[R-sig-ME] glmer random effects structure: a case

[Simone]

Dear Malcolm,

Thank you so much for your detailed (very
interesting references!) and helpful answer. I have centered Var2 by IND and I
have used both the individual-specific mean Var2 (Var2meanIND) as well as the
individual-specific centered Var2 (Var2varIND). I understand that this way I can
test if the variation among individuals (first case) or within them (second
case) relate to the response variable.

As you suspected, DATEs are often close
each other and it is quite probable I have autocorrelated data. You mentioned
that the nlme package handles correlated residuals and I have found the code to
do that but the problem is that I cannot do it for my case study since the
distribution I am using is a binomial and nlme is only for linear mixed model,
isn’it?

For now, I have being using the below syntax
and using LRT (anova) between reduced nested models to compute the p-value for
each predictor. I know that LRT is very criticized but I have been asked to
calculate p-values for each predictor.


Mod1<-glmer(Var1 ~ SEX + AGE +
Var2meanIND + Var2varIND + (1|DATE) + (1|IND) , data = mydata, family = binomial,
control = glmerControl(optimizer="bobyqa"))


This way I am not accounting at all for the
autocorrelation, do you have any suggestions?

Thanks again,

Simone
Date: Sun, 15 Nov 2015 16:41:28 +0100
Subject: Re: [R-sig-ME] glmer random effects structure: a case
From: M.Fairbrother at bristol.ac.uk
To: miseno77 at hotmail.com
CC: r-sig-mixed-models at r-project.org

Dear Simone,
How many INDs and DATEs are in your dataset? It sounds like you have plenty of INDs, but it's less clear how many DATEs you have. If you have a lot, you may have a situation of cross-classification: observations are nested both in INDs and DATEs, but neither of those is nested in the either.
If you don't have many DATEs, it will make more sense to use fixed effects for those. And even if you have a lot, if the DATEs are located close to each other in time, you may have a lot of autocorrelation, and that requires other techniques. (In R, you may need to use the older package nlme, which allows for correlated residuals.)
In any event, if INDs are in many cases captured on multiple DATEs, it definitely doesn't make sense to nest INDs in DATEs. Clearly they aren't nested. (Assuming I've understood your data structure correctly.)
It also sounds like you should be centering Var2 by IND. This is pretty much de rigueur in multilevel models with x variables that vary within clusters. Enders and Tofighi 2007 is a useful, clear paper on this issue, and you might also want to look at these recent papers by me and colleagues in the Centre for Multilevel Modelling at Bristol:
doi:10.1017/psrm.2014.7
doi:10.1017/psrm.2013.24
Basically, take the mean of Var2 for each individual, and enter that as a covariate. Then take the difference between the original Var2 and its mean for that individual, and enter that as a covariate as well. You'll get two pieces of information in your fitted model: the distinct "between" and "within" effects of Var2. It sounds like that is what you want.Hope that's useful.
- Malcolm


Date: Sat, 14 Nov 2015 17:48:46 +0100

From: Simone <miseno77 at hotmail.com>

Cc: "r-sig-mixed-models at r-project.org"

        <r-sig-mixed-models at r-project.org>

Subject: [R-sig-ME] glmer random effects structure: a case




Hi all,

I have a simple (but not that simple to me) question on how to specify the random structure in R.A binary response variable (Var1) has been measured from a number of individuals (IND) that have been susceptible of being captured over a number of dates (DATE). I suspect that Var1 might depend either on its sex (SEX), or age (AGE) or Var2 which is a continuous variable measured from each individual every time it is captured. Since Var2 is a measure of the quality of each individual, it is likely that some individuals will tend to have greater values of Var2 than others during the entire study period.Note that some individuals have been captured only one time, other two, other three and so on (very unbalanced). For each date an individual can be captured only one time.So, I have two groups: IND and DATE. I would think this is a two-level model with IND nested to DATE so that:

model1 <- glmer(Var1 ~ SEX + AGE + Var2 + (1|DATE/IND), family = binomial, data = mydata)

Does it make sense? I think i am not taking into account the fact that the mean of Var2 may be different among individuals but I don't know how to do that.I would really appreciate an answer to this question that I am sure would help me a lot to understand better how mixed models work.
  		 	   		  
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