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!
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