# How to use quasi-Poisson model after overdisperson with glmer(mydata,family = poisson(link = 'log'))?

I have to fit my data with Laplace glmm with random effect using poisson distribution error.

m1 =  glmer(y ~  x +  SIDI +  z.fac +
(1|SITE) + (1|DATE), family = poisson(link = 'log'), data=dat)


But when I use an overdispersion.test, it indicates overdispersion with p-value < 0.05

And then, a paper told me I should proceed quasi-possion refit, however:

    m1b =  glmer(y~  x +  SIDI +  z.fac +
(1|SITE) + (1|DATE), family = quasipoisson(link = 'log'), data=dat)


It told me that quasi+ distribution shouldn't be applying glmer(). And I know I can use it in glm(family), but my data needs to consider random effect. Who can tell me what should I do when proceeding quasipoisson refit model (don't forget the random effects). By the way , glmer( offset(log(x)) ) and glmer( log(x) ), between them, have any difference? what's the offset() meaning? I can't comprehend what the document tells with help(offset).

 temp <- structure(list(DATE=structure(c(17684,17684,17684,17684,
17684,17684,17684,17684,17684,17684,17684,17684,17684,
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17705,17705,17705,17705,17705,17706,17706,17706,17706,
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18067,18067,18067,18067,18067,18067,18067,18067,18068,
18068,18068,18068,18068,18068,18068,18068,18068,18073,
18073,18073,18073,18073,18073,18073,18073,18073,18073,
18073,18073,18073,18073,18073,18073,18074,18074,18074,
18074,18074,18074,18074,18074,18074,18074,18074,18074,
18074,18074,18074,18074,18074,18074,18075,18075,18075,
18075,18075,18075,18075,18075,18075,18075,18075,18075,
18075,18075,18075,18075,18075,18075),class="Date"),SITE=structure(c(1L,
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16L,16L,16L,17L,17L,17L,18L,18L,18L,19L,19L,19L,20L,
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17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,
17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,17L,
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24L,24L,36L,36L,36L,37L,37L,37L,38L,38L,38L,25L,25L,
25L,26L,26L,26L,27L,27L,27L,28L,28L,28L,29L,29L,29L,
30L,30L,30L),.Label=c("CB1","CB2","CB3","CB4","CB5",
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• Have a look here bbolker.github.io/mixedmodels-misc/glmmFAQ.html#overdispersion There you can find that the glmmTMB package provides support for a “quasi-Poisson” parameterization. – Stefan Mar 8 at 4:48
• thank you very much . I will look it . – PeterPanPan Mar 8 at 4:52
• but I still have a question. First I used the lme4::glmer() with poisson family. If I turn to glmmTAB. Shall I give up the lme4::glmer. I mean that in all proceeds I only use glmmTMB to fit with Poisson and if overdisperson, I refit model with glmmTAB's quasi-Possion? – PeterPanPan Mar 8 at 5:28
• Can you tell ue which overdispersion test you used, and show its output? And, for your offset Q: stats.stackexchange.com/questions/100772/… – kjetil b halvorsen Mar 8 at 12:29
• For overdispersion, instead of going "quasi", I'd try observation-level random effects (stats.stackexchange.com/questions/351352/…), i.e. add +(1|ID), where ID is just a unique character for each row in your data. – Carsten Mar 8 at 14:44