Description Usage Arguments Value Author(s) Examples
Calculates parameters for model based clustering using ridge fusion estimation of precision matrix
1 | SSRidgeFused(Z, Xu, lambda1, lambda2, Scale=FALSE, warm=NULL,tol=.001)
|
Z |
A list of length J that contains the labeled data for each class |
Xu |
The unlabeled data |
lambda1 |
A vector with all possible Ridge tuning parameters |
lambda2 |
A vector with all possible Ridge Fusion tuning parameters |
Scale |
If |
warm |
Default is |
tol |
tolerence for convergence criterion of the alphas |
An object of class SSRidgeFusion
, basically a list including elements
Omega |
a list where each element is the precision matrix estimate for the corresponding element of S |
Ridge |
lambda1 |
FusedRidge |
lambda2 |
iter |
The number of iterations until the EM algorithm converged |
Alpha |
Mixing coefficients for each of the unlabeled data points |
Means |
Class/Cluster Means |
Pi |
Probability Mass Function for the classes |
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 | ## 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)
## Running Semi-Supervised Ridge Fused Model based clustering
Hi=SSRidgeFused(Z,ZU,1,1,Scale=TRUE,warm=NULL)
## Showing example of a warm.start
Hi2=SSRidgeFused(Z,ZU,1,1,Scale=TRUE,warm=Hi$Alphas)
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