[R-sig-ME] lmer model construction

Jeremy Chacon chaco001 at umn.edu
Wed Oct 7 16:33:21 CEST 2015 

Thierry, Jake, thank you both for your help.

Jake, you are correct, what is important to me is an effect size. I'll
stick with interpreting these and their SEs for now.

I appreciate all your help.

Best,

Jeremy

On Wed, Oct 7, 2015 at 9:08 AM, Jake Westfall <jake987722 at hotmail.com>
wrote:

> I guess the first step is to try to decide on some reasonably precise
> definition of what it means for a variable to be "important." I think this
> is a fundamental problem plaguing most discussions of "variable
> importance." Are you just talking about getting an indication of the
> strength of association between a predictor and the response variable--like
> an effect size? In that case I would recommend just looking at and
> interpreting the slope and its standard error and using your scientific
> judgment. Standardized effect size is kind of a fraught issue in linear
> mixed models.
>
> Jake
>
> > Date: Wed, 7 Oct 2015 08:55:18 -0500
> > From: chaco001 at umn.edu
> > To: thierry.onkelinx at inbo.be
> > CC: r-sig-mixed-models at r-project.org
> > Subject: Re: [R-sig-ME] lmer model construction
> >
> > Dear Thierry,
> >
> > Thanks for your help. You are correct, my LRT was incorrectly dropping
> more
> > than one term--I've fixed that now.  And good to hear I setup the nested
> > part of the model correctly.
> >
> > My more general question is how do I know whether terms such as main
> > effects are important? I understand there is disagreement about whether
> to
> > assign p-values to terms in a mixed model due to the degrees of freedom
> not
> > being fully known, but I would still like some measure of how important a
> > term is.
> >
> > Is a good way to keep doing likelihood-ratio tests all the way down to a
> > single-intercept model? Or is there another, perhaps better way to
> > interpret importance in the results of a mixed model?
> >
> > Thanks again,
> >
> > Jeremy
> >
> > On Wed, Oct 7, 2015 at 5:10 AM, Thierry Onkelinx <
> thierry.onkelinx at inbo.be>
> > wrote:
> >
> > > Dear Jeremy,
> > >
> > > Adding a random effect for petridish takes the nested design into
> account.
> > >
> > > P * S * M expands to P + S + M + P:S + P:M + S:M + P:S:M. So it
> includes
> > > the threeway interaction. I'm not sure if you want that.
> > > P * S + M expands to P + S + M + P:S. Hence the difference with P * S
> * M
> > > is P:M + P:S:M. So a LRT between P * S * M and P * S + M tests the
> combined
> > > effect of P:M and P:S:M.
> > >
> > > Best regards,
> > >
> > > ir. Thierry Onkelinx
> > > Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and
> > > Forest
> > > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> > > Kliniekstraat 25
> > > 1070 Anderlecht
> > > Belgium
> > >
> > > To call in the statistician after the experiment is done may be no more
> > > than asking him to perform a post-mortem examination: he may be able
> to say
> > > what the experiment died of. ~ Sir Ronald Aylmer Fisher
> > > The plural of anecdote is not data. ~ Roger Brinner
> > > The combination of some data and an aching desire for an answer does
> not
> > > ensure that a reasonable answer can be extracted from a given body of
> data.
> > > ~ John Tukey
> > >
> > > 2015-10-06 21:06 GMT+02:00 Jeremy Chacon <chaco001 at umn.edu>:
> > >
> > >> Hello all,
> > >>
> > >> I would appreciate any advice on how to construct and analyze my
> model.
> > >>
> > >> I have conducted a study where I put bacterial colonies onto petri
> dishes.
> > >> The colonies were randomly spread across the petri dishes, and the
> number
> > >> of colonies varied slightly across each petri dish. Some petri dishes
> > >> received one bacterial species, some petri dishes received another
> > >> species.
> > >> Additionally, half of the petri dishes contained one type of growth
> media,
> > >> and the other half contained a different media.
> > >>
> > >> So my experimental design is basically a two-factor design:
> > >>
> > >> 2 levels of species X 2 levels of media.
> > >>
> > >> The design is balanced.
> > >>
> > >> The complicated part is that my response of interest is how the
> proximity
> > >> of one bacterial colony to its neighbors affects the size of the
> bacterial
> > >> colony, and importantly, how this relationship is affected by the
> > >> bacterial
> > >> species, growth media, and their interaction.
> > >>
> > >> In other words, I have a nested design where my measurements of
> interest
> > >> (bacterial colonies) are nested with the truly independent replicates
> > >> (petri dishes), which is why I intend to use a mixed model.
> > >>
> > >> So the data look like this:
> > >>
> > >> results =
> > >>
> > >> species    media    colonySize    proximityToNeighbor    petriDishID
> > >>   A        A        12            4                      1
> > >>   A        A        38            42                     1
> > >>   A        B        18            50                     2
> > >>
> > >> etc, with one observation per colony, and typically about 100
> colonies per
> > >> petri dish.
> > >>
> > >> I am trying to correctly construct the model using lme4. I would
> > >> appreciate
> > >> suggestion on my model. Also, I would appreciate suggestions on
> > >> interpretations.
> > >>
> > >> My current thought: use a random intercept for each petri dish:
> > >>
> > >> m1 = lmer(colonySize ~ proximityToNeighbor * species * media + (1 |
> > >> petriDishID), data = results)
> > >>
> > >> but this does not describe the nesting of each colony within a petri
> dish
> > >> (at least as far as I understand). Do I need to do this?
> > >>
> > >> In terms of interpretation, I have been (1) looking at plots to get a
> feel
> > >> for effect size and then (2) getting significance values by doing a
> > >> predictor removal model comparison, like below:
> > >>
> > >> m1 = lmer(colonySize ~ proximityToNeighbor * species * media + (1 |
> > >> petriDishID), data = results)
> > >>
> > >> m2 = lmer(colonySize ~ proximityToNeighbor * species + media + (1 |
> > >> petriDishID), data = results)
> > >>
> > >> anova(m1, m2, test = "F")
> > >>
> > >>
> > >> When I do this, I get a tiny p-value, which (along with plots)
> suggests to
> > >> me that the interaction between species and media in their effect on
> > >> proximityToNeighbor's effect on colonySize is important. Does this
> sound
> > >> correct? Any better ways to do this?
> > >>
> > >> Thanks very much!
> > >>
> > >> Jeremy
> > >>
> > >>
> > >> --
> > >>
> > >>
> > >>
> *___________________________________________________________________________Jeremy
> > >> M. Chacon, Ph.D.*
> > >>
> > >> *Post-Doctoral Associate, Harcombe Lab*
> > >> *University of Minnesota*
> > >> *Ecology, Evolution and Behavior*
> > >>
> > >>         [[alternative HTML version deleted]]
> > >>
> > >> _______________________________________________
> > >> R-sig-mixed-models at r-project.org mailing list
> > >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >>
> > >
> > >
> >
> >
> > --
> >
> >
> *___________________________________________________________________________Jeremy
> > M. Chacon, Ph.D.*
> >
> > *Post-Doctoral Associate, Harcombe Lab*
> > *University of Minnesota*
> > *Ecology, Evolution and Behavior*
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 

*___________________________________________________________________________Jeremy
M. Chacon, Ph.D.*

*Post-Doctoral Associate, Harcombe Lab*
*University of Minnesota*
*Ecology, Evolution and Behavior*

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