[R-sig-Geo] Spatial Lag and Error Model
Janmaat, John
John.Janmaat at ubc.ca
Sun Mar 27 06:27:11 CEST 2011
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]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo at r-project.org
>>> 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.Bivand at nhh.no
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