Description Usage Arguments Value Author(s) Examples
Calculates validation scores for possible tuning parameters for Semi-Supervised Ridge Fusion Model Based Clustering
1 | SSRidgeFusedCV(X,Xu,Lam1,Lam2,Fold,FoldU,scaleCV=FALSE,tolCV=0.01)
|
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 |
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 |
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 |
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 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)
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