[R-sig-ME] How to analyse data with two replicates per group

Thierry Onkelinx th|erry@onke||nx @end|ng |rom |nbo@be
Fri Jan 25 09:28:03 CET 2019

Dear Simon,

IMHO you should use individual as a random effect. If a systematic
difference between both eyes are relevant / plausible, then add a variable
coding for left or right to the fixed effect. Otherwise, don't include it
in the model.

Create a subset of the data which has no missing values in all variables
used in your full model. Fit all your models on this subset. Then you can
compare AIC.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

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


Op do 24 jan. 2019 om 17:30 schreef Simon POTIER <sim.potier using gmail.com>:

> Dear all,
> Thanks in advance for your help.
> I will try to be precise, do not hesitate to tell me if you need more
> details.
> I have a dataset measuring some features of raptors eyes (let says here eye
> size for exemple). I have measured both eyes (left and right eyes) of 47
> individuals.
> If I want to compare eye size with age and sex, what type of analysis
> should I use:
> 1) Should I analyse both eye separately (which can be interesting in term
> of vision)?
> 2) Can/should I analyse both eye together including Individual as a random
> effect? In other terms, I was wondering whether including individuals as
> random effect (with only two levels for each individual (left and right
> eye)) is possible?
> One more question, I want to compare the AIC of my models, but I get this
> error message:
> Warning message:
> In AIC.default(mm.edge, mm.edge2, mm.edge3) :
>   models are not all fitted to the same number of observations
> I understand that the problem come from the fact that I have some NA in
> some fixed effect. How can I deal with this please because it is not
> possible to compare AIC?
> Best regards,
> Simon Potier
> --
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> Simon Potier
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