On Mon, 17 Aug 2009, Michael Haenlein wrote:
Dear Roger,
Thanks again for your help!
Based on your recommendations, I switched to errorsarlm.
Again, method="eigen" resulted in the error message "Error: cannot allocate
vector of size 340.5 Mb" - but I expected that.
I then tried method="Matrix" and got another error message:
"Non-symmetric neighbours list
Error in as_dsTMatrix_listw(listw) : not a symmetric matrix"
Finally I tried method="spam" and got a series of error messages:
"Error in stop("insufficient space for sparse matrix addition") : no
function to return from, jumping to top level
In addition: Warning message: In optimize(sar.error.f.sp, interval =
interval, maximum = TRUE, : NA/Inf replaced by maximum positive value"
Although I got a whole bunch of these messages my model converged to a
solution, but it does not feel right as Lambda is essentially 1 and I don't
get a log likelihood:
The message from the Matrix method was correct, spam ought not to have
tried to continue, I guess it is failing and lambda is ending up at the
end of its interval.
The spatial weights must be symmetric or similar to symmetric for spatial
regression fitting. The Jacobian is found by a sparse Cholesky
decomposition, which means that W must be symmetric. listw2U() should get
you there, I should have spotted the problem before. You may also need to
play with the interval= argument.
Let me know how it goes.
Roger
Call: errorsarlm(formula = y ~ Gender, data = Michael_dat, listw =
Network_01, method = "spam")
Type: error
Coefficients:
lambda (Intercept) GenderM GenderND GenderU
0.99899996 3.86318643 0.11191533 -0.08194235 -0.55121204
Log likelihood: NA
I really feel bad about asking you again.
I will try to make my network symmetrical and hope that this will solve the
error in the method="Matrix" option.
Where the other errors in method="spam" are concerned, I honestly don't have
a clue where the issue might be.
If you could point me to any direction where I might have made a mistake,
I'd appreciate it a lot.
Thanks again very, very much for your help,
Best,
Michael
-----Original Message-----
From: Roger Bivand [mailto:roger.biv...@nhh.no]
Sent: Montag, 17. August 2009 16:20
To: Michael Haenlein
Cc: Haenlein, Michael; r-sig-geo@stat.math.ethz.ch
Subject: RE: [R-sig-Geo] Question spdep package - Moran's I
On Mon, 17 Aug 2009, Michael Haenlein wrote:
Dear Roger,
Thanks very much for your reply!
I think what the reviewer is referring to is a lagsarlm() model --
thanks very much for bringing this functionality to my attention!
I just tried to fit such a model but I get an error message: "Error:
Cannot allocate vector of size 340.5 MB".
I assume this is because my network is very large:
Number of regions: 6681
Number of nonzero links: 39770
Percentage nonzero weights: 0.08909896 Average number of links:
5.952702 Non-symmetric neighbours list
?larsarlm and see method="Matrix". lagsarlm() is not the same model as
errorsarlm(). It will also run slowly in the sparse matrix version; I
suggest using errorsarlm() which does include the spatial autocorrelation
of the error in the model, rather than the spatial autocorrelation of the
dependent variable, so is better aligned with what lm.morantest() was
testing. Both will give likelihood ratio tests of the significance of the
spatial coefficient, but lagsarlm() only gives LR tests of the
coefficients on the x, while with errorsarlm(), you get more-or-less
regular t-tests (they are z-tests because the coefficient standard errors
are the asymptotic ones).
Unless you have a substantive hypothesis for how y_i affects y_j
irrespective of the x variables, errorsalm() may be a safer fallback,
because it is only saying that there is some residual autocorrelation in
the aspatial model, and that is what gets modelled.
Best wishes,
Roger
I'm working on Lenovo Thinkpad (X300) with Windows Vista Business, SP 2
and
2.00 GB RAM.
Would you know whether there is any chance to run this model without
upgrading my RAM?
My current memory.limit() in R is 1535 which seems as far as I can get
with
2.00 GB RAM.
Otherwise I might need to find someone with a more powerful machine than
mine.
Thanks very much again for having pointed me towards lagsarlm()!
Best,
Michael
-----Original Message-----
From: Roger Bivand [mailto:roger.biv...@nhh.no]
Sent: Montag, 17. August 2009 14:30
To: Haenlein, Michael
Cc: r-sig-geo@stat.math.ethz.ch
Subject: Re: [R-sig-Geo] Question spdep package - Moran's I
On Mon, 17 Aug 2009, Haenlein, Michael wrote:
If the referee is refering to the social networks literature, you will
have
to find out what "one step" means there, if anything.
It is not a term used in connection with Moran's I for spatial data.
If you believe that the referee's "one step" might be met by fitting a
model
including both the x variables and the dependence in either y or the
residuals of the regression, look at lagsarlm() and errorsarlm(). Maybe
check against the Ward/Gleditsch "Spatial regression models" Sage volume
if
need be.
When working across disciplines, nothing constitutes absolute authority in
this way. Testing residuals ought to be OK, but see Schabenberger & Gotway
for caveats (especially about concluding that autocorrelation is present
when the real problem is model misspecification).
Hope this helps,
Roger
--
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
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