# NOT RUN {
if(require(mvtnorm)){
## Generate two random samples of size 50 from two multivariate normal distributions
# sample size
n<-50
# true parameters of class 1 and class 2
param.class1<-simulation$condition1
param.class2<-simulation$condition2$`5`$`2`
# simulated dataset
data.class1<-rmvnorm(n = n,mean =param.class1$mu ,sigma =param.class1$S)
data.class2<-rmvnorm(n = n,mean =param.class2$mu ,sigma=param.class2$S)
data<-rbind(data.class1,data.class2)
classes<-c(rep(1,nrow(data.class1)),rep(2,nrow(data.class2)))
## estimated parameters: maximum likelihood estimate
s<-cov(data)
s1<-cov(data.class1)
s2<-cov(data.class2)
## default parameters:
# use the minimum of median variances distributions of the three supplied covariance matrices
def.shrink<-shrinkTEGS(s,s1,s2)
def.shrink$lambda
## use customize lamdas
def.shrink<-shrinkTEGS(s,s1,s2,param = list(lambda=c(0.1,0.2,0.3)))
def.shrink$lambda
# use for each covariance matrix the 0.4 percentile of its variances distributions
def.shrink<-shrinkTEGS(s,s1,s2,param = list(type="opt",probs=0.4))
def.shrink$lambda
}
# }
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