Thomas Lumley <tlumley <at> u.washington.edu> writes:
:
: On Mon, 3 Jan 2005, Doran, Harold wrote:
:
: > Dear List:
: >
: > I am having to build a block-diagonal matrix (vl) and am currently using
: > the following code.
: >
: > I<-diag(sample.size)
: > vl<-kronecker(I,vl.mat)
: >
: >
: > This code works fine, but for large N, it is a huge memory hog. Is there
: > a more efficient method for constructing vl?
: >
:
: Obvious alternatives such as
:
: nr<-nrow(v1.mat)
: nc<-ncol(v1.mat)
: result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc)
: for(i in 1:sample.size){
: result[ (i-1)*nr+1:nr, (i-1)*nc+1:nc]<-v1.mat
: }
:
: or
: nr<-nrow(v1.mat)
: nc<-ncol(v1.mat)
: iy<-as.vector(outer(rep(1:nc,each=nr),(0:(sample.size-1))*nc,"+"))
: ix<-as.vector(outer(rep(1:nr, nc),(0:(sample.size-1))*nr,"+"))
: result<-matrix(0,nrow=sample.size*nr,ncol=sample.size*nc)
: result[cbind(ix,iy)]<-v1.mat
:
: seem to take a little less memory and about the same time,
: but constructing a large block-diagonal matrix is intrinsically an
: inefficient thing to do.
You could try combining some of those ideas with use of the SparseM
package.
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