On Tue, 29 Jul 2003, Volker Bahn wrote:
Dear list members,
I posted the question below a few weeks ago on this list and didn't receive
any answers (but an email from one other person showing interest in an
answer). I was wondering whether this was because of summertime or my poor
phrasing of the question or did I really land somewhere outside the area of
expertise of this enlightened and helpful group of people? If the latter was
true, could you maybe suggest colleagues who might be able to help?
Thank you.
Volker Bahn
Old question:
I'm running spatial linear models (slm) of the CAR family in Splus 6.1 on a
7.2 RedHat Linux machine (but the same effect described below is true for my
Splus 6.1 Windows Version running on a XP machine).
I want to compare the slm model to a similar lm model by AIC and use the
log-likelihood to calculate the AIC. However, the loglik given for the slm
model by Splus does not seem to be correct to me. Two observations lead me
to this conclusion:
1) If I calculate loglik myself based on the residual standard error (RSE)
with a formula from Burnham and Anderson (2002) I get a value completely
different from Splus:
Loglik = (-n / 2) * LN(RSE^2 * df / n) - (n / 2) * LN(2*PI()) - (n / 2)
With
RSE = residual standard error (in output)
df = degrees of freedom on the RSE
n = sample size
That's not the right formula for a spatial linear model: where in B&A
(2002) are such models discussed (I could not find them)?
Log-likelihoods and hence AICs are only defined up to an additive constant
(which depends on the dominating measure used). Are you sure you are
using comparable ones?
[...]