kenny xu <kennyxu1983 <at> hotmail.com> writes:
>
> Dear All
>
> I have fitted the following glmm:
>
> cmai ~ time.f * intrv.f + (1 | nhome.f/Res_Code.f)
>
> with poisson distribution, using both glmer and glmmadmb.
>
> But the estimation for the fixed and random effects were different, i.e.
This is a surprising set of differences. I'm going to suggest
you send follow-ups to r-sig-mixed-models at r-project.org, which is
specialized for mixed models
> > summary(lmer.AGGREG.cmai.out3)
>
> Call:
> glmmadmb(formula = cmai ~ time.f * intrv.f + (1 | nhome.f/Res_Code.f),
> data = beam.AGGREG.cmai.long, family = "poisson", link =
"log",
> zeroInflation = F, admb.opts = admbControl(impSamp = 0, run = F),
> save.dir = "tmp")
Is there a particular reason you're using 'run=FALSE'? This
specification
will tell glmmADMB not to run the model, but to collect the results of
a previous run from the working directory -- not necessarily wrong,
but very easy to make a mistake this way and pick up the results
from a model run with a *different* specification (which might???
be what happened here) (Also, just as a matter of practice, it's
strongly advised to use FALSE instead of F, just in case someone
decided to assign a value to 'F' ...)
> AIC: 1032.2
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 0.542 3.105 0.17 0.86
> time.f2 0.104 5.177 0.02 0.98
> time.f3 -0.526 3.230 -0.16 0.87
> intrv.f1 0.929 2.712 0.34 0.73
> time.f2:intrv.f1 -0.416 5.302 -0.08 0.94
> time.f3:intrv.f1 0.177 3.261 0.05 0.96
>
> Number of observations: total=1032, nhome.f=35, nhome.f:Res_Code.f=344
> Random effect variance(s):
> Group=nhome.f
> Variance StdDev
> (Intercept) 0.7118 0.8437
> Group=nhome.f:Res_Code.f
> Variance StdDev
> (Intercept) 1.454 1.206
> Log-likelihood: -508.108
>
> > summary(lmer.AGGREG.cmai.out2)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: cmai ~ time.f * intrv.f + (1 | nhome.f/Res_Code.f)
> Data: beam.AGGREG.cmai.long
> AIC BIC logLik deviance
> 1835 1874 -909.5 1819
> Random effects:
> Groups Name Variance Std.Dev.
> Res_Code.f:nhome.f (Intercept) 0.040125 0.20031
> nhome.f (Intercept) 0.033702 0.18358
> Number of obs: 1032, groups: Res_Code.f:nhome.f, 344; nhome.f, 35
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 3.62040 0.04749 76.23 <2e-16 ***
> time.f2 -0.01964 0.01706 -1.15 0.2496
> time.f3 0.01643 0.01691 0.97 0.3310
> intrv.f1 0.07540 0.06819 1.11 0.2689
> time.f2:intrv.f1 0.02148 0.02395 0.90 0.3698
> time.f3:intrv.f1 -0.04835 0.02394 -2.02 0.0435 *
Otherwise I'm stumped. The numbers of observations etc. etc.
seem consistent. It's hard to compare AIC/log-likelihood between
glmmADMB and glmer because (at present) they use different
additive offsets ...
You could send me the data if it's not too sensitive.
Ben Bolker