SSRidgeFusion-class: Class '"SSRidgeFusion"'

In RidgeFusion: R Package for Ridge Fusion in Statistical Learning

Description

A class to implement semi-supervised model based clustering with ridge fusion precision matrix estimation

Usage

SSRidgeFusion(...)
predict.SSRidgeFusion(object,newdata,class=TRUE,...)

Arguments

...	Optional Arguments
object	An object of RidgeFusedQDA
newdata	data to be predicted
class	if TRUE then predicted classes are returned if false QDA scores are returned

Objects from the Class

Objects can be created by calls of the form SSRidgeFusion(...).

Slots

Alphas:

Object of class "matrix" ~~

Means:

Object of class "list" ~~

Pi:

Object of class "vector" ~~

Omega:

Object of class "list" ~~

Ridge:

Object of class "numeric" ~~

FusedRidge:

Object of class "numeric" ~~

iter:

Object of class "numeric" ~~

Extends

Class "RidgeFusion", directly.

Methods

predict

signature(object = "SSRidgeFusion"): ...

print

signature(x = "SSRidgeFusion"): ...

Author(s)

Brad Price

Examples

showClass("SSRidgeFusion")
## Creating a toy example with 5 variables
library(mvtnorm)
set.seed(526)
p=5
    Sig1=matrix(0,p,p)
	for(j in 1:p){
		for(i in j:p){          
            Sig1[j,i]=.7^abs(i-j)
            Sig1[i,j]=Sig1[j,i]
            
		}
	}
    Sig2=diag(c(rep(2,p-5),rep(1,5)),p,p)
X1=rmvnorm(100,rep(2*log(p)/p,p),Sig1)
Y=rmvnorm(100,,Sig2)

## Creating a list of the data for each class
Z=list(X1,Y)

##Creating Unlabeled data set
Z1=rmvnorm(250,rep(2*log(p)/p,p),Sig1)
Z2=rmvnorm(250,,Sig2)
ZU=rbind(Z1,Z2)
## Running Semi-Supervised Ridge Fused Model based clustering 
Hi=SSRidgeFused(Z,ZU,1,1,Scale=TRUE,warm=NULL)
Class=predict(Hi,Z1,class=TRUE)
Score=predict(Hi,Z1,class=FALSE)

RidgeFusion documentation built on May 1, 2019, 8:03 p.m.