R/feature.table.R

In OmicsMarkeR: Classification and Feature Selection for 'Omics' Datasets

#' @title Feature Consistency Table
#' @description Extracts and sorts the features identified for a given method.
#' @param features A fs.stability fitted object
#' @param method Algorithm of interest 
#' Available options are \code{"plsda"} (Partial Least Squares 
#' Discriminant Analysis), \code{"rf"} (Random Forest), 
#' \code{"gbm"} (Gradient Boosting Machine), \code{"svm"} 
#' (Support Vector Machines), \code{"glmnet"} 
#' (Elastic-net Generalized Linear Model), and \code{"pam"} 
#' (Prediction Analysis of Microarrays)
#' @return A data frame containing:
#'  \item{features}{Features identified by model}
#'  \item{consistency}{Number of iterations feature was identified}
#'  \item{frequency}{Frequency of iterations the feature was identified}
#' @author Charles Determan Jr
#' @example inst/examples/feature.table.R
#' @export


feature.table <- function(features, method){
    assert_is_list(features)
    assert_is_character(method)
        
    if(!method %in% features$methods){
        stop("Method was not used on fitted data")
    }
    
    feats <- as.data.frame(
        lapply(features$features[method],'[[', "features"))
    k <- ncol(feats)
    table.dat <- sort(table(unlist(feats)), decreasing = TRUE)
    out <- as.matrix(table.dat)
    out <- cbind(
        as.character(rownames(out)), 
        as.numeric(out[,1]), 
        round(as.numeric(out[,1])/k, 3)
    )
    colnames(out) <- c("features", "consistency", "frequency")
    out <- data.frame(out)
    out
}