FilterUnivariate: Univariate Filters

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Univariate Filters

Description

Returns the top set of features that are statistically associated with the outcome.

Usage

univariate_Logit(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH", 
                 uniTest=c("zIDI","zNRI"),limit=0,...,n=0)
univariate_residual(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                    uniTest=c("Ftest","Binomial","Wilcox","tStudent"),
                    type=c("LM","LOGIT"),limit=0,...,n=0)
univariate_tstudent(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                    limit=0,...,n=0)
univariate_Wilcoxon(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                     limit=0,...,n=0)
univariate_KS(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                     limit=0,...,n=0)
univariate_DTS(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                     limit=0,...,n=0)
univariate_correlation(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                       method = "kendall",limit=0,...,n=0)
univariate_cox(data=NULL, Outcome=NULL, pvalue=0.2, adjustMethod="BH",
                     limit=0,...,n=0)
univariate_BinEnsemble(data,Outcome, pvalue=0.2,limit=0,adjustMethod="BH",...)
univariate_Strata(data,Outcome,pvalue=0.2,limit=0,
                   adjustMethod="BH",
                   unifilter=univariate_BinEnsemble,strata="Gender",...)
correlated_Remove(data=NULL,fnames=NULL,thr=0.999,isDataCorMatrix=FALSE)

Arguments

data	The data frame
Outcome	The outcome feature
pvalue	The threshold pvalue used after the p.adjust method
adjustMethod	The method used by the p.adjust method
uniTest	The unitTest to be performed by the linear fitting model
type	The type of linear model: LM or LOGIT
method	The correlation method: pearson,spearman or kendall.
limit	The samples-wise fraction of features to return.
fnames	The list of features to test inside the correlated_Remove function
thr	The maximum correlation to allow between features
unifilter	The filter function to be stratified
strata	The feature to be used for data stratification
...	Parameters to be passed to the correlated_Remove function
n	the number of original features passed to p.adjust
isDataCorMatrix	The provided data is the correlation matrix

Value

Named vector with the adjusted p-values or the list of no-correlated features for the correlated_Remove

Author(s)

Jose G. Tamez-Pena

Examples

    ## Not run: 

        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 <- univariate_Logit(data=dataCancerImputed, 
                                    Outcome="pgstat",
                                    pvalue = 0.05)

        qValueMatrix <- q_values
        idiqValueMatrix <- q_values
        barplot(-log(q_values),las=2,cex.names=0.4,ylab="-log(Q)",
        main="Association with PGStat: IDI Test")

        q_values <- univariate_Logit(data=dataCancerImputed, 
                                    Outcome="pgstat", 
                                    uniTest="zNRI",pvalue = 0.05)
        qValueMatrix <- cbind(idiqValueMatrix,q_values[names(idiqValueMatrix)])

        q_values <- univariate_residual(data=dataCancerImputed, 
                                    Outcome="pgstat", 
                                    pvalue = 0.05,type="LOGIT")
        qValueMatrix <- cbind(qValueMatrix,q_values[names(idiqValueMatrix)])

        q_values <- univariate_tstudent(data=dataCancerImputed, 
                                       Outcome="pgstat", 
                                       pvalue = 0.05)
        qValueMatrix <- cbind(qValueMatrix,q_values[names(idiqValueMatrix)])

        q_values <- univariate_Wilcoxon(data=dataCancerImputed, 
                                       Outcome="pgstat", 
                                       pvalue = 0.05)
        qValueMatrix <- cbind(qValueMatrix,q_values[names(idiqValueMatrix)])

        q_values <- univariate_correlation(data=dataCancerImputed, 
                                       Outcome="pgstat", 
                                       pvalue = 0.05)
        qValueMatrix <- cbind(qValueMatrix,q_values[names(idiqValueMatrix)])

        q_values <- univariate_correlation(data=dataCancerImputed, 
                                          Outcome="pgstat", 
                                          pvalue = 0.05,
                                          method = "pearson")

        #The qValueMatrix has the qValues of all filter methods.  
        qValueMatrix <- cbind(qValueMatrix,q_values[names(idiqValueMatrix)])
        colnames(qValueMatrix) <- c("IDI","NRI","F","t","W","K","P")
        #Do the log transform to display the heatmap
        qValueMatrix <- -log10(qValueMatrix)
        #the Heatmap of the q-values
        gplots::heatmap.2(qValueMatrix,Rowv = FALSE,dendrogram = "col",
        main = "Method q.values",cexRow = 0.4)

    
## End(Not run)

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.