AIC in lmer

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

--
Richard Chandler
Department of Natural Resources Conservation
UMass Amherst
(413)545-1237

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