Spatial Lag and Error Model
I have read the documentation, and have tried both the LU and MC methods. I continue to get a similar error. With a subset of the data and a simplified model, then it will execute the command. In my case, I am using both linear and quadratic terms for some independent variables to capture curvature in the marginal effects. These quadratic terms do enter strongly significantly in the linear model, but seem to cause problems for errorsarlm() and lagsarlm(), particularly when the full sample is used. Thanks again for the comments. John. -----Original Message----- From: Roger Bivand [mailto:Roger.Bivand at nhh.no] Sent: Saturday, March 26, 2011 4:03 AM To: Janmaat, John Cc: r-sig-geo at r-project.org Subject: RE: [R-sig-Geo] Spatial Lag and Error Model
On Fri, 25 Mar 2011, Janmaat, John wrote:
Hello again, Thanks for the suggestion of updating the packages. That dealt with
one
issue. For the rest, here is some of the info you asked for. For errorsarlm and lagsarlm I get a memory allocation problem.
sessionInfo()
R version 2.12.1 (2010-12-16) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252 [4] LC_NUMERIC=C LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] RANN_2.1.2 pgirmess_1.5.1 spdep_0.5-29 coda_0.14-2 deldir_0.0-13 nlme_3.1-98 MASS_7.3-11 Matrix_0.999375-48 [9] boot_1.2-43 spgwr_0.6-10 maptools_0.8-4 lattice_0.19-17 foreign_0.8-42 sp_0.9-79 loaded via a namespace (and not attached): [1] grid_2.12.1 tools_2.12.1 Here are the error messages when commands run for full sample:
wd.GMerrorsarSum <- GMerrorsar(frmSum,data=wd2,weightings) wd.GMerrorsarWin <- GMerrorsar(frmWin,data=wd2,weightings) wd.errorsarSum <- errorsarlm(frmSum,data=wd2,weightings)
Error: cannot allocate vector of size 922.5 Mb
See ?errorrsarlm, method= argument. You should not expect to be able to use dense matrix methods with as many observations. For k nearest neighbours, I suggest LU for an exact result, perhaps MC for an approximation.
wd.errorsarWin <- errorsarlm(frmWin,data=wd2,weightings)
Error: cannot allocate vector of size 922.5 Mb
Ditto.
wd.MESSlagsarSum <- lagmess(frmSum,data=wd2,weightings) wd.MESSlagsarWin <- lagmess(frmWin,data=wd2,weightings) wd.lagsarSum <- lagsarlm(frmSum,data=wd2,weightings)
Error: cannot allocate vector of size 922.5 Mb
Ditto - ?lagsarlm
wd.lagsarWin <- lagsarlm(frmWin,data=wd2,weightings)
Error: cannot allocate vector of size 922.5 Mb
Ditto. You may also see singularities because your RHS variables appear to be close to colinear.
wd.GMerrorsarSum <- gstsls(frmSum, data=wd2,weightings) wd.GMerrorsarWin <- gstsls(frmWin, data=wd2,weightings)
If I run the same set of commands on a subset of the data, then the command output is:
wd.GMerrorsarSum <- GMerrorsar(frmSum,data=wd2,weightings) wd.GMerrorsarWin <- GMerrorsar(frmWin,data=wd2,weightings) wd.errorsarSum <- errorsarlm(frmSum,data=wd2,weightings)
Error in solve.default(asyvar, tol = tol.solve) : system is computationally singular: reciprocal condition number = 8.6909e-27
wd.errorsarWin <- errorsarlm(frmWin,data=wd2,weightings)
Error in solve.default(asyvar, tol = tol.solve) : system is computationally singular: reciprocal condition number = 8.34845e-27
Both of these come when inverting the asymmetric covariance matrix of the variables, read about why this happens on the function help pages - it is described in details, and may also concern the scaling of your variables.
wd.MESSlagsarSum <- lagmess(frmSum,data=wd2,weightings) wd.MESSlagsarWin <- lagmess(frmWin,data=wd2,weightings) wd.lagsarSum <- lagsarlm(frmSum,data=wd2,weightings)
Error in solve.default(inf, tol = tol.solve) : system is computationally singular: reciprocal condition number = 7.82721e-27
wd.lagsarWin <- lagsarlm(frmWin,data=wd2,weightings)
Error in solve.default(inf, tol = tol.solve) : system is computationally singular: reciprocal condition number = 7.81985e-27
Ditto.
wd.GMerrorsarSum <- gstsls(frmSum, data=wd2,weightings) wd.GMerrorsarWin <- gstsls(frmWin, data=wd2,weightings)
Suggestions?
