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

[Yasuaki SHINOHARA]

Dear Thierry,

Thank you very much for your reply.
I understood why. The interaction of blockPreVsMid:FactorD turned 
significant in the model which contrasted the testing block factor as 
PreVsMid and PreVsPost (i.e.,cbind(c(1,-1,0),c(-1,0,1))), although the 
interaction was not significant in the model with the testing block 
contrasted as PreVsMid and PreMidVsPost (i.e., 
cbind(c(1,-1,0),c(1,1,-2))).

Could I ask another question?
What is the difference in making a contrast of PreVsMid as c(1,-1,0) 
and as c(0.5, -0.5, 0)?
It seems that the beta and SE are double if I code the contrasts with 
(0.5, -0.5, 0). I hope it does not matter.

Also, I coded "contrasts(data$FactorA)<-cbind(c(1,-1,0),c(-1,0,1))" to 
test the differences between the mean of level 1 vs. the mean of level 
2 and between the mean of level 1 and the mean of level 3. Is this 
correct? Some website says something different from what I understood 
(e.g., the first Answer of 
http://stats.stackexchange.com/questions/44527/contrast-for-hypothesis-test-in-r-lmer 
).

My model includes both categorical and numeric variable, and all 
categorical variables were coded manually.

Best wishes,
Yasu

On Wed, 25 May 2016 09:44:14 +0200
  Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
> 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 at 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
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>


************************************
Yasuaki SHINOHARA, Ph.D.
Assistant Professor
Center for English Language Education (CELESE)
Waseda University Faculty of Science and Engineering
3-4-1 Okubo, Shinjuku-ku, 169-8555, Tokyo JAPAN
email: y.shinohara at aoni.waseda.jp