[R-sig-ME] Posthoc for the glmmTMB package

[Ikponmwosa Egbon]

Hello All,

Please, I am a novice to 'glm with mixed effects (glmm)' and need the
guidance of mixed-model experts on how to conduct a posthoc test after
using the glmmTMB (http://www.biorxiv.org/content/biorxiv/early/
2017/05/01/132753.full.pdf) for a zero-inflated (Poisson) model for a
count data with repeated measures (over different times, hence time was
built in as a random effect).

Although I have run the model, I could not separate the different levels
(or treatments) within a factor (Genotypes) to know which is similar or
different, as often seen in the traditional ANOVAs or linear models,
wherein posthoc family-wise comparisons are usually conducted. Or perhaps
there are things I am not seeing with the novice spectacles.

*Please, see the script/output for statistical context below*:

> multiple<-read.delim("Multiplechoice.txt")
> str(multiple)
'data.frame': 1440 obs. of  3 variables:
 $ Genotypes: Factor w/ 8 levels "AR3","BR6","BR7",..: 2 2 2 2 2 2 2 2 2 2
...
 $ Time     : Factor w/ 18 levels "10m","15m","20m",..: 16 16 16 16 16 16
16 16 16 16 ...
 $ Insects  : int  2 0 0 0 1 0 0 0 0 0 ...
> head(multiple)
  Genotypes Time Insects
1       BR6   5m       2
2       BR6   5m       0
3       BR6   5m       0
4       BR6   5m       0
5       BR6   5m       1
6       BR6   5m       0
> library("glmmTMB")
> zipm0 <- glmmTMB(Insects~Genotypes + (1 | Time),
+                  zi = ~Genotypes,
+                  data = multiple, family = poisson)
> summary(zipm0)
 Family: poisson  ( log )
Formula:          Insects ~ Genotypes + (1 | Time)
Zero inflation:           ~Genotypes
Data: multiple

     AIC      BIC   logLik deviance df.resid
  2363.3   2453.0  -1164.7   2329.3     1423

Random effects:

Conditional model:
 Groups Name        Variance Std.Dev.
 Time   (Intercept) 0.7921   0.89
Number of obs: 1440, groups:  Time, 18

Conditional model:
                        Estimate Std. Error z value Pr(>|z|)
(Intercept)             -0.05931    0.23378  -0.254 0.799747
GenotypesBR6            -0.09546    0.13452  -0.710 0.477939
GenotypesBR7            -1.02963    0.19379  -5.313 1.08e-07 ***
GenotypesDR3            -0.34788    0.17393  -2.000 0.045491 *
GenotypesOut group      -0.21968    0.55045  -0.399 0.689827
GenotypesP. grandifolia -1.64415    0.40947  -4.015 5.94e-05 ***
GenotypesSA1            -0.57111    0.16067  -3.555 0.000378 ***
GenotypesVZ2            -0.43942    0.15825  -2.777 0.005492 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Zero-inflation model:
                        Estimate Std. Error z value Pr(>|z|)
(Intercept)              -0.7956     0.2453  -3.244  0.00118 **
GenotypesBR6             -0.7542     0.5047  -1.494  0.13509
GenotypesBR7             -2.5021     3.5578  -0.703  0.48189
GenotypesDR3              1.5065     0.3695   4.077 4.55e-05 ***
GenotypesOut group        2.8023     0.4860   5.766 8.14e-09 ***
GenotypesP. grandifolia   1.4009     0.6815   2.056  0.03983 *
GenotypesSA1             -0.5077     0.5662  -0.897  0.36987
GenotypesVZ2             -0.3105     0.4580  -0.678  0.49779
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


I look forward to having some feedbacks, and any other assistance that is
deemed necessary would be highly appreciated. Thank you for your time and
your assistance.


Kind
regards,


Ik.

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