R Special Interest Group for the use of R in ecological and environmental data analysis

nested mixed model?

Hi everybody,

I am trying to fit a model in R using the lme() function. I would like
to have your opinion about what I did and if there are better ways to
resolve this analysis. First Let me explain you how look the data that
we are analyzing. We want to compare the tooth microtexture of four
species of ungulates. 

For that we have taken pictures in eight different points within each
tooth of one individual. We used as many teeth as were available for
each individual taken in account their position and at the same time
separating them by the place they were located (mandible: down tooth;
maxilla: upper tooth). 

I am not an expert, but until here the model looks as nested design,
please let me know if I am wrong. In summary we have the following
hierarchy arrangement:

Species (4 species) > bone(mandible or maxilla) > tooth > points within
each tooth (8 points).

I have fitted this model using: lme(response ~ species, data=tooth,
random=~1|bone/tooth/points,na.action=na.omit)

I will be really grateful if you can give me your opinion about that.

Best wishes

Mauro

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Luciano Selzer | 3 Feb 13:59

Re: nested mixed model?

Hi, I'm by no means an expert, just an PhD student. But in my humble opinion
shouldn't you consider the individual effect? I think that the microtexture
could be influenced by this.
Luciano

2010/2/3 Mauricio Cifuentes <mcifuent@...>

> Hi everybody,
>
> I am trying to fit a model in R using the lme() function. I would like
> to have your opinion about what I did and if there are better ways to
> resolve this analysis. First Let me explain you how look the data that
> we are analyzing. We want to compare the tooth microtexture of four
> species of ungulates.
>
> For that we have taken pictures in eight different points within each
> tooth of one individual. We used as many teeth as were available for
> each individual taken in account their position and at the same time
> separating them by the place they were located (mandible: down tooth;
> maxilla: upper tooth).
>
> I am not an expert, but until here the model looks as nested design,
> please let me know if I am wrong. In summary we have the following
> hierarchy arrangement:
>
> Species (4 species) > bone(mandible or maxilla) > tooth > points within
> each tooth (8 points).
>
> I have fitted this model using: lme(response ~ species, data=tooth,
> random=~1|bone/tooth/points,na.action=na.omit)

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ONKELINX, Thierry | 3 Feb 16:09

Re: nested mixed model?

I aggree with Luciano. You should take the individual into account. The
point is only relevant as a random effect if you have multiple records
per point.

I would use something like lme(response ~ species, data=tooth,
random=~1|individual/bone/tooth) 

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx@...
www.inbo.be

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Andrew Kosydar | 3 Feb 19:17

Re: nested mixed model?

Hi Mauro,

I agree with both Thierry and Luciano, considering your nested design the random effects lie at the level of
the individual.  My only concern is that given the coding below, only the intercept varies and the slopes
are set to be equal for the random variables.  From what I have read (e.g. Schielzeth and Forstmeier 2008,
Behavioral Ecology), not investigating the slopes of the random variables can lead to spurious results. 
I would also consider using Markov Chain Monte Carlo (MCMC) approximations (see Bolker et al. 2009,
TREE).  However, I don't know if MCMC estimations work with nested designs.  Hence, perhaps the following
might work?

tooth.lmer<-lmer(response ~ species, data=tooth, random=~age_of_individual|individual/bone/tooth)
tooth.pval<-pvals.fnc(tooth.lmer, nsim=1000, withMCMC=TRUE)
tooth.pval$fixed
tooth.pval$random

Perhaps others have an opinion regarding variation of slopes and intercepts with mixed effects models? 
And the use of MCMC estimations with nested designs?

All the best,

Andrew

Andrew Kosydar
University of Washington
Department of Biology
24 Kincaid Hall, Box 351800
Seattle, WA 98195
USA

On Wed, 3 Feb 2010, ONKELINX, Thierry wrote:

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