my.lasso.classify: Sparse Optimal Discriminant Clustering

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

Description

To obtain SODC components and other relevant predictions in SODC method.

Usage

my.lasso.classify(data, c, lambda1, lambda2, tol = 10^(-10), iter.max = 50)

Arguments

data	A numberic dataset matrix.
c	An integer scalar with the desired number of groups.
lambda1	L1 penalty parameter, if lambda1=-1, then odc.clust choose the optimal lambda2 automatically given lambda2.idx. Otherwise perfoms SODC clustering using given lambda1.
lambda2	L2 penalty parameter, if lambda2=-1, then odc.clust choose the optimal lambda2 automatically given lambda2.idx. Otherwise perfoms ODC clustering using given lambda2.
tol	A tolerance value indicating the degree of prediction error of W.
iter.max	the maximum number of iterations allowed.

Value

Z	The SODC component.It is an n by k-1 matrix where n is the number of observations.
varset	An array indicating the selected varialbes index numbers.
what	Predicted W using SODC method.
nvarselected	The number of selected varialbes by SODC.The smaller the value, the sparser.

SODC documentation built on May 30, 2017, 7:11 a.m.