[R-sig-ME] lmer model construction

[Thierry Onkelinx]

Dear Jeremy,

The parameters of the main effects are conditional on the interaction. So
you can't compare them directly. I suggest that you read 'Regression
Modeling Strategies' (Harrell, 2001) for more details on this topic.

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

2015-10-07 15:55 GMT+02:00 Jeremy Chacon <chaco001 op umn.edu>:

> 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 op 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 op 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 op 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]]