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

[Marsela Alvanopoulou]

Hi again,

Thank you very much for your response. I found most of the answers in glmm
wiki faq. I used MASS::glmmPQL for the model, car::Anova and multcomp::glht
for the hypothesis testing and I still need some work to check the effect
of the tank, if any.

I also want to check if there is a significant difference on the number of
parasites per gram (continuous response variable). I multiplied all values
by 100 to get a discrete variable like before. Does that affect the final
conclusions?

Thanks again!

On Mon, Jun 15, 2015 at 4:34 PM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:

> 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 at 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
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>


-- 
Marsela Alvanopoulou
MSc Student, Biology Dept.,
University of Bergen, Norway

*e-mail: Marsela.Alvanopoulou at student.uib.no
<Marsela.Alvanopoulou at student.uib.no>*
*e-mail: marsela.alvanopoulou at imr.no <marsela.alvanopoulou at imr.no>*
*linkedin: gr.linkedin.com/pub/marsela-alvanopoulou/69/3b3/410/
<http://gr.linkedin.com/pub/marsela-alvanopoulou/69/3b3/410/>*

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