Nothing
simmix.delt<-function(n,M,sig,p,seed,dime=NULL){
#Simulates a mixture of l normal distributions in R^d,
#with diagonal cov matrices
#
#n is the sample size
#M is l*d-matrix, rows are the means
#sig is l*d-matrix, for l:th mixture d covariances
#p is l-vector, proportion for each mixture
#
#returns n*d-matrix
#
set.seed(seed)
if (is.null(dime)){
if (dim(t(M))[1]==1) l<-1 else l<-length(M[,1])
d<-length(M[1,])
data<-matrix(rnorm(d*n),,d) #n*d matriisi, valkoista kohinaa
for (i in 1:n){
ehto<-runif(1)
alku<-0
loppu<-p[1]
lippu<-0
for (j in 1:(l-1)){
if ((alku<=ehto) && (ehto<loppu)){
data[i,]<-sig[j,]*data[i,]+M[j,]
lippu<-1
}
alku<-alku+p[j]
loppu<-loppu+p[j+1]
}
if (lippu==0) data[i,]<-sig[l,]*data[i,]+M[l,]
}
}
if (!is.null(dime) && (dime==1)){
d<-1
l<-length(M)
data<-matrix(rnorm(d*n),,d) #n*d matriisi, valkoista kohinaa
for (i in 1:n){
ehto<-runif(1)
alku<-0
loppu<-p[1]
lippu<-0
for (j in 1:(l-1)){
if ((alku<=ehto) && (ehto<loppu)){
data[i]<-sig[j]*data[i]+M[j]
lippu<-1
}
alku<-alku+p[j]
loppu<-loppu+p[j+1]
}
if (lippu==0) data[i]<-sig[l]*data[i]+M[l]
}
}
return(data)
}
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