[R-sig-ME] Random slope does not improve hierarchical fitting across time

[xavier piulachs]

Dear members of mixed-models list,


I'm adressing you in order to ask a question about Hierarchical and ZI counts measured over time.
To have preliminar results, I'm modeling longitudinal data with a Negative Binomial GLMM, via
lme4 and glmmADBM packages (very similar results). I have considered two possibilities:


1) A single random intercept:
glmer.0.int.NB <- glmer.nb(counts ~ obstime + (1|id), data = tr.j)    # lme4 package

tr.j$ID <- as.factor(tr.j$id)
glmmADMB.0.int.NB <- glmmadmb(claimyr ~ obstime + (1|ID), data = tr.j, family = "nbinom")
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.9652     0.1222 -7.9005   0.0000
obstime       0.0238     0.0073  3.2735   0.0011


2) Random intercept and random slope effects:
glmer.0.slp.NB <- glmer.nb(counts ~ obstime + (obstime|id), data = tr.j)   #  lme4 package

glmmADMB.0.slp.NB <- glmmadmb(claimyr ~ obstime + (obstime|ID), data = tr.j, family = "nbinom")
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.9401     0.1190 -7.9005   0.0000
obstime       0.0230     0.0075  3.0540   0.0023


Surprisingly, the anova test indicates non significant improvement by fitting second model:

anova(glmer.0.int.NB, glmer.0.slp.NB)  # LRT: p-value = 0.2725 > 0.05
anova(glmmADMB.0.int.NB, glmmADMB.0.slp.NB) # LRT: p-value = 0.1042 > 0.05


As far as I know, when dealing repeated measurements across time, we expect that outcomes closer in time to be

more correlated (it is indeed a more realistic approach), so I'm totally disconcerted by this result.

Can anyone explain what could be the reason?


Best,

Xavier


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