[R-sig-ME] R-sig-mixed-models Digest, Vol 94, Issue 38
Szymek Drobniak
geralttee at gmail.com
Sat Nov 1 21:12:33 CET 2014
Hi Eoin,
putting PI on the left site of "|" (e.g. 1|PI:...) does not allow for
correlation between parasite levels. I would test several versions with
both sex and parasite as generating heterogenuous (co)variance structure so
e.g. (Sex+PI-1|block:line), (Sex:PI-1|block:line). Also - although both
allow for non-zero covariances between sexes/parasites/sex:parasite
combinations, they do not allow for testing a likely hypothesis that these
correlations are different from unity. This could be done e.g. in asreml
using the corh() structure.
Cheers
Szymek
> Message: 2
> Date: Thu, 30 Oct 2014 16:54:55 +0100
> From: Eoin Duffy <eoinduffy0000 at googlemail.com>
> To: "ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be>
> Cc: "r-sig-mixed-models at r-project.org"
> <r-sig-mixed-models at r-project.org>
> Subject: [R-sig-ME] Follow up question testing three way interaction
> between two fixed effects and a random effect nested in a fixed
> Message-ID:
> <
> CAPeV8wV6JqmvPQi53J_VwhUz3w-2EZ2Zj91ECx9JwGfAkwWM5w at mail.gmail.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Dear Thierry
>
> Thank you very much for your insightful reply. I was a bit unsure about
> specifying Block as a random effect so thanks for further clarifying that
> for me.
>
> I have a follow up question about a similar, separate analysis if yourself
> or the mailing list have time to think about. I need to include an
> additional fixed factor (2 levels) which block (3 levels) is nested in
> which then has line (35 levels) nested in it.
>
> So the background is. I am measuring male and female fitness in Drosophila
> (n=10/sex) from 35 lines over three blocks (same line ID during each
> block), all this was performed twice using different 'tester' flies from
> two different populations that were or were not infected with a parasite
> (i.e.parasite infection +/-: PI) in order to examine whether parasite
> infection deferentially affected intersexual fitness across lines.
>
> So I'm primarily interested in the three way interaction between sex x line
> x parasite infection (PI), 'does intersexual fitness differ between lines
> if their fitness was measured using flies that were or were not infected
> with the parasite?'
>
> My model looks like this, modifying from Thierry's suggested code below
> with PI (2 levels), Block (3 levels), Sex as fixed factors and line as a
> random factor nested within Block, nested with PI, which I think is right.
>
> M1<-lmer(Fitness~Sex+Block+PI+(0+Sex|PI:Block:Line), noNAdata)
> M2<-lmer(Fitness~Sex+Block+PI+(1+PI:Block:Line), noNAdata)
> anova(M1,M2)
>
> Which produces the below output
>
> > anova(M1, M2)
> refitting model(s) with ML (instead of REML)
> Data: newdataWol
> Models:
> ..1: Fitness ~ Sex + Block + WolInfection + (1 | WolInfection:Block:Line)
> object: Fitness ~ Sex + Block + WolInfection + (0 + Sex |
> WolInfection:Block:Line)
> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
> ..1 6 11051 11089 -5519.6 11039
> object 8 10986 11037 -5485.1 10970 69.012 2 1.033e-15 ***
>
> My question is does it seem as through I have specified my models correctly
> in order to check the significance of the 3 way sex x line x parasite
> infection
> interaction?
>
> Any suggestions would be greatly appreciated.
>
> Eoin
>
>
>
> On Thu, Oct 30, 2014 at 10:10 AM, ONKELINX, Thierry <
> Thierry.ONKELINX at inbo.be> wrote:
>
> > Dear Eoin,
> >
> > Much depends on how you code Line. If Each line has a unique code, thus
> > each line ID occurs in only one block, then (1|Sex:Block:Line) is equal
> to
> > (1|Sex:Line). If you have a crossed design and you reuse line ID among
> > block (a line ID can occur in more than one block), then
> (1|Sex:Block:Line)
> > is different from (1|Sex:Line). (1|Sex:Block:Line) is the most explicit
> way
> > to write it and it does not depends on the coding of line ID.
