[R-sig-ME] Mixed model for compositional data

Thierry Onkelinx thierry.onkelinx at inbo.be
Tue Nov 8 09:31:18 CET 2016

Dear Stéphanie,

A workaround would be to model the numbers per item and use the (log of)
the total number per night as an offset. Then the model parameters are
essentially ratio's.

no effects of season and moonlite: number ~ offset(log(total)) + item +
only season: number ~ offset(log(total)) + item*season + (1|animal)
season:moonlight: number ~ offset(log(total)) + item*season*moonlight +

The model fit will not be guaranteed to sum to 1 per night and animal.

Best regards,


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

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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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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

2016-11-08 8:40 GMT+01:00 Stéphanie Périquet <stephanie.periquet op gmail.com>:

> Hi all,
> I have repeated measures of individual diet composition (I actually have
> the number of each item eaten and the duration of each observation) and
> I would like to model the effect of variables such as season and
> moonlight on diet composition (not the actual number of item eaten).
> I gathered that I should use compositional but the packed 'compostions'
> doesn't allow the inclusion of random factors.
> Does anyone has experience whit such data and analyses and point me in
> the right direction?
> Thanks a lot in advance for your replies!
> Regards,
> Stéphanie
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