Re: [R] coxme AIC score and p-value mismatch??

The AICs do not seem right to me either. Unless I am missing something, it appears that the formula: AIC= -2x logLik -2k is being applied, rather than: AIC= -2x logLik +2k Meaning models with fewer degrees of freedom are being penalised. So in your example I make the degrees of freedom 9.61

Re: [R] coxme AIC score and p-value mismatch??

I think I've figured it out, the AIC column is the IMPROVEMENT in AIC compared to the null model. So bigger values are better. -- View this message in context: http://r.789695.n4.nabble.com/coxme-AIC-score-and-p-value-mismatch-tp2333980p2550409.html Sent from the R help mailing list archive at

Re: [R] coxme AIC score and p-value mismatch??

Indeed! That is what was confusing me too. Glad you figured it out. -Teresa On Wed, Sep 22, 2010 at 7:31 AM, whoppitt [via R] ml-node+2550409-1571116512-138...@n4.nabble.comml-node%2b2550409-1571116512-138...@n4.nabble.com wrote: I think I've figured it out, the AIC column is the IMPROVEMENT

Re: [R] coxme AIC score and p-value mismatch??

You noticed that the AIC increases while the p-value decreases as you change the model and hold the data fixed. There would be more indication of a problem if you instead noticed the same relationship between the AIC and the p-value as you changed the data and held the model fixed. David

[R] coxme AIC score and p-value mismatch??

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the