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: ? = 0.22, SE = 0.05, z = 4.34, p < 0.001
pre & mid vs. post test: ? = -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: ? = -0.01, SE = 0.04, z = -0.14, p > 0.05 (=0.89),
pre vs. post test: ? = 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