[R-sig-ME] lmer or glmer?

[Ken Beath]

Rather than using poisson you should use quasi poisson, as the data that
you have is not counts so you can't assume the fixed relationship between
mean and variance that Poisson requires. You should also look at the
residuals versus fitted values as these will indicate whether the
increasing variance with mean from a Poisson or quasi Poisson is correct.
The spread of the residuals should look fairly constant.


On 13 January 2015 at 06:41, Michael Jackson <Michael.Jackson at vuw.ac.nz>
wrote:

> Hi Thierry,
>
> Thanks for the input. Ive attached some links to qqnorm(resid(x)) plots
> run for varying data.
>
> Plot 1 (link below) (based on lmer code) is characteristic of all my plots
> when using my raw data, i.e., showing positive skew to a greater or lesser
> extent than this example. I identified the response variable as key to
> driving this and therefore tried log transforming my response variable.
>
> Plot 2 is the same raw data and lmer coding, but with a log transformed
> response variable. It now shows slight negative skew and if anything is
> worse.
>
> Plot 3 is from my glmer coding I proposed in my first message (the two
> former used the lmer coding) and also my raw untransformed response or
> predictors. This looks lots better. A SW test is also not significant
> (p=0.67). This is actually based on using "family=poisson(link=log)" as ive
> now read that the "family=" aspect only relates to the response, not
> predictors ...Id be interested in your thoughts.
>
>
> Plot 1
> http://s166.photobucket.com/user/michaeljackson1972/media/Plot1_zps1e8eb440.png.html
>
> Plot 2
> http://s166.photobucket.com/user/michaeljackson1972/media/Plot2_zpsa5e555b8.png.html
>
> Plot 3
> http://s166.photobucket.com/user/michaeljackson1972/media/Plot3_zpsf4a5b638.png.html
>
> Thanks again,
>
> Mike
>
>
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*Ken Beath*
Lecturer
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