1
$\begingroup$

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).

It would be appreciate for your answers. It's my dataset below:

 temp <- structure(list(DATE=structure(c(17684,17684,17684,17684,
17684,17684,17684,17684,17684,17684,17684,17684,17684,
17684,17684,17686,17686,17686,17686,17686,17686,17686,
17686,17686,17686,17686,17686,17687,17687,17687,17687,
17687,17687,17687,17687,17687,17688,17688,17688,17688,
17688,17688,17688,17688,17688,17688,17688,17688,17689,
17689,17689,17689,17689,17689,17689,17689,17689,17689,
17689,17689,17690,17690,17690,17690,17690,17690,17690,
17690,17690,17690,17690,17690,17690,17690,17690,17691,
17691,17691,17691,17691,17691,17691,17691,17691,17691,
17691,17691,17692,17692,17692,17692,17692,17692,17692,
17692,17692,17693,17693,17693,17693,17693,17693,17693,
17693,17693,17693,17693,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17694,17694,17694,17694,17694,17694,17694,17694,
17694,17695,17695,17695,17695,17695,17695,17695,17695,
17695,17695,17695,17695,17695,17695,17695,17696,17696,
17696,17696,17696,17696,17696,17696,17696,17696,17696,
17696,17697,17697,17697,17697,17697,17697,17697,17697,
17697,17697,17697,17697,17697,17698,17698,17698,17698,
17698,17698,17699,17699,17699,17699,17699,17699,17699,
17699,17699,17702,17702,17702,17702,17702,17702,17702,
17702,17702,17702,17702,17702,17702,17702,17702,17703,
17703,17703,17703,17703,17703,17703,17703,17703,17703,
17703,17703,17704,17704,17704,17704,17704,17704,17704,
17704,17704,17705,17705,17705,17705,17705,17705,17705,
17705,17705,17705,17705,17705,17706,17706,17706,17706,
17706,17706,17706,17706,17706,17706,17706,17706,17709,
17709,17709,17709,17709,17709,17709,17709,17709,17709,
17709,17709,17710,17710,17710,17710,17710,17710,17710,
17710,17710,17710,17711,17711,17711,17711,17711,17711,
17711,17712,17712,17712,17712,17712,17712,17712,17712,
17712,17712,17713,17713,17713,17713,17713,17713,17713,
17713,17713,17713,17713,17713,18053,18053,18053,18053,
18053,18053,18053,18053,18053,18053,18053,18053,18053,
18053,18053,18053,18053,18053,18053,18059,18059,18059,
18059,18059,18059,18060,18060,18060,18060,18060,18060,
18061,18061,18061,18061,18061,18061,18061,18061,18061,
18061,18061,18061,18061,18061,18061,18061,18061,18061,
18066,18066,18066,18066,18066,18066,18066,18066,18066,
18066,18066,18066,18066,18067,18067,18067,18067,18067,
18067,18067,18067,18067,18067,18067,18067,18067,18067,
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,
1L,1L,2L,2L,2L,3L,3L,3L,4L,4L,4L,5L,5L,5L,6L,6L,
6L,7L,7L,7L,8L,8L,8L,9L,9L,9L,10L,10L,10L,11L,11L,
11L,12L,12L,12L,13L,13L,13L,14L,14L,14L,15L,15L,15L,
16L,16L,16L,17L,17L,17L,18L,18L,18L,19L,19L,19L,20L,
20L,20L,1L,1L,1L,2L,2L,2L,3L,3L,3L,4L,4L,4L,5L,
5L,5L,6L,6L,6L,7L,7L,7L,8L,8L,8L,9L,9L,9L,10L,
10L,10L,11L,11L,11L,12L,12L,12L,13L,13L,13L,14L,14L,
14L,15L,15L,16L,16L,16L,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,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,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,18L,18L,18L,19L,19L,19L,20L,
20L,20L,1L,1L,1L,2L,2L,2L,3L,3L,3L,4L,4L,4L,5L,
5L,5L,6L,6L,6L,7L,7L,7L,8L,8L,8L,9L,9L,9L,10L,
10L,10L,11L,11L,11L,12L,14L,14L,14L,15L,15L,15L,13L,
13L,13L,16L,16L,16L,18L,18L,18L,19L,19L,19L,20L,20L,
20L,1L,1L,1L,2L,2L,2L,3L,3L,3L,4L,4L,4L,5L,5L,
5L,6L,6L,6L,7L,7L,7L,8L,8L,8L,9L,9L,9L,10L,10L,
10L,11L,11L,11L,12L,12L,12L,13L,13L,13L,14L,14L,14L,
15L,15L,15L,16L,16L,16L,17L,17L,17L,18L,18L,18L,19L,
19L,19L,20L,20L,20L,1L,1L,1L,2L,2L,2L,3L,3L,3L,
4L,5L,5L,6L,6L,6L,7L,8L,8L,8L,9L,9L,9L,10L,11L,
11L,11L,12L,12L,12L,13L,13L,13L,14L,15L,15L,15L,16L,
