covKCD (version 0.1)

mcov: Matrix-variate covariance matrix

Description

Compute the covariance matrix of a sample of data matrices.

Usage

mcov(Y, use = "everything")

Value

a p1*p2 by p1*p2 sample covariance matrix of the n vectorized data matrices.

Arguments

Y

a numeric n*p1*p2 data array corresponding to n data matrices of dimension p1*p2.

use

a character string giving method for dealing with missing values, fed to the cov function.

Author

Peter Hoff

Examples

p1<-4 ; p2<-3 ; n<-200

# create a matrix Y with separable covariance
Sig1<-rWishart(1,p1,diag(p1))[,,1] 
Sig2<-rWishart(1,p2,diag(p2))[,,1] 

Y<-array(rnorm(n*p1*p2),dim=c(n,p1,p2))  
Y<-aperm( apply(Y,c(1,3),function(y){ msqrt(Sig1)%*%y } ),c(2,1,3)) 
Y<-aperm( apply(Y,c(1,2),function(y){ msqrt(Sig2)%*%y } ),c(2,3,1)) 

# covariance 
S<-mcov(Y) 
image(S)     

plot(S,kronecker(Sig2,Sig1)) ; abline(0,1)