[R-sig-ME] Orthogonal vs. Non-orthogonal contrasts

[Thierry Onkelinx]

Dear Yasu,

A is part of two interactions. Hence you cannot interpret this main effect
without the interactions. Note that changing the contrast will also effect
the interactions.

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

2016-05-25 4:42 GMT+02:00 Yasuaki SHINOHARA <y.shinohara op aoni.waseda.jp>:

> Dear all,
>
> Hello, I am doing research of second language acquisition.
> I am wondering about the glmer in R for my analyses. Could you please
> answer my question?
>
> I have the following logistic mixed effects model.
> model<-glmer(corr ~ A + B + C + D + A:B + B:C + A:D +(1+A|subject) +
> (1+A|item:speaker),family=binomial,data=mydata,control=glmerControl(optimizer="bobyqa",
> optCtrl=list(maxfun=1000)))
>
> I tested language learners (subjects) three time (pre-training,
> mid-training, post-training) with the "item" produced by "speaker", so
> Factor A is "testing block" which has three levels (i.e., pre, mid, post).
> Since each subject took the test three times, the random slopes for the
> Factor A were also included as a random factor.
>
> I made an orthogonal contrast for the Factor A (testing block) as follows.
> PreVsMid<-c(1,-1,0)
> PreMidVsPost<-c(1,1,-2)
> contrasts(mydata$A)<-cbind(PreVsMid,PreMidVsPost)
>
> The results from summary(model) function for this factor were as follows.
> pre vs. mid test: β = 0.22, SE = 0.05, z = 4.34, p < 0.001
> pre & mid vs. post test: β = -0.21, SE = 0.04, z = -5.96, p < 0.001.
>
> However, I thought it would be better if I made a non-orthogonal contrast
> for this factor as "pre vs. mid" and "pre vs. post" to test my hypothesis.
> So I made a new contrast for the Factor A as follows.
> PreVsMid<-c(1,-1,0)
> PreVsPost<-c(1,0,-1)
> contrasts(mydata$A)<-cbind(PreVsMid,PreVsPost)
>
> The results from summary(model) function for this contrast were
> pre vs. mid test: β = -0.01, SE = 0.04, z = -0.14, p > 0.05 (=0.89),
> pre vs. post test: β = 0.42, SE = 0.07, z = 5.96, p < 0.001.
>
> Although the first contrast (pre vs. mid) is the same for both models, why
> the results of pre vs. mid contrast are so different (one is very
> significant, but the other one is not significant)?
>
> I really appreciate any help.
>
> Best wishes,
> Yasu
>
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