Sorry if this is the wrong ml for this question, I am new to R. I am trying to use R to analyze the data from my thesis experiment and I am having troubles accounting for the pseudoreplication properly from having each participant repeat each treatment combination (combination of fixed factors) 5 times. The design of the experiment is as follows...
Responses: CompletionTIme VisitedTargets Fixed-factors: Targets (4-levels): 4, 9, 14, 19 Entropy (3-levels): Low, Medium, High Random-factors: Participants: 31 total participants Replicates: 5 (this could also be viewed as a time factor I think) BlockOrder: 1 though 60 (the order of the trials was random for each participant, but I am not so concerned about this right now) The fixed part of the model seems pretty intuitive: fixed=log(CompletionTime)~(Targets*Entropy) The random part of the model is where I get stuck on, I've tried many combinations and all give me the wrong degrees of freedom. I really don't know what to use. Any help would be greatly appreciated!!!! Here is the code I am using in R: library(nlme) datafile="http://people.rit.edu/rmh3093/mot.csv"; master1 = read.table(datafile,header=T) Block=factor(master1$Block) BlockOrder=factor(master1$Block_Order) Replicate=factor(master1$Replicate) Participant=factor(master1$Participant_ID) Targets=factor(master1$Targets) Entropy=factor(master1$Entropy) CompletionTime=master1$Completion_Time summary(lme(log(CompletionTime)~(Entropy*Targets),random=~1|Participant,method="ML")) Thanks in advance! -Ryan ______________________________________________ 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.
