I'd like to preface this answer by suggesting that if you have multiple measurements within subjects then you should possibly be thinking about using mixed-effects models. Here you have a balanced design and seem to be thinking about a constrained bootstrap, but I don't know whether the resulting distribution will have the right properties - others on the list may.
That said, I think that this needs the 'strata' argument of the boot function. See the help. Something like data.boot <- boot(data, boot.huber, 1999, strata = Subject) (not tested) Cheers Andrew On Thu, Jul 4, 2013 at 12:19 PM, Sol Lago <solcita.l...@gmail.com> wrote: > Hi there, > > This is the first time I use this forum, and I want to say from the start I > am not a skilled programmer. So please let me know if the question or code > were unclear! > > I am trying to bootstrap an interaction (that is my test statistic) using the > package "boot". My problem is that for every resample, I would like the > randomization to be done within subjects, so that observations from different > subjects are not mixed. Here is the code to generate a dataframe similar to > mine: > > Subject = rep(c("S1","S2","S3","S4"),4) > Num = rep(c("singular","plural"),8) > Gram = rep(c("gram","gram","ungram","ungram"),4) > RT = c(657,775,678,895,887,235,645,916,930,768,890,1016,590,978,450,920) > data = data.frame(Subject,Num,Gram,RT) > > This is the code I used to get the empirical interaction value: > > summary(lm(RT ~ Num*Gram, data=data)) > > As you can see, the interaction between my two factors is -348. I want to get > a bootstrap confidence interval for this statistic, which I can generate > using the "boot" package: > > #Function to create the statistic to be boostrapped > boot.huber <- function(data, indices) { > data <- data[indices, ] #select obs. in bootstrap sample > mod <- lm(RT ~ Num*Gram, data=data) > coefficients(mod) #return coefficient vector > } > > #Generate bootstrap estimate > data.boot <- boot(data, boot.huber, 1999) > > #Get confidence interval > boot.ci(data.boot, index=4, type=c("norm", "perc", "bca"),conf=0.95) #4 gets > the CI for the interaction > > My problem is that I think the resamples should be generated without mixing > the individual subjects observations: that is, to generate the new resamples, > the observations from subject 1 (S1) should be shuffled within subject 1, not > mixing them with the observations from subjects 2, etc... I don't know how > "boot" is doing the resampling (I read the documentation but don't understand > how the function is doing it) > > Does anyone know how I could make sure that the resampling procedure used by > "boot" respects the subject level information? > > Thanks a lot for your help/advice! > ______________________________________________ > 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. -- Andrew Robinson Deputy Director, CEBRA Senior Lecturer in Applied Statistics Tel: +61-3-8344-6410 Department of Mathematics and Statistics Fax: +61-3-8344 4599 University of Melbourne, VIC 3010 Australia Email: a.robin...@ms.unimelb.edu.au Website: http://www.ms.unimelb.edu.au FAwR: http://www.ms.unimelb.edu.au/~andrewpr/FAwR/ SPuR: http://www.ms.unimelb.edu.au/spuRs/ ______________________________________________ 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.