Please read ?lagsarlm under the tol.solve= item:
the tolerance for detecting linear dependencies in the columns of matrices to
be inverted - passed to \code{solve()} (default=1.0e-10). This may be used if
necessary to extract coefficient standard errors (for instance lowering to
1e-12), but errors in \code{solve()} may constitute indications of poorly
scaled variables: if the variables have scales differing much from the
autoregressive coefficient, the values in this matrix may be very different in
scale, and inverting such a matrix is analytically possible by definition, but
numerically unstable; rescaling the RHS variables alleviates this better than
setting tol.solve to a very small value
Does that help?
--
Roger Bivand
NHH Norwegian School of Economics
Helleveien 30, 5045 Bergen, Norway
roger.biv...@nhh.no
________________________________________
Fra: r-sig-geo-boun...@r-project.org [r-sig-geo-boun...@r-project.org] på
vegne av Milan Sharma [milansharma2...@yahoo.com]
Sendt: 29. desember 2012 7:43
To: r-sig-geo@r-project.org
Emne: [R-sig-Geo] fitting lag model in R
Hi all,
can any one of you see whats wrong in the code below?
> model<-lm(Tcrash~X100MVMT+Alcohol+Alignment+PVMT, data=data)#works
> sar<-spautolm(Tcrash~X100MVMT+Alcohol+Alignment+PVMT,listw=WI_queenW,
> data=data)#works
> lag <- lagsarlm(Tcrash~X100MVMT+Alcohol+Alignment+PVMT, data=data,
> listw=listwW)#shows error
Error in solve.default(inf, tol = tol.solve) :
system is computationally singular: reciprocal condition number = 2.44638e-12
> lag <- lagsarlm(Tcrash~X100MVMT+Alcohol+Alignment+PVMT, data=data,
> listw=WI_queenW)
Error in solve.default(inf, tol = tol.solve) :
system is computationally singular: reciprocal condition number = 1.51647e-13
> WI_queenW
Characteristics of weights list object:
Neighbour list object:
Number of regions: 71
Number of nonzero links: 364
Percentage nonzero weights: 7.22079
Average number of links: 5.126761
Weights style: B
Weights constants summary:
n nn S0 S1 S2
B 71 5041 364 728 8160
.............................
Milan
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