# SSRidgeFusedCV: Tuning Parameter Selection For Semi-Supervised Ridge Fusion... In RidgeFusion: R Package for Ridge Fusion in Statistical Learning

## Description

Calculates validation scores for possible tuning parameters for Semi-Supervised Ridge Fusion Model Based Clustering

## Usage

 `1` ```SSRidgeFusedCV(X,Xu,Lam1,Lam2,Fold,FoldU,scaleCV=FALSE,tolCV=0.01) ```

## Arguments

 `X` A list of length J that contains the labeled data for each class `Xu` The unlabeled data `Lam1` A vector with all possible Ridge tuning parameters `Lam2` A vector with all possible Ridge Fusion tuning parameters `scaleCV` If `TRUE` scale invariant method is used `Fold` see Ridge Fused CV usage `FoldU` A list of length of the number of validation sets containing the indices of each set for the unlabeled data `tolCV` Covergence tolerance for each iteration of the cross validation via validation likelihood

## Value

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

 `Omega` a list where each element is the inverse covariance matrix estimate for the corresponding element of S `BestRidge` The grid point of lambda1 that minimizes the validation score `BestFusedRidge` The grid point of lambda2 that minimizes the validation score `CV` Matrix containing the full grid of points that were input and the validation scores

## 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 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50``` ```## Not run: ## 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) Samp=list(0,0) Samp[[1]]=sample(1:100) Samp[[2]]=sample(1:100) ## Creating Fold list Fold1=list(0,0) for(i in 1:5){ Fold1[[i]]=list(0,0) for(j in 1:2){ Fold1[[i]][[j]]=Samp[[j]][((20*(i-1))+1):(i*20)] } } ## Creating Validation sets for unlabeled data SampU=sample(1:500) FoldU1=list(0,0) for(i in 1:5){ FoldU1[[i]]=SampU[((100*(i-1)+1)):(i*100)] } Hello=SSRidgeFusedCV(Z,ZU,10^(-2:-1),10^(-3:1),Fold1,FoldU1,scaleCV=FALSE) ## End(Not run) ```