FRESA.CAD (version 3.3.0)

ClustClass: Hybrid Hierarchical Modeling

Description

This function returns the outcome associated features and the supervised-classifier present at each one of the unsupervised data clusters

Usage

ClustClass(formula = formula,
	            data=NULL,
	            filtermethod=univariate_Wilcoxon,
	            clustermethod=GMVECluster,
	            classmethod=LASSO_1SE,
	            filtermethod.control=list(pvalue=0.1,limit=10),
	            clustermethod.control= list(p.threshold = 0.95, 
	                                   p.samplingthreshold = 0.5),
	            classmethod.control=list(family = "binomial")
	            )

Arguments

formula

An object of class formula with the formula to be fitted

data

A data frame where all variables are stored in different columns

filtermethod

The function name that will return the relevant features

clustermethod

The function name that will cluster the data points

classmethod

The function name of the binary classification method

filtermethod.control

A list with the parameters to be passed to the filter function

clustermethod.control

A list with the parameters to be passed to the clustering function

classmethod.control

A list with the parameters to be passed to the classification function

Value

features

The named vector of FDR adjusted p-values returned by the filtering function.

cluster

The clustering function output

models

The list of classification objects per data cluster

Details

This function will first call the filter function that should return the relevant a named vector with the p-value of the features associated with the outcome. Then it will call user-supplied clustering algorithm that must return a relevant data partition based on the discovered features. The returned object of the clustering function must contain a $classification object indicates the class label of each data point. Finally, the function will call the classification function on each cluster returned by the clustering function.

Examples

# NOT RUN {
	
# }
# NOT RUN {
      library(mlbench) # Location of the Sonar data set
	  library(mclust) # The cluster library
      data(Sonar)
      Sonar$Class <- 1*(Sonar$Class == "M")
	  #Train hierachical classifier
      mc <- ClustClass(Class~.,Sonar,clustermethod=Mclust,clustermethod.control=list(G = 1:4))
	  #report the classification
      pb <- predict(mc,Sonar)
      print(table(1*(pb>0.0),Sonar$Class))
	
# }