[R-sig-ME] Scaling the response of a linear model to different factor groups
Daniel Rubi
daniel_rubi at ymail.com
Wed Jul 22 04:17:36 CEST 2015
Thanks a lot.
--------------------------------------------
On Tue, 7/21/15, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
Subject: Re: [R-sig-ME] Scaling the response of a linear model to different factor groups
To: "Daniel Rubi" <daniel_rubi at ymail.com>
Cc: "r-sig-mixed-models at r-project.org" <r-sig-mixed-models at r-project.org>
Date: Tuesday, July 21, 2015, 4:07 AM
Dear
Daniel,
You answered your
first question yourself: if you don't add the
interaction then effect of whisker_row is forced to be
identical between the species. The interaction seems to be
required from an ecological point of view.
I would not standardise the whisker
lengths. I find that is makes the model harder to interpret.
Instead, rather think about the whether the effects of
species and whisker_row is additive of multiplicative. E.g.
do you want to express the difference between the first and
second rows to be x mm (additive) or rather as the second
row is x% of the first row (multiplicative). The additive
model is plain lmm. You get the multiplicative model by log
transforming the length or by using a gamma distribution
with log link.
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
2015-07-18 20:55 GMT+02:00
Daniel Rubi <daniel_rubi at ymail.com>:
I hope this question is general
enough to be of broad interest.
Here's the abstract explanation of my problem:
I have two groups for which I measured a certain feature.
Specifically, this feature can be divided into
sub-categories, where I am aware that in each group there
are inherent differences among the sub-categories. I'm
interested to test if the two groups differ WRT to this
feature, and if so what is the contribution of each
sub-category to this feature.
Now, here's the actual data I'm working with:
My groups are two species of rodents. The feature is their
whisker lengths. The whiskers are organized in four rows on
the faces of each of the species (they are compatible betwen
the two species).In both species the whiskers at different
rows have different lengths (e.g., row 1 has the longest
among all other rows whiskers in both species).
What would be the correct linear model to test this?
The simple mixed-effects model I can think of would be:
whisker_length ~ species + whisker_row + (1|animal)
where animal is a random effect, since I measure whiske
lengths for several animals of each species.
Is this model sufficient?
My concerns are:
1. Interpretation - if the result of the model is that both
species and whisker_row (one or more of the four rows) are
significant, does this model inform me whether the
significant whisker rows are different between the two
species? My impression is that the only interpreation is
that whisker_row significantly determines whisker length,
regardless of the species. Hence, should I add an
interaction term between species and whisker_row to capture
that?
2. Should I standardize all whisker lengths relative to
their rows, so that they are on a common scale according to
their row?
Thanks a lot,
rubi
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