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




~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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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