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-mod...@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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
