# SSRidgeFused: Semis Supervised Ridge Fusion Model Based Clustering In RidgeFusion: R Package for Ridge Fusion in Statistical Learning

## Description

Calculates parameters for model based clustering using ridge fusion estimation of precision matrix

## Usage

 `1` ```SSRidgeFused(Z, Xu, lambda1, lambda2, Scale=FALSE, warm=NULL,tol=.001) ```

## Arguments

 `Z` A list of length J that contains the labeled data for each class `Xu` The unlabeled data `lambda1` A vector with all possible Ridge tuning parameters `lambda2` A vector with all possible Ridge Fusion tuning parameters `Scale` If `TRUE` scale invariant method is used `warm` Default is `NULL`, if initialized with mixing distributions for each of the unlabeled data, will use in initialization of parameters `tol` tolerence for convergence criterion of the alphas

## Value

An object of class `SSRidgeFusion`, basically a list including elements

 `Omega` a list where each element is the precision matrix estimate for the corresponding element of S `Ridge` lambda1 `FusedRidge` lambda2 `iter` The number of iterations until the EM algorithm converged `Alpha` Mixing coefficients for each of the unlabeled data points `Means` Class/Cluster Means `Pi` Probability Mass Function for the classes

## Author(s)

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```## 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) ## Showing example of a warm.start Hi2=SSRidgeFused(Z,ZU,1,1,Scale=TRUE,warm=Hi\$Alphas) ```