Help for spdep package
On Tue, 18 Dec 2012, Saman Monfared wrote:
Dear all. I try to fit conditional auto regressive models (CAR and SAR) in package spdep. Also, I have fitted some other models like GLM, Empirical Bayes and ... My program to CAR and SAR is: esar1f1 <- spautolm(IMR.m~ 0+PCFP+Sup+B.F,data =data1, listw=nb2listw(nb6, style="W"), family="SAR", method="full", verbose=TRUE) summary(esar1f1) esar1f2 <- spautolm(IMR.m~ 0+PCFP+Sup+B.F,data =data1, listw=nb2listw(nb6, style="W"), family="CAR", method="full", verbose=TRUE)
esar1f1
Call:
spautolm(formula = IMR.m ~ 0 + PCFP + Sup + B.F, data = data1,
listw = nb2listw(nb6, style = "W"), family = "SAR", method = "full",
verbose = TRUE)
Coefficients:
PCFP Sup B.F lambda
0.1711382 -0.2262700 0.2414336 -1.7946991
Log likelihood: -56.24405
What is lambda?? I couldn't find a good refrences to understand tese model!!!
See the help page (lambda is said to be the ML autoregressive coefficient about half way down), and certainly read Waller & Gotway (it may be that they term the spatial coefficient rho, but lambda is used here for consistency with errorsarlm()).
How can I predict this model for new data??
There are no predict methods provided - but analysis of the model will
show that \hat{y} is X \beta, isn't it?
How can I cross validate results of CAR and SAR??
They are not nested processes, but as Clif & Ord, Ripley and the others show, a CAR and a SAR may be expressed in terms of one-another. By the way, your lambda may me maxing out at the bottom of its range, so be careful, very strong negative autocorrelation is very unlikely. Roger
Roger Bivand Department of Economics, NHH Norwegian School of Economics, 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