#' MegaR analysis
#' @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
gettrainingdoneglm <- function(mytable3,classid,sampleid,ruleout,psd,metadat){
    otu_table_scaled <- mytable3
    otu_table_scaled_state <- data.frame(t(otu_table_scaled))
    otu_table_scaled_state$country <- metadat[,classid][match(
        rownames(otu_table_scaled_state), metadat[,sampleid])]
    otu_table_scaled_state <- stats::na.omit(otu_table_scaled_state)
    otu_table_scaled_state <- otu_table_scaled_state[
        otu_table_scaled_state$country != ruleout,]
    otu_table_scaled_state1 <-droplevels( otu_table_scaled_state)

    set.seed(60)
    smp_size <- floor((psd/100) * nrow(otu_table_scaled_state1))
    train_ind <- sample(seq_len(nrow(otu_table_scaled_state1)), size = smp_size)
    train <- otu_table_scaled_state1[train_ind, ]
    train<-droplevels(train)
    test <- otu_table_scaled_state1[-train_ind,]
    RF_state_classify <- caret::train(as.factor(country)~. , data =train,
                                      method = "glm",  maxit=10000, trControl = caret::trainControl(savePredictions = T, classProbs = T, verboseIter = T))
    return(list(train, test, RF_state_classify))
}