[R-sig-ME] GLMM -Firefly Flash

[Thierry Onkelinx]

Dear Vickly,

Please keep the mailing list in cc.

The idea is that you need a sufficient number of observations per
parameter. 10 to 20 is often used as a rule of thumb. If you have a lower
number, the model is too complex given the data will probably overfit.
Think about a simple linear model (intercept + 1 parameter for slope).
Although you can technically fit this model when you have 2 or 3
observations, the resulting model is not very useful.

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

2016-11-23 10:37 GMT+01:00 Vickly Mobilim <vickmoe7 op gmail.com>:

> Hi Thierry,
>
> Thank you for the kind reply! That is very helpful.
>
> May I know more about the calculation? I have never seen it. How do you
> use it to know if it is sufficient to build a model?
>
> On Nov 23, 2016 5:27 PM, "Thierry Onkelinx" <thierry.onkelinx op inbo.be>
> wrote:
>
> Dear Vickly,
>
> I assume you have measurements on the individual animals and you can
> identify the animal during the different exposures. I think you want a
> model like this: flash_rate ~ treatment * exposure + temperature + humidity
> + size_ratio + (1|animal_id) This requires -1 + 4 * 3 + 1 + 1 + 1 + 1 = 15
> parameters. You have 78 * 3 = 234 observations. That is 234 / 15 = 15.6
> observations per parameter, which reasonable to fit the model.
>
> 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
>
> 2016-11-22 18:01 GMT+01:00 Vickly Mobilim <vickmoe7 op gmail.com>:
>
>> Greetings,
>>
>> I've read several writing of yours about GLMM and I thought it would be
>> the
>> best tool to answer my research questions. However, I wasn't sure if I
>> really need it and my data permit me to use it. That said, I have 78
>> individuals of firefly divided into four groups (A= 20 indv., B = 20
>> indv.,
>> C = 20 indv. and D = 18 indv.). This is due to several limitations that I
>> can't take more samples of firefly. I will explain the details of the
>> experiment below.
>>
>> I'm hoping that you can advise me on this issue, whether you have seen
>> such
>> cases of low sample size using GLMM or whether GLMM is not suitable for my
>> study.
>>
>>
>>
>> I expose the fireflies with several intensity of white light according to
>> their group (Group A = 0.05lux, B = 0.1lux, C = 0.3lux and D = 0.5lux)
>> then
>> measure their flash rates and duration before, during and after exposure
>> to
>> light (repeated measure design). Temperature, humidity and individual
>> eye-to-body size ratio were also measured. My main aim was to measure the
>> impact of several light pollution intensity to their flash rates and
>> duration and taking temperature, humidity and eye-to-body size into
>> account.
>>
>> I realized that calculating changes in their flash rates and duration are
>> achievable by subtracting post-experiment result with pre-experiment
>> result
>> then use unpaired t-test to compare the results. However, my data was not
>> normal and I used Mann-Whitney U test instead. But this does not take
>> temperature, humidity and eye-to-body size into account. As I was looking
>> into the possibility of taking them into account, I found several
>> modelling
>> technique that is suitable including GLMM but I am not sure if I can
>> employ
>> them because according to a statistician I am in consult with, the sample
>> size is too small to be developed into a model that it would invite more
>> problem in analysis.
>>
>> --
>> Regards,
>> Vickly Mobilim
>>
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>>
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>
>
>

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