Dear List,
I am running generalized linear models considering spatial autocorrelation.
(Moran’ s I = 0.52)
(sample size 4873, explanatory variable number: 6)
After trying SAR and CAR in package spdep, the results are as followed.
I would like to learn which model was better fit.
However, the measures based on log likelihood and AIC imply different
contradictions.
1. log likehood
SAR is better than CAR (4919,629 > 3694.246)
2. AIC
CAR is better than SAR (-7370.5 > -9821.3)
Please kindly instruct which criterion I should follow, and advice on any
other measure will be highly appreciated.
Elaine
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SAR
Lambda: 0.999 LR test value: 13618 p-value: < 2.22e-16
Log likelihood: 4919.629
ML residual variance (sigma squared): 0.0075669, (sigma: 0.086988)
Number of observations: 4873
Number of parameters estimated: 9
AIC: -9821.3
Nagelkerke pseudo-R-squared: 1
CAR
Lambda: 0.999 LR test value: 11167 p-value: < 2.22e-16
Log likelihood: 3694.246
ML residual variance (sigma squared): 0.012682, (sigma: 0.11261)
Number of observations: 4873
Number of parameters estimated: 9
AIC: -7370.5
Nagelkerke pseudo-R-squared: 1
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