# RidgeFusedCV: Ridged Fused Validation Likelihood In RidgeFusion: R Package for Ridge Fusion in Statistical Learning

## Description

Calculates the Valdiation Likelihood Score for candidate tuning parameters

## Usage

 `1` ```RidgeFusedCV(X,lambda1,lambda2,Fold,tol=10^-6,warm.start=TRUE,scaleCV=FALSE,INF=FALSE) ```

## Arguments

 `X` A list of length J that contains the data for each class `lambda1` A vector with all possible Ridge tuning parameters `lambda2` A vector with all possible Ridge Fusion tuning parameters `Fold` A list of length K, the number of folds, where each element is a list of length of the number of classes that contains the indices for the kth fold of the jth class. Fold[[1]][[1]] contains the indices of the first fold in class 1, Fold[[1]][[2]] contains the indices of the first fold of class 2 `tol` Convergence tolerance for blockwise coordinate descent algorithm at each grid point `warm.start` A True/False variable, that indicates if warm.starts should be used `scaleCV` If `TRUE` scale invariant method is used `INF` If `TRUE` sets all inverse covariance matrices equal in the result

## Value

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

 `Omega` a list where each element is the precision matrix estimate for the corresponding element of S `BestRidge` The ridge grid point that minimizes the validation likelihood score `BestFusedRidge` The fused ridge grid point that minimizes the validation likelihood score `CV` The matrix of validation likelihood scores and the grid points they match

## 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``` ```## 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) 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)] } } ## Calculating Validation likelihood scores for ##tuning parameter grid 10^(-1:1) for Ridge, and 10^(2:3) for Ridge Fusion Tell=RidgeFusedCV(Z,10^(-1:1),10^(2:3),Fold1,scaleCV=TRUE) Tell names(Tell) ```