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

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