FRESA.CAD (version 3.4.8)

multivariate_BinEnsemble: Multivariate Filters

Description

Returns the top set of features that are associated with the outcome based on Multivariate logistic models: LASSO and BSWiMS

Usage

multivariate_BinEnsemble(data,Outcome,limit=-1,adjustMethod="BH",...)

Value

Named vector with the adjusted p-values of the associted features

Arguments

data

The data frame

Outcome

The outcome feature

adjustMethod

The method used by the p.adjust method

limit

The samples-wise fraction of features to return.

...

Parameters to be passed to the correlated_Remove function

Author

Jose G. Tamez-Pena

Examples

    if (FALSE) {

        library("FRESA.CAD")

        ### Univariate Filter Examples ####

        # Get the stage C prostate cancer data from the rpart package
        data(stagec,package = "rpart")

        # Prepare the data. Create a model matrix without the event time and interactions
        stagec$pgtime <- NULL
        stagec$eet <- as.factor(stagec$eet)
        options(na.action = 'na.pass')
        stagec_mat <- cbind(pgstat = stagec$pgstat,
                            as.data.frame(model.matrix(pgstat ~ .*.,stagec))[-1])
        fnames <- colnames(stagec_mat)
        fnames <- str_replace_all(fnames,":","__")
        colnames(stagec_mat) <- fnames

        # Impute the missing data
        dataCancerImputed <- nearestNeighborImpute(stagec_mat)
        dataCancerImputed[,1:ncol(dataCancerImputed)] <- sapply(dataCancerImputed,as.numeric)

        # Get the top Features associated to pgstat

        q_values <- multivariate_BinEnsemble(data=dataCancerImputed, 
                                    Outcome="pgstat")



    }