Description Usage Arguments Value Author(s) Examples
Calculates the Valdiation Likelihood Score for candidate tuning parameters
1 | RidgeFusedCV(X,lambda1,lambda2,Fold,tol=10^-6,warm.start=TRUE,scaleCV=FALSE,INF=FALSE)
|
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 |
INF |
If |
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 |
Brad Price
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)
|
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