Instead of nb2mat try as.spam.listw(nb2listw(cell2nb(...)))
this will coerce the adjacency matrix into a sparse matrix representation saving lot of memory. Nikhil On Sun, Jun 20, 2010 at 10:27 PM, Daniel Malter <dan...@umd.edu> wrote: > > Hi, thanks much. This works in principle. The corrected code is below: > > a <- nb2mat(cell2nb(nrow(x),ncol(x),torus=T), style="B") > g <- delete.vertices(graph.adjacency(a), which(x!=1)-1) > > plot(g) > clusters(g) > > the $no argument of the clusters(g) function is the sought after number. > However, the function is very slow, and my machine runs out of memory (1G) > for a 101 by 101 matrix. > > Daniel > -- > View this message in context: > http://r.789695.n4.nabble.com/Spatial-number-of-independent-components-tp2262018p2262090.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
