Regarding what I asked earlier: no error occurs if there are two exogenous

variables, and it does run incredibly faster (a few seconds instead of 15

minutes or so with the eigen method).

As far as I can understand, the problem I encountered comes from a sequence

in the sub-program dosparse, which says (in a loop over the columns of x):

thisx<-x[,-i]

lm.null<-lm.fit(thisx, y)

If x has only two columns (which is the case if there is only one variable

on the right hand side), thisx has only one column at each step of the loop

and is not seen as a matrix by the lm.fit function, which returns an error.

My questions are now:

- is my interpretation right ?

- is this a bug, or is this a desired feature ?

- if this a bug, is there a way to correct it and be able to run a lagsarlm

with one exogenous variable and the SparseM method ?

Thanks in advance.

Gilles

-----Original Message-----

From: r-sig-geo-bounces at stat.math.ethz.ch

[mailto:r-sig-geo-bounces at stat.math.ethz.ch]On Behalf Of Gilles

Spielvogel

Sent: vendredi 28 janvier 2005 18:14

To: r-sig-geo at stat.math.ethz.ch

Subject: [R-sig-Geo] Problem with lagsarlm (spdep) with SparseM method

Hello,

when running the command:

lagsarc2<-lagsarlm(vary ~ varx , mydata, c2.listw, na.action=na.fail,

type="lag", method="SparseM", quiet=FALSE, zero.policy=TRUE)

R returns the following error:

Spatial lag model

Jacobian calculated using sparse matrix techniques using SparseM

Error in lm.fit(thisx, y) : `x' must be a matrix

The listw object I use comes originally from a queen contiguity GAL object

made with GeoDA. The command works perfectly if I use the option "eigen"

instead of "SparseM", but it takes a lot of time.

Does anyone have any idea about this problem ?

Thanks in advance for your help.

Gilles

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