http://stats.stackexchange.com/questions/62225/conditional-logistic-regression-vs-glmm-in-r might be a good start ____________________________ Ersatzistician and Chutzpahthologist I can answer any question. "I don't know" is an answer. "I don't know yet" is a better answer.
On Tue, Jul 1, 2014 at 10:24 AM, Suzon Sepp <suzon.s...@gmail.com> wrote: > Hi, > > It seems that I'm quite lost in this wide and powerful R's universe, so I > permit myself to ask your help about issues with which I'm struggling. > Thank you, > > I would like to know if the answer’s accuracy (correct = 1; incorrect = 0) > varies depending on 2 categorical variables which are the group (A and B) > and the condition (a, b and c) knowing that I’ve got n subjects and 12 > trials by conditions for each subject (i.e. 12 repetitions). > > To do that, I’m focusing on logistic regression analysis. I’ve got no > problem with this kind of analysis until now (logistic regression with > numeric predictor variables and/or categorical predictor with 2 levels > only) but, in this new context, I think I have to focus more specifically > on logistic regression including *nested (or random?) factors* in a*repeated > measures design* (because of the variables “Subject” and “Trial”) with a > categorical predictor variable with *more than 2 levels* (the variable > “Condition”) and I never did such a thing…yet. > > mydata = > mydata$Subject: Factor w/38 levels: "i01", "i02", "i03", "i04" > mydata$Group: Factor w/2 levels: "A", "B" > mydata$Condition: Factor w/3 levels: "a", "b", "c" > mydata$Trial: Factor w/12 levels: "t01", "t02", ..."t12" > mydata$Accuracy: Factor w/2 levels: "0", "1" > > Subject Group Trial Condition Accuracy > i01 A t01 a 0 > i01 A t02 a 1 > ... > i01 A t12 a 1 > i01 A t01 b 1 > i01 A t02 b 1 > ... > i01 A t12 b 0 > i01 A t01 c 0 > i01 A t02 c 1 > ... > i01 A t12 c 1 > i02 B t01 a 1 > ... > > First, I’m wondering if I have to calculate a % of accuracy for each > subject and each condition and thus “remove” the variable “Trial” but > “lose” data (power?) in the same time… or to take into account this > variable in the analysis and in this case, how to do that? > > Second, I don’t know which function I’ve to choose (lmer, glm, glmer…)? > > Third, I’m not sure I proceed correctly to specify in this analysis that > the responses all come from the same subject: within-subject design = > …+(1|Subject) as I can do for a repeated measures ANOVA to analyze the > effect of my different variables on a numeric one such as the response > time: > test=aov(Int~Group*Condition+*Error(Subject/(Group*Condition)*),data=mydata) > and here again how can I add the variable "Trial" if I don't work on an > average reaction time for each subject in the different conditions? > > Below, examples of models I can write with glmer(), > > fit.1=glmer(Accuracy~Group* Condition > +(1|Subject),data=mydata,family=binomial) > > fit.2=glmer(Accuracy~Group* Condition > +(1|Subject)-1,data=mydata,family=binomial) (“without intercept”) > > fit.3=glmer(Accuracy~Group* Condition +(1|Subject)+(1|Trial)...?? > > > I believed the analysis I've to conduct will be in the range of my > qualifications then I realize it could be more complicated than that of > course (ex GLMMs), I can hear "do it as we do usually" (=repeated measures > ANOVA on a percentage of correct answers for each subject ??) as if there's > only one way to follow but I think there's a lot, which one's revelant for > my data, that's I want to find. > > Hope you can put me on the track, > > Best > > Suzon > > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
