On Mon, 22 Nov 2010, elaine kuo wrote:
Dear List,
I am comparing the squared R values of linear models and its spatial
autoregressive counterparts. (SARerror)
(1. lm (Y~X1)
2. lm (Y~ X1+X2)
3. lm(Y~X1+X2+X3))
The squared R values of linear models are generated by command summary
(lm).
Similarly, I tried to produce those of spatial autoregressive models
based on the squared Pearson?s correlation of explanatory and response
variables. It failed
Don't. There is no direct equivalent to the OLS R-squared, these models
are fitted by maximum likelihood. You may choose to compare
likelihood-based measures, so a likelihood ratio test (as reported in the
output of the summary method for the fitted model) between the fitted
model and an OLS model with the spatial coefficient fixed at zero is OK.
If you want something like an R-squared, try the Nagelkerke R-bar-squared,
based on the likelihood, reported optionally in the summary object - see
?summary.sarlm. You should then compare this with a Nagelkerke value for
your OLS model if you feel that this would be helpful.
Roger
The code is as followed.
Please kindly modify the code and thank you.
1. single predictor
sar.x1 <-errorsarlm(Y~X1,data=datam.std,listw=nb8.w, na.action=na.omit,
method="Matrix", zero.policy=TRUE)
summary(sar.x1)
cor(sar.x1$X1, sar.x1$Y, method = "pearson")
error message
error in cor(sar.x1$ X1, sar.x1$Y, method = "pearson") :
supply both 'x' and 'y' or a matrix-like 'x'
2. multiple predictors
sar.all <-errorsarlm(Y~X1+X2+X3,data=datam.std,listw=nb8.w,
na.action=na.omit, method="Matrix", zero.policy=TRUE)
summary(sar.all)
cor(sar.all$X1+ sar.all$X2+ sar.all$X3, sar.x1$Y, method = "pearson")
error message
error in cor(sar.all$X1+ sar.all$X2+ sar.all$X3, sar.x1$Y, method =
"pearson") :
supply both 'x' and 'y' or a matrix-like 'x'
Elaine
[[alternative HTML version deleted]]
--
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: [email protected]
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