> >
> > A few more things:
> > - Although block is random from a philosophical standpoint, it is better
> > to use it as a fixed effect because it has only 3 levels. More details on
> > http://glmm.wikidot.com/faq
> > - I'd rather look at (0 + Sex|Line) than (1|Line:Sex). (0 + Sex|Line)
> > allows for a different variance in line effect between male and female,
> and
> > a correlation between male and female within the line
> >
> > M1 <- lmer(FitnessCured ~ Sex + Block + (0 + Sex|Block:Line), noNAdata)
> > M2 <- lmer(FitnessCured ~ Sex + Block + (1|Block:Line), noNAdata)
> > anova(M1, M2)
> >
> > 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
> > + 32 2 525 02 51
> > + 32 54 43 61 85
> > Thierry.Onkelinx at inbo.be
> > www.inbo.be
> >
> > 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
> >
> > -----Oorspronkelijk bericht-----
> > Van: r-sig-mixed-models-bounces at r-project.org [mailto:
> > r-sig-mixed-models-bounces at r-project.org] Namens Eoin Duffy
> > Verzonden: dinsdag 28 oktober 2014 22:14
> > Aan: r-sig-mixed-models at r-project.org
> > Onderwerp: [R-sig-ME] Testing interaction between fixed effect and random
> > effect nested within another random effect
> >
> > Hello mixed model list
> >
> >
> > I am working on a mixed model using lmer in R and I am a bit stuck on
> some
> > coding. I have measured male and female fitness in Drosophila from 35
> > inbred lines (genotype) over three blocks.
> >
> > My response variable is 'fitness' with n=10 individuals/sex/line/block
> > tested.
> >
> > Sex is fixed, Block is random and Line nested within block is random. I
> > primarily interested in the interaction between sex and line. Therefore
> my
> > model looks like
> >
> > m1<-lmer(FitnessCured~Sex+(1|Block/Line)+(1|Block)+(1|Sex:Line),noNAdata)
> >
> > If I wanted to tested the significance of the Sex:Line interaction my
> plan
> > is to just compare the above model to a model without the interaction and
> > use anova to compare the two models
> >
> > e.g. m2<-lmer(FitnessCured~Sex+(1|Block/Line)+(1|Block),noNAdata)
> > anova(m1,m2)
> >
> > However what I am wondering is if I am testing the significance of the
> > Sex:Line interaction (included as a random effect) will R know Line is
> > nested within Block ???
> >
> > How do I specify the interaction between Sex by Line nested within Block
> ??
> >
> > Should it be something like
> >
> > m1T<-lmer(FitnessCured~Sex+(1|Block/Line)+(1|Block)+(1|Sex:Block:Line)
> >
> > Any thoughts would be appreciated. I have included a sample of my data
> > below
> >
> > Block Line Sex FitnessInfected FitnessCured2 1 2 M
> > 1.4573 0.22153 1 2 M 1.1551
> > 1.13794 1 2 M 1.4573 1.13797 1 2
> > M 1.4573 0.41089 1 2 M -1.5648
> > 1.137911 1 2 F -0.2669 -1.247312 1
> > 2 F 0.2785 -1.247313 1 2 F -0.5396
> > -1.247314 1 2 F -0.5396 0.460215 1
> > 2 F 1.8237 -1.247316 1 2 F
> > 0.7330 0.496517 1 2 F 1.5511 -1.247318
> > 1 2 F -0.5396 1.477419 1 2 F
> > 1.0966 1.186820 1 2 F -0.5396
> > -1.247321 1 3 M 1.2054 0.716222 1 3
> > M 1.2585 0.314624 1 3 M -1.5648
> > 0.267226 1 3 M -0.8932 -0.861527 1
> > 3 M 0.5047 1.137928 1 3 M 0.7704
> > 1.137929 1 3 M -1.5648 -1.768931 1
> > 3 F -0.5396 0.678232 1 3 F
> > -0.5396 -1.247333 1 3 F -0.5396 1.077834
> > 1 3 F -0.5396 -1.247335 1 3 F
> > -0.5396 -1.247336 1 3 F -0.5396
> > 0.714537 1 3 F -0.5396 0.7508
> >
> > --
> > Eoin Duffy
> >
> > PhD Researcher
> > Institute of Environmental Sciences
> > Jagiellonian University
> > Gronostajowa 7
> > 30-387, Krakow
> > Poland
> >
> > [[alternative HTML version deleted]]
> >
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>
>
>
> --
> Eoin Duffy
>
> PhD Researcher
> Institute of Environmental Sciences
> Jagiellonian University
> Gronostajowa 7
> 30-387, Krakow
> Poland
>
> [[alternative HTML version deleted]]
>
>
> --
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