Thanks Hans for your two messages to my question on glmer yesterday. May I ask another question?

In your first reply you suggested to include only one single random effects (Palm) and Time as Fixed factor (1). I like that model as I can see the effects among years.  However in your second suggested model, Time as random effect does not indicate to me whether there is difference among years. There is some variance, but variance does not say nothing to me, specially if there is not a total/residual variance to compare with!


1) Model1<-glmer(Total leaves ~ DT * G * Time + (1 | palm), family=poisson)
2) Model1<-glmer(Total leaves ~ DT * G + (1 | Time) + (1 | palm), family=poisson)

Can you let me know your thought?

Leo




Message: 4
Date: Mon, 20 Feb 2012 02:01:09 +0100
From: Hans Ekbrand <hans@sociologi.cjb.net>
To: r-sig-mixed-models@r-project.org
Subject: Re: [R-sig-ME] Need some help with glmer output
Message-ID: <20120220010109.GB10481@isas.ltsp>
Content-Type: text/plain; charset=us-ascii

On Sat, Feb 18, 2012 at 09:13:04AM -0800, lopez toledo wrote:
> Dears lme4 users:
> I'm doing a repeated measurement analysis, which seems pretty obvious to 
> me, but I'm getting some strange results, which I do not 
> understand. Hope you can help me!! Thanks in advance!
> 
> I'm evaluating the effects of Defoliation Treatments (4 levels), Gender 
> (Male/Female) and Time on several responses (Growth , leaf production, 
> inflorescence production, etc) of a palm dioecious species 
> 
>
 I have about 550 palms total which have been measured 3 times. My main 
question is whether the effects depends on the intensity of defoliation,
 palm gender and how this has changed through time. I am 
> considering the effects of repeated measurement as Palm/Time
> I am exploring with the model below, which include as fixed factors DT*G*Time and as 
> random factor Palm/Time to consider the effect of repeated measurements. 
> 
>
 In this case, I'm using glmer, 'cos I've have counts as response 
variables (number of leaves, number of inflorescences, etc).
> 
> Model1<-glmer(Total leaves ~ DT * G * Time + (1 | palm/Time), family=poisson)
> 
> 
> I've got two questions:
> 1) Am 
> I considering the random effects correctly or not? as when I run the model there is the following message
> 
> 
> "Number of levels of a grouping factor for the random effects
> is *equal* to n, the number of observations"

Is it not enough to define one simple random effect for palm? Wouldn't
that take care of the problem of repeated measurements of the same
palm?

Try:

Model1<-glmer(Total leaves ~ DT * G * Time + (1 | palm), family=poisson)



------------------------------

Message: 5
Date: Mon, 20 Feb 2012 02:07:52 +0100
From: Hans Ekbrand <hans@sociologi.cjb.net>
To: r-sig-mixed-models@r-project.org
Subject: Re: [R-sig-ME] Need some help with glmer output
Message-ID: <20120220010752.GC10481@isas.ltsp>
Content-Type: text/plain; charset=us-ascii

On Mon, Feb 20, 2012 at 02:01:09AM +0100, Hans Ekbrand wrote:
> On Sat, Feb 18, 2012 at 09:13:04AM -0800, lopez toledo wrote:
> > Dears lme4 users:
> > I'm doing a repeated measurement analysis, which seems pretty obvious to 
> > me, but I'm getting some strange results, which I do not 
> > understand. Hope you can help me!! Thanks in advance!
> > 
> > I'm evaluating the effects of Defoliation Treatments (4 levels), Gender
> > (Male/Female) and Time on several responses (Growth , leaf production,
> > inflorescence production, etc) of a palm dioecious species 
> > 
>
 > I have about 550 palms total which have been measured 3 times. My 
main question is whether the effects depends on the intensity of 
defoliation, palm gender and how this has changed through time. 

Rereading this paragraph, and in particular "and how this has changed
through time", I think you might want to specify Time as a random
variable too. However, I think it should be on its own, like this:

Model1<-glmer(Total leaves ~ DT * G + (1 | Time) + (1 | palm), family=poisson)

> Is it not enough to define one simple random effect for palm? Wouldn't
> that take care of the problem of repeated measurements of the same
> palm?
> 
> Try:
> 
> Model1<-glmer(Total leaves ~ DT * G * Time + (1 | palm), family=poisson)
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