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

[Malcolm Fairbrother]

Dear Simone,
Glad that was useful, and yes everything you say sounds right to me. For p
values, my understanding is that LRTs are fine, however. Or you could also
use a bootstrap, or MCMC.
As for autocorrelation, sorry, I hadn't been thinking through the
implications of your having a binary outcome variable. And I had been under
the impression that nlme could fit logit models, but a quick investigation
around the web suggests I was wrong about that. (If anyone knows otherwise,
please correct me/us.)
I am not an expert on dealing with autocorrelation in the case of binary
outcomes. Someone else may have advice about that, however.
Best wishes,
Malcolm


On 16 November 2015 at 16:07, Simone <miseno77 at hotmail.com> wrote:

> 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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