Dear all,

sorry to write again on this topic, but i feel like I haven't make myself clear.
I try to rephrase my question, hope I'm not annoying you. 

So given that each level of season - e.g. April and Oct - occurs at each level of subject while each level of treatment -e.g. high or control - only occurs on a half of the the subjects respectively and randomly,  should I specify the random effects in the model as 

(1) having subject nested within treatment and crossed with date, 
 V1 ~ treatment * season + V2 + (1|treatment/subject) + (1|season)

(2) subject crossed with date ignoring the nesting with treatment, (3) random effects on subject only ignoring the crossed and nested data structure
V1 ~ treatment * season + V2 + (1|subject) + (1|season)

(3) random effects on subject only ignoring the crossed and nested data structure
V1 ~ treatment * season + V2 + (1|subject)

Additionally it would be useful to know if any of these models correctly account for the fact that I have repeated measures on subjects on two seasons.

Beast regards.
m,


Il giorno 31 Jan 2012, alle ore 12:43, matteo dossena ha scritto:

> Thierry,
> thanks for the answer.
> I apologize for having omitted the fixed effect from the model posted previously.
> 
> My doubts are regarding the specification of the random structure.
> more specifically should i specify in the random structure that subject is nested within treatment?
> Correct me if I'm wrong, but , doesn't the syntax: (1|subject/season) means season nested within subject? 
> 
> second
> Having repeated measures on subject, including subject in the random effect, am I already accounting for the non independence of the residuals?
> or should I also add the correlation structure?
> 
> sorry for the incompleteness of my previous post, hope this hep to clarify my question.
> 
> best regards
> m.
> 
> 
> Il giorno 31 Jan 2012, alle ore 12:02, ONKELINX, Thierry ha scritto:
> 
>> Dear Matteo,
>> 
>> You want to know the effect of treatment and season: then you should use them as fixed effect. Here a some models
>> 
>> V1 ~ treatment * season + V2 + (1|subject)
>> V1 ~ treatment * season + V2 + (1 + V2|subject) #interaction between subject and V2
>> V1 ~ treatment * season + V2 + (1|subject/season) #interaction between subject and season
>> 
>> You way want to do some reading on mixed models. E.g. Zuur et al (2009)
>> 
>> Best regards,
>> 
>> Thierry
>> 
>> 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
>> + 32 2 525 02 51
>> + 32 54 43 61 85
>> Thierry.Onkelinx@inbo.be
>> www.inbo.be
>> 
>> 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
>> 
>> 
>> -----Oorspronkelijk bericht-----
>> Van: r-sig-mixed-models-bounces@r-project.org [mailto:r-sig-mixed-models-bounces@r-project.org] Namens matteo dossena
>> Verzonden: maandag 30 januari 2012 21:05
>> Aan: r-sig-mixed-models@r-project.org
>> Onderwerp: [R-sig-ME] repeated measure in partially crossed design
>> 
>> Dear All,
>> 
>> I'm having a some doubts on how to properly specify the random effect  in lmer for the following experiment:
>> 
>> I have 10 subject assigned to treatment A and 10 assigned to treatment B. For each subject I measured 2 variables, V1 and V2, and these measures were repeated in season A and season B.
>> 
>> I'm interested in assessing the effect of treatment, season and their interaction on the relationship between the two variables. 
>> 
>> I'm a little confused on how I have to specify the random structure.
>> 
>> According to the design  I identified the following random structure
>> 
>> (1 + V2 | treatment/subject) + (1 + V2 | season)
>> 
>> where the first term account for the nested design and temporal pseudorplication and the second term for the other unmeasured variable that covary with season and could affect the within season relationship between V1 and V2
>> 
>> correct?
>> 
>> Then i performed a model selection procedure, to investigate which would be the best random structure. To do so I subsequently removed each term from the random structure, and  compared the AIC score.
>> From this procedure it turned out that the best random structure is:  (1 + var2 | season).
>> 
>> My concern here is, if subject is no longer included in the random structure, i'm no longer accounting for the pseudorplication, am I?
>> 
>> Is the fact that the term (1 + V2 | treatment/subject) does not improve the model fit telling that in fact there is no significant correlation between measures on the same subject?
>> 
>> 
>> To further explore the issue, i also analysed the following random structure:
>> 
>> (1 + V2 | subject) + (1 + V2 | season)
>> 
>> and in this case the model selection procedure identified this as the best random structure.
>> 
>> So here is where i got confused.
>> My question is how do i properly specify the random structure?
>> 
>> Searching for an answer to my doubt in the literature, in Crawley 2007, i came across a further way to account  for temporal pseudoreplication (in lme: random = ~ season | subject) translated in lmer would it be
>> 
>> (season | subject) ? 
>> 
>> at this stage I had to give up and ask for help.
>> Could anyone give me some advice? is my strategy somehow wrong?
>> 
>> Cheers
>> matteo
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> 
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