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.
