[R] AIC in lmer

[vito muggeo]

Hi,
my reply just concerns the usage of AIC in mixed models and not the lmer 
package.

The "standard" AIC is actually unconditional.

Vaida and Blanchard (2003, Proceeding 19 IWSM,101-105) discuss that a 
"conditional" version should be more appropriate in a mixed framework.

I don't whether the paper has been pubblished elsewhere.

regards,
vito




Richard Chandler wrote:
> Hello all,
> 
> Is AIC calculated incorrectly in lmer? It appears as though it uses
> AIC = -2*logLik - 2*#parms, instead of -2*LogLik + 2*#parms? Below is
> output from one of many models I have tried:
> 
> Generalized linear mixed model fit using PQL 
> Formula: cswa ~ pcov.ess1k + (1 | year) 
>    Data: ptct50.5 
>  Family: poisson(log link)
>       AIC    BIC    logLik deviance
>  224.8466 219.19 -114.4233 228.8466
> Random effects:
>      Groups        Name    Variance    Std.Dev. 
>        year (Intercept)   0.0062643    0.079147 
> # of obs: 125, groups: year, 2
> 
> Estimated scale (compare to 1)  1.277183 
> 
> Fixed effects:
>               Estimate Std. Error  z value Pr(>|z|)
> (Intercept) -0.1059628  0.1283976 -0.82527   0.4092
> pcov.ess1k   0.0101182  0.0093962  1.07683   0.2816
> 
> 
> A snip of my data:
> 
>       cswa pcov.ess250 year
>   [1,]    4        7.14 2004
>   [2,]    4       19.26 2003
>   [3,]    1        3.66 2004
> 
> I'm using R 2.1.1 with Windows XP.
> 
> Thanks,
> Richard
> 

-- 
====================================
Vito M.R. Muggeo
Dip.to Sc Statist e Matem `Vianelli'
Università di Palermo
viale delle Scienze, edificio 13
90121 Palermo - ITALY
tel: 091 6626240
fax: 091 485726/485612