Posthoc for the glmmTMB package

Tue, Jul 18, 2017 7:49 PM

I think the easiest way to do this is with lsmeans

https://stats.stackexchange.com/questions/145765/post-hoc-testing-in-multcompglht-for-mixed-effects-models-lme4-with-interact

  You *should* be able to use lsmeans with glmmTMB objects after running
 source(system.file("other_methods","lsmeans_methods.R",package="glmmTMB"))

  I believe this is all operating/making comparisons based on the
conditional model, not the zero-inflation model ...

On 17-07-18 10:08 AM, Ikponmwosa Egbon wrote:

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.

Posthoc for the glmmTMB package

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