clustered data with glmer() and glmmPQL()
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
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a master student from
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Greece. I?m trying to model count data with
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GLMM (lme4
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package), using as discrete response variable the number of parasites per
fish and as categorical predictor variable three
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different species.
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I'm using as random effect the three different tanks I used and as fixed
the infection level
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.
?
?
This is the model I'm running:
mod
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<-
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glmer
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(parasite~species+(1|tank),
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family=poisson
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, 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
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to the Tukey test. Is it legal to use
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TukeyHSD(aov(parasite~species, data=mydata))
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?
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Finally I tested the assumptions
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and
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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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