Read the function help pages fully before you use the functions? Roger
-----Original Message----- From: Roger Bivand [mailto:Roger.Bivand at nhh.no] Sent: Friday, March 25, 2011 4:12 AM To: Janmaat, John Cc: r-sig-geo at r-project.org Subject: Re: [R-sig-Geo] Spatial Lag and Error Model On Thu, 24 Mar 2011, Janmaat, John wrote:
-----Original Message----- From: Janmaat, John Sent: Thursday, March 24, 2011 3:40 PM To: 'Roger.Bivand at nhh.no' Subject: RE: [R-sig-Geo] Spatial Lag and Error Model Hello Roger, Thanks for your reply. A code snippet: nl <- knearneigh(cbind(wd2$Long,wd2$Lat),k=4) nb <- knn2nb(nl) weightings <- nb2listw(nb) wd.GMerrorsarSum <- gstsls(frmWin, data=wd2,weightings) The error message: Error in solve.default(QQ, Qye) : system is computationally singular: reciprocal condition number = 4.20784e-28 There are 10,996 observations.
You did not provide sessionInfo() output. If you update spdep, you'll find that this issue (of poorly conditioned crossproduct matrices in the stsls step) was addressed in December 2010, and the current release should work. The code and error messages from errorsarlm() and lagsarlm() will probably also show other misunderstandings, but that can be addressed if you
post
them. Hope this helps, Roger
The dataset has been processed to remove any overlapping points (no
zero
distances) and to remove points that do not have at least one
neighbour
within 100m. Overlapping points existed as accounts would change
when
a
resident moved, leading to multiple observations for a single lot.
This
is what I originally thought could be the issue, as there are then
zero
distances. It has also had all observations with NA removed. The
model
represented in frmWin is solved fine by lm(), with no variables
dropped.
I'm not sure how to check for near linear dependence in WX though, so
I
would appreciate it if you could direct me there. I am using k nearest neighbours, as opposed to rook or queen as my
data
has lat and long for the lot centroid, as opposed to a polygon for
each
lot. There are some natural boundaries within the data, such that it can
be
divided into subsets where within each subset the neighbour list for
the
observations in the subset is no different from that generated for
the
whole dataset. I have also done an analysis separately for such subsets, with the same result. Thanks in advance for any suggestions. John. -----Original Message----- From: Roger Bivand [mailto:Roger.Bivand at nhh.no] Sent: Thursday, March 24, 2011 1:27 AM To: Janmaat, John Cc: r-sig-geo at r-project.org Subject: Re: [R-sig-Geo] Spatial Lag and Error Model On Wed, 23 Mar 2011, Janmaat, John wrote:
Hello, I have a city water use dataset with almost 12000 observations. I
am
using spdep. I am using K nearest neighbours (4) to build the
neighbour
list. I am trying to estimate a model to predict household water
use,
controlling for a set of independent variables like lot size, house size, etc. The spatial model diagnostics, lm.LMtests, suggests that there is both a spatial error and a spatial lag in the data.
However,
on the lm estimations (lagsarlm,errorsarlm), report that the system
is
singular. I can estimate the model using GMerrorsar and lagmess. However, gstsls exits with the same error as the lm estimations. Is there a function I am missing to estimate a model with both
spatial
autocorrelation and a spatial lagged dependent variable that won't
have
the singularity problem?
Well, what we are missing are the verbatim error messages and
function
calls. It may well be that your diagnosis of the problem is not
precise
enough, especially as the code used depends on your choice of input arguments. Does lm() of the same model report any unfitted
coefficients
(are there near-linear dependencies present in the X variables, or between X and WX)? Roger
Thanks, John. -------------------- Dr. John Janmaat Department of Economics I.K. Barber School of Arts and Sciences University of British Columbia - Okanagan Campus 3333 University Way, Kelowna, BC V1V 1V7 Tel: (250)807-8021 WWW: http://people.ok.ubc.ca/jjanmaat/ [[alternative HTML version deleted]]
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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.Bivand at nhh.no