[R-sig-ME] clustered data with glmer() and glmmPQL()

[Thierry Onkelinx]

Dear Maria,

The assumption of normality is only required for the residuals of linear
(mixed) models, not for the residuals of generalised linear (mixed) models.

You can't use aov() for two reasons: it assumes a Gaussian distribution and
it assumes independent observations.

mod1 and mod2 are in principle the same model (but fitted differently).
Both assume the same correlation structure.

3 levels is not enough to get a sensible variance estimate for a random
effect. See glmm wiki faq for more details.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2015-06-15 15:06 GMT+02:00 Marsela Alvanopoulou <marselalv op gmail.com>:

> Hello,
>
> I'm
> ​ ​
> a master student from
> ​ ​
> Greece. I´m trying to model count data with
> ​ ​
> GLMM (lme4
> ​ ​
> package), using as discrete response variable the number of parasites per
> fish and as categorical predictor variable three
> ​ ​
> different species.
> ​ ​
> I'm using as random effect the three different tanks I used and as fixed
> the infection level
> ​​
> .
> ​
> ​
> This is the model I'm running:
>
> mod
> ​ ​
> <-
> ​ ​
> glmer
> ​ ​
> (parasite~species+(1|tank),
> ​ ​
> family=poisson
> ​​
> , data=mydata)
>
> I noticed that the estimate of the intercept does not give the mean of the
> first species, so I ran a simple glm model to get the estimate. With
> summary() I got the p values that allow me to reject my hypothesis and
> continue
> ​ ​
> to the Tukey test. Is it legal to use
> ​
> TukeyHSD(aov(parasite~species, data=mydata))
> ​ ?
> ​
> ​
> Finally I tested the assumptions
> ​ ​
> and
> ​ ​
> I found violation of normality and independence.
>
> I also tried MASS package where the assumption of independent residuals was
> not violated anymore but the histogram gave me a much more skewed
> distribution, but also anova() is not available for QTLs.
>
> mod2 <- glmmPQL (parasite~species, random=~1|tank, family=poisson,
> data=mydata)
>
> Thank you in advance for your help.
>
> Maria
>
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