Thanks a lot for your help, Roger! I ran into another problem when I try to calibrate spatial lag and spatial durbin model with this dataset using spdep. The error messages that I got are:
Error in solve.default(-(mat), tol.solve = tol.solve) : Lapack routine dgesv: system is exactly singular In addition: There were 38 warnings (use warnings() to see them) > warnings() Warning messages: 1: In determinant(x, TRUE) : This version of the Matrix package returns |determinant(L)| instead of determinant(A), i.e., a *DIFFERENT* value. If still necessary, do change your code, following http://matrix.r-forge.r-project.org 2: In optimize(sar.lag.mixed.f, interval = interval, maximum = TRUE, ... : NA/Inf replaced by maximum positive value ...... I have been able to calibrate the spatial lag model with the same dataset using Geoda. Could anyone help me figure this out? Thanks a lot! Mi On Mon, Jun 21, 2010 at 5:13 AM, Roger Bivand <roger.biv...@nhh.no> wrote: > On Sun, 20 Jun 2010, Mi Diao wrote: > >> Dear all, >> >> I am trying to calibrate a Spatial Durbin Model for a dataset with >> 52000 observations. Geoda can handle a dataset of this size, but it >> can only estimate spatial lag and spatial error model. Can the spdep >> package of R handle my dataset? > > Yes. At a recent demo, I ran the 25k ?house example on a small memory older > laptop without trouble (http://spatial.nhh.no/R/sea10.zip). With 50k and > many RHS variables, you may find a busy Windows 1Gb machine gasps a bit - if > so, shut down other programs (IE etc.) before starting R. Look at ?lagsarlm, > then ?impacts. Typically, you'll need something like: > > # lw is your weights object, probably from nb2listw() > # df is the data.frame with your data > > # first generate sparse matrix product traces > W <- as(as_dgRMatrix_lw(lw), "CsparseMatrix") > trMat <- trW(W, type="MC") > > SD_fit <- lagsarlm(y ~ x, data=df, listw=lw, type="mixed", > method="Matrix", tr=trMat, control=list(compiled_sse=TRUE)) > # type="mixed" fits a Spatial Durbin model; > # method could also be "Chebyshev" or "MC" for approximations, > # "Matrix" uses updating sparse Cholesky Jacobians, see ?do_ldet > # for details. "Matrix" needs symmetric neighbours, some other methods > # may not. Beware of k-nearest neighbour schemes, for the same reasons > # as in GeoDa > > summary(SD_fit) > > # now generate samples from the fitted model to assess the > # significance of the impacts > imp_SD_fit <- impacts(SD_fit, tr=trMat, R=2000) > summary(imp_SD_fit) > plot(imp_SD_fit) > summary(imp_SD_fit, zstats=TRUE, short=TRUE) > > Hope this helps, > > Roger > >> >> Thanks a lot for your kind help! >> >> Mi >> >> _______________________________________________ >> R-sig-Geo mailing list >> R-sig-Geo@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/r-sig-geo >> > > -- > Roger Bivand > Economic Geography Section, Department of Economics, Norwegian School of > Economics and Business Administration, Helleveien 30, N-5045 Bergen, > Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43 > e-mail: roger.biv...@nhh.no > > -- -------------------------------------------------------------- Mi Diao Ph. D. Candidate Department of Urban Studies and Planning Massachusetts Institute of Technology Cambridge, MA 02139 _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo