[R-sig-ME] try lme instead

[Gregoire, Timothy]

With lme of the nlme package you can specify a correlation argument to account for longitudinal correlation. Try a CAR1 correlation.

Tim 

Timothy G. Gregoire
J. P. Weyerhaeuser Professor of Forest Management
School of Forestry & Environmental Studies
Yale University
360 Prospect St, New Haven, CT, U.S.A. 06511
Ph: 1.203.432.9398 mob: 1.203.508.4014  fax:1.203.432.3809


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Today's Topics:

   1. Re: lme4 random effects for repeated measures, unbalanced
      data (Ben Bolker)


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

Message: 1
Date: Sat, 30 Jul 2016 11:16:07 -0400
From: Ben Bolker <bbolker at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] lme4 random effects for repeated measures,
	unbalanced data
Message-ID: <de3be868-c64c-052c-00db-436e3abd5708 at gmail.com>
Content-Type: text/plain; charset=windows-1252



On 16-07-19 07:28 PM, Ann Marie Raymondi wrote:
> Hello Listers,
> 
> I have a vegetation data set that consists of 150 plots that were 
> sampled
> 1-3 times over a three year period.  Plots are my unit of observation 
> and they are unbalanced (since plots were sampled either once, twice, 
> or three times).  I would like to use mixed-effects models in order to 
> account for variation in both plots and sampling year and to keep my 
> sample size large (instead of conducting my analysis within individual 
> years). Here is an example of the grouping of my data:
> 
> Plot_ID       Plot     Year
> 2012_101   101     2012
> 2013_101   101     2013
> 2014_101   101     2014
> 2012_201   201     2012
> 2013_201   201     2013
> 2013_301   301     2013
> 
> My response variables are cover of vegetation functional groups and 
> predictors include variables related to fire and treatment history.
> Additionally, I am not interested in how plots change over time per 
> se, but rather, in aggregating sampling from all three years to 
> increase my sample size and to account for the spatial/temporal 
> correlation that arises from doing so.  It is my assumption that 
> treating plot as a random effect (intercept only) accounts for 
> variation that arises from potential spatial autocorrelation, but my 
> main question is how to account for the repeated measures and if I 
> need to account for the grouping of cells within sampling
> years:
> 
> Potential model:
> 
> model<-lmer(response~covariates + (1|Plot) + (1|Year).
> 
> However, I know that is not appropriate to use a random effect with 
> only three levels, year in this case.  I'm hoping for recommendations 
> on how to incorporate year as a random effect.  Is including (Year |  
> Plot)  recommended?  And if so, how might I interpret that effect, 
> i.e., is it accounting for variation introduced by different sampling 
> year or variation in plots over sampling year?

> Thank you in advance for any help/suggestions!
>

  Include Year as a fixed effect.  You could try to include (Year|Plot), but it will overlap with the residual error (since each plot is measured once per year), so it probably won't work (without some more fussing around).  You'll probably get most of the signal by including Year as a fixed effect.



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