16L,16L,17L,17L,17L,18L,18L,18L,19L,19L,19L,20L,20L,
20L,21L,22L,23L,24L,25L,26L,27L,28L,29L,30L,31L,31L,
32L,33L,34L,35L,36L,37L,38L,21L,31L,32L,33L,34L,35L,
22L,23L,24L,36L,37L,38L,25L,25L,25L,26L,26L,26L,27L,
27L,27L,28L,28L,28L,29L,29L,29L,30L,30L,30L,21L,21L,
21L,31L,33L,33L,33L,34L,34L,34L,35L,35L,35L,22L,22L,
22L,23L,23L,23L,24L,24L,24L,29L,29L,29L,32L,36L,36L,
36L,37L,37L,37L,38L,38L,38L,25L,26L,27L,27L,27L,28L,
30L,30L,30L,21L,21L,21L,31L,31L,31L,32L,32L,32L,33L,
33L,33L,34L,35L,35L,35L,22L,22L,22L,23L,23L,23L,24L,
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",
"CB6","CB7","CB8","CB9","CB10","CB11","CB12","CB13","CB14",
"CB15","CB16","CB17","CB18","CB19","CB20","CC1","CC10",
"CC11","CC12","CC13","CC14","CC15","CC16","CC17","CC18",
"CC2","CC3","CC4","CC5","CC6","CC7","CC8","CC9","cc1",
"cc10","cc11","cc12","cc13","cc14","cc15","cc16","cc17",
"cc18","cc2","cc3","cc4","cc5","cc6","cc7","cc8","cc9"
),class="factor"),y=c(0L,0L,8L,0L,16L,20L,0L,8L,
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8L,0L,24L,4L,0L,4L,0L,4L,0L,0L,0L,88L,4L,0L,12L,
0L,4L,0L,0L,0L,0L,0L,8L,0L,12L,0L,0L,0L,0L,0L,
4L,0L,0L,0L,12L,12L,12L,0L,16L,32L,48L,16L,36L,0L,
0L,4L,0L,48L,0L,0L,0L,0L,0L,0L,8L,8L,12L,16L,16L,
8L,0L,52L,56L,36L,4L,4L,8L,0L,0L,0L,4L,12L,4L,16L,
4L,8L,4L,0L,0L,0L,0L,8L,8L,24L,0L,0L,0L,0L,0L,
0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,4L,0L,0L,
0L,0L,0L,0L,0L,0L,0L,0L,0L,4L,0L,0L,0L,0L,0L,0L,
0L,0L,0L,0L,0L,0L,0L,0L,4L,0L,4L,0L,0L,0L,4L,4L,
0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,
0L,0L,0L,0L,0L,0L,20L,32L,4L,24L,0L,0L,0L,0L,0L,
8L,8L,32L,20L,52L,36L,72L,52L,52L,12L,4L,16L,0L,
0L,4L,4L,0L,0L,20L,8L,0L,16L,32L,32L,0L,12L,20L,
24L,8L,40L,92L,20L,28L,8L,32L,92L,0L,4L,4L,0L,8L,
20L,4L,20L,4L,12L,28L,24L,8L,16L,36L,84L,8L,0L,0L,
16L,44L,48L,4L,0L,0L,20L,24L,12L,40L,136L,188L,0L,
24L,44L,12L,32L,40L,0L,8L,32L,24L,16L,8L,12L,16L,
12L,12L,12L,24L,16L,80L,68L,20L,12L,44L,28L,36L,12L,
56L,168L,48L,20L,40L,64L,4L,0L,0L,20L,4L,28L,8L,
4L,12L,76L,60L,48L,12L,4L,0L,4L,20L,28L,4L,4L,8L,
20L,56L,28L,392L,328L,476L,60L,4L,0L,116L,72L,84L,
8L,260L,284L,140L,60L,184L,4L,124L,108L,48L,48L,288L,
608L,528L,148L,136L,40L,0L,84L,120L,16L,92L,116L,76L,
32L,8L,4L,28L,28L,24L,0L,12L,4L,40L,20L,92L,0L,0L,
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4L,0L,8L,4L,4L,0L,0L,32L,68L,28L,0L,0L,0L,80L,0L,
4L,12L,4L,8L,12L,48L,0L,24L,40L,88L),x=c(13168L,
2168L,1240L,1916L,3876L,15732L,1056L,1984L,1548L,1184L,
1676L,2212L,288L,556L,416L,14800L,4436L,6360L,808L,1348L,
3216L,988L,1180L,948L,10152L,32172L,5356L,1704L,5612L,
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3724L,3204L,2120L,51932L,65564L,93480L,69792L,67688L,
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Improve this question
$\endgroup$
7
  • 1
    $\begingroup$ 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. $\endgroup$
    – Stefan
    Commented Mar 8, 2020 at 4:48
  • 1
    $\begingroup$ thank you very much . I will look it . $\endgroup$
    – PeterPanPan
    Commented Mar 8, 2020 at 4:52
  • 1
    $\begingroup$ 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? $\endgroup$
    – PeterPanPan
    Commented Mar 8, 2020 at 5:28
  • $\begingroup$ Can you tell ue which overdispersion test you used, and show its output? And, for your offset Q: stats.stackexchange.com/questions/100772/… $\endgroup$
    – kjetil b halvorsen ♦
    Commented Mar 8, 2020 at 12:29
  • $\begingroup$ 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. $\endgroup$
    – Carsten
    Commented Mar 8, 2020 at 14:44

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