# RidgeFused: Ridged Fused Inverse Covariance Matrix Estimation In RidgeFusion: R Package for Ridge Fusion in Statistical Learning

## Description

Calculates the ridge fusion precision estimator for multiple classes

## Usage

 `1` ```RidgeFused(S,lambda1,lambda2,nc,tol=10^(-7), maxiter=1e3,warm.start=NULL,scale=FALSE) ```

## Arguments

A list is returned where the elements are:

 `S` A list of length J that contains the sample covariance estimators of each class `lambda1` Ridge tuning parameter, must be greater than or equal to 0 `lambda2` Ridge Fusion tuning parameter, must be greater than or equal to 0 `nc` A vector of length J that contains the sample size of each class `tol` Convergence tolerance for blockwise coordinate descent algorithm `maxiter` The number of iterations the algorithm will run if convergence tolerance is not met `warm.start` If `NULL` no warm start is used. If initialized with a list of positive definite inverse covariance matrix estimates of length J, will use them as initialization for the algorithm. `scale` If `FALSE` scale invariant method is used

## Value

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

 `Omega` a list where each element is the inverse covariance matrix estimate for the corresponding element of S `Ridge` lambda1 `FusedRidge` lambda2 `iter` Number of iterations until convergence

## 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 to use as S S=list(0,0) S[[1]]=(99/100)*cov(X1) S[[2]]=(99/100)*cov(Y) ## Creating the vector of sample sizes nc2=c(100,100) ## Running RidgeFused scale invariant method for tuning parameters lambda1=1 ,lambda2=2 A=RidgeFused(S,1,2,nc2,scale=TRUE) A names(A) ```