sodc.clust: Sparse Optimal Discriminant Clustering

In SODC: Optimal Discriminant Clustering(ODC) and Sparse Optimal Discriminant Clustering(SODC)

Description

To perform Sparse Optimal Discriminant Clustering

Usage

sodc.clust(x, centers, l1 = -1, l2 = -1, cv.num = 5, clus = kmeans, boot.num = 20, 
l2.idx = seq(-3, 3, by = 6/20), l1.idx = seq(-3, 3, by = 6/20))

Arguments

x	A numberic dataset matrix.
centers	An integer scalar with the desired number of groups.
l1	L1 penalty parameter. The larger lambda1, the sparser result. if l1==-1, will automatically select optimal lambda1.
l2	L2 penalty parameter. if l2==-1, will automatically select optimal lambda2.
cv.num	The numbers of cross validation iteration for selecting tuning parameter lambda2.
clus	The clustering method applied on SODC component.
boot.num	The numbers of bootstrap iteration for selecting tuning parameter lambda1.
l2.idx	A sequence of index numbers from which one can get a sequence of lambda2 values by calculating 10^l2.idx.
l1.idx	A sequence of index numbers from which one can get a sequence of lambda1 values by calculating 10^l1.idx.

Value

cl	An object of class "kmeans" or other class depending on the value of argument "clus" using SODC component.
clvar	An object of class "kmeans" or other class depending on the value of argument "clus" using SODC selected varialbes.
opt.lambda1	optimal lambda1 selected. If l1!=-1, will return opt.lambda1=l1.
opt.lambda2	optimal lambda2 selected. If l2!=-1, will return opt.lambda2=l2.

SODC documentation built on May 2, 2019, 3:35 p.m.