[R-sig-ME] Advice on GLMM with verIdent

[Thierry Onkelinx]

Hi Diego,

poly(Period, 2) | Phylogeny with fit a different parabola for each level of
Pylogeny. It allows the same fit as poly(Period, 2):Phylogeny The main
difference is that the parameters are smaller due to shrinkage.

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

2016-12-20 22:34 GMT+01:00 Diego Pavon <diego.pavonjordan op gmail.com>:

> Hello Thierry
>
> Thanks for your answer!
>
> I am interested in the interaction between period and phylogeny, to see
> whether the trends differ between groups. If I understood correctly, I
> shouldn't put something that I am interested in in the random part, am I
> right?
>
> Best
>
> Di
>
>
> 2016-12-20 22:03 GMT+01:00 Thierry Onkelinx <thierry.onkelinx op inbo.be>:
>
>> Dear Diego,
>>
>> The linear trend is required otherwise the optimum of the parabola is
>> fixed a x = 0.
>>
>> You could try to fit the phylogeny effect as a random effect instead of a
>> fixed effect. poly(Period, 2) | Phyloge ny and add species as a nested
>> random intercept.
>>
>> If you have the individual latitudes you can try to use those instead of
>> using only their average.
>>
>> 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
>>
>> 2016-12-20 17:49 GMT+01:00 Diego Pavon <diego.pavonjordan op gmail.com>:
>>
>>> Dear all,
>>>
>>> I write you because I need some advice about the model I want to fit to
>>> my
>>> data, which I suspect it is not too 'correct'...
>>>
>>> My response variable is continuous (mean weighted latitude) of 24
>>> species.
>>> I have these mean latitudes calculated for 4 periods (95-99, 00-04,
>>> 05-09,
>>> 10-13), for each species. Therefore I have 96 observations. I want to see
>>> if there is a trend of the mean latitude over time (indicating a shift in
>>> the population... all the species together). In this case, I would use
>>> Period as a continuous varaible and not a factor. I want to fit a GLMM
>>> with
>>> random = SpeciesID. However, data exploration revealed a quadratic
>>> relationship between my response (mean latitude) and Period (my
>>> continuous
>>> covariate for time). But also, I am interested also to see whether there
>>> are differences between functional groups (some species may move faster
>>> than others). Thus I include the variable Phylogeny, which is a factor
>>> with
>>> 5 levels. Thus, the model in nlme notation is:
>>>
>>> lme(NEnessKM ~ Period_std + I(Period_std^2) + factor(Phylogeny)
>>>           + Period_std : factor(Phylogeny) + I(Period_std^2) :
>>> factor(Phylogeny),
>>>           random = ~1| factor(Species),
>>>           weights = varIdent(form =~ 1 | factor(Species)),
>>>           control = ctrl2,
>>>           method = "REML",
>>>           data = NEness5y)
>>>
>>>
>>> My concern is that this model might be too complicated. I have only 96
>>> observation. If I follow the rule of 15 observation per parameter
>>> estimated, this model i way to complex. I understand that in order to
>>> include quadratic terms, one must include also the linear effect... and
>>> that would also apply for the interactions. If the relationship between
>>> my
>>> response (NEness = Mean Latitude) and Period is a parabola, I guess I
>>> should include also the Period^2 in the interaction (Period^2 *
>>> Phylogeny)?
>>> But in that case, I have to include the interaction of Period *
>>> Phylogany?
>>>
>>> Is there a way to reduce the complexity? Is it totally wrong if I do not
>>> include the linear effects and keep only Period^2?
>>>
>>> Thanks for sharing your knowledge and for the advice.
>>>
>>> Best
>>>
>>> Diego
>>>
>>>
>>>
>>>
>>> --
>>> *Diego Pavón Jordán*
>>>
>>> *Finnish Museum of Natural History*
>>> *PO BOX 17 *
>>>
>>> *Helsinki. Finland*
>>>
>>> *0445061210https://www.researchgate.net/profile/Diego_Pavon-jordan
>>> <https://www.researchgate.net/profile/Diego_Pavon-jordan>*
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models op r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>
>
> --
> *Diego Pavón Jordán*
>
> *Finnish Museum of Natural History*
> *PO BOX 17 *
>
> *Helsinki. Finland*
>
> *0445061210https://www.researchgate.net/profile/Diego_Pavon-jordan
> <https://www.researchgate.net/profile/Diego_Pavon-jordan>*
>

	[[alternative HTML version deleted]]