Thank you for your answer. Sorry for the missing example. In fact, i think, i solved the issue by some data-manipulations in the function. I splitted the data (one set for each measuring time), selected the cases at random, and then combined the two measuring times again. Results look promising to me, but if someone is aware of problems, please let me know.
This code should run: library(boot) anova.daten=data.frame(subject=sort(rep(1:10,2)), mz=rep(1:2,10), ort=sort(rep(1:2,10)),PHQ_Sum_score=rnorm(20,10,2)) #generate data summary(aov(PHQ_Sum_score~mz*ort+Error(subject/mz),data=anova.daten)) F_values <- function(formula, data1, indices) { data2=subset(data1, data1$mz==2) #subsetting data for each measuring time data3=subset(data1, data1$mz==1) data4 <- data3[indices,] # allows boot to select sample subjekte=na.omit(data4$subject) data5=rbind(data3[subjekte,], data2[subjekte,]) #combine data data5$subject=factor(rep(1:length(subjekte),2)) #convert repeated subjects to unique subjects fit=aov(formula,data=data5) #fit model return(c(summary(fit)[1][[1]][[1]]$`F value`, summary(fit)[2][[1]][[1]]$`F value`)) #return F-values } results <- boot(data=anova.daten, statistic=F_values, R=10, formula=PHQ_Sum_score~mz*ort+Error(subject/mz)) #bootstrap Thanks a lot, Felix Fischer ______________________________________________ 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.