Josh's comment prompted me to check mty go-to reference. Davison and Hinckley (1997 Section 3.8) recommend sampling the Subjects, but not within the Subjects.
Cheers Andrew On Thu, Jul 04, 2013 at 05:53:58PM -0700, Joshua Wiley wrote: > Hi, > > It is not the easiest to follow code, but when I was working at UCLA, > I wrote a page demonstrating a multilevel bootstrap, where I use a two > stage sampler, (re)sampling at each level. In your case, could be > first draw subjects, then draw observations within subjects. A strata > only option does not resample all sources of variability, which are: > > 1) which subjects you get and > 2) which observations within those > > The page is here: http://www.ats.ucla.edu/stat/r/dae/melogit.htm > > As a side note, it demonstrates a mixed effects model in R, although > as I mentioned it is not geared for beginners. > > Cheers, > > Josh > > > > On Wed, Jul 3, 2013 at 7:19 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. > > > > -- > Joshua Wiley > Ph.D. Student, Health Psychology > University of California, Los Angeles > http://joshuawiley.com/ > Senior Analyst - Elkhart Group Ltd. > http://elkhartgroup.com > > ______________________________________________ > 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, ACERA Department of Mathematics and Statistics Tel: +61-3-8344-6410 University of Melbourne, VIC 3010 Australia (prefer email) http://www.ms.unimelb.edu.au/~andrewpr Fax: +61-3-8344-4599 http://www.acera.unimelb.edu.au/ Methods of Statistical Model Estimation (CRC, 2013) http://www.crcpress.com/product/isbn/9781439858028 Forest Analytics with R (Springer, 2011) http://www.ms.unimelb.edu.au/FAwR/ Introduction to Scientific Programming and Simulation using R (CRC, 2009): 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.
