This sounds very much like a recursive problem: something like this seems to
get the gist of what you want.
DataSplits <- function(Data, alpha = 0.05) {
DataSplitsCore <- function(Data, alpha, level) {
tt <- t.test(Data[,1],Data[,2])
print(tt)
if (tt$p.value > alpha) {
print(paste("Stopped at level", level))
return(invisible(TRUE))
} else {
nr = floor(NROW(Data)/2)
if (nr == 1) {print(paste("Reached Samples of Size 1")); stop}
d1 = DataSplitsCore(Data[(1:nr),], alpha = alpha, level = level
+ 1)
if (d1) return(invisible(TRUE))
d2 = DataSplitsCore(Data[-(1:nr),], alpha = alpha, level = level
+1)
if (d2) return(invisible(TRUE))
return(invisible(FALSE))
}
}
DataSplitsCore(Data, alpha = alpha, level = 1)
}
Your description wasn't the clearest about what to do when the data sizes
didn't match, but this should give you a start. Let me know if this doesn't
do as desired and I can help tweak it.
Hope this can be of help,
Michael Weylandt
PS -- You might as well use R's built in t.test function.
On Thu, Aug 11, 2011 at 5:17 AM, Marina de Wolff
<marinadewo...@hotmail.com>wrote:
>
> I want to implement the following algorithm in R:
>
> I want to split my data, use a t test to compare both means of the groups
> to see if they significantly differ from each other. If this is a yes (p <
> alpha) I want to split again (into 4 groups) and do the same procedure
> twice, and stop otherwise (here the problem arises). As a final result I
> would have different groups of data.
>
> I made some code where the data is splitted, until no splitting is
> possible. So for 16 datapoints, we can split 4 times with a final result of
> 16 groups (p is NA for the 4th split since sd cannot be calculated..).
>
> The code calculated all p values, but I don't want this. I want it to stop
> when p > alpha. I tried while, but didn't succeed.
>
> I hope someone can help me to acchieve my goal.
>
> This is what I tried so far with test data:
>
> a = rnorm(9,0,0.1)
> b = rnorm(7,1,0.1)
> data = c(a,b)
> plot(data)
>
> # Want to calculate max of groups/split for the data
> d = seq(1,100,1)
> n = 2^d
> m <- which(n <=length(data))
> n = n[m[1]:m[length(m)]]
>
> # All groups
> i=0
> j=0
> dx = 0
> dy =
> for (i in 1:length(n)){
> split <- length(data)/(n[i])
> for (j in 1:(n[i]/2)){
> x = data[(1 + (j-1)*(2*split)):(round(split) + (j-1)*(2*split))]
> dx = cbind(dx,x)
> y = data[((round(split)+1) + (j-1)*(2*split)):(2*j*split)]
> dy = cbind(dy,y)
> }}
>
> dx = dx[,2:dim(dx)[2]]
> dy = dy[,2:dim(dy)[2]]
>
> k=0
> meanx=0
> meany=0
> sdx=0
> sdy=0
> nx=0
> ny=0
> for (k in 1:dim(dx)[2]) {
> meanx[k] = mean(unique(dx[,k]))
> meany[k] = mean(unique(dy[,k]))
> sdx[k] = sd(unique(dx[,k]))
> sdy[k] = sd(unique(dy[,k]))
> nx[k] = length(unique(dx[,k]))
> ny[k] = length(unique(dy[,k]))
> }
>
> t = (meanx-meany)/sqrt((sdx^2/nx) + (sdy^2/ny))
> df = ((sdx^2/nx) + (sdy^2/ny))^2/((sdx^2/nx)^2/(nx-1) +
> (sdy^2/ny)^2/(ny-1))
> p = 2*pt(-abs(t),df=df)
> alpha = 0.05
> [[alternative HTML version deleted]]
>
> ______________________________________________
> 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.
>
[[alternative HTML version deleted]]
______________________________________________
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.
