You could try something like this:
Loop through your bootstrapped samples and store which ones have the
outlier you are looking for using code like:
count = c(count, outlier.value %in% boot.sample$outlier.variable)
Then subtract the count variable from the total number of samples to
get the number of samples without the outlier
N.nooutlier = Total - count
Andrew Miles
On Nov 16, 2010, at 4:55 PM, ufuk beyaztas wrote:
Hi dear all,
i have a data (data.frame) which contain y and x coloumn(i.e.
y x
1 0.58545723 0.15113102
2 0.02769361 -0.02172165
3 1.00927527 -1.80072610
4 0.56504053 -1.12236685
5 0.58332337 -1.24263981
6 -1.70257274 0.46238255
7 -0.88501561 0.89484429
8 1.14466282 0.34193875
9 0.58827457 0.15923694
10 -0.79532232 -1.44193770 )
i changed the first data points by an outlier (i.e.
y x
1 10 25
2 0.02769361 -0.02172165
3 1.00927527 -1.80072610
4 0.56504053 -1.12236685
5 0.58332337 -1.24263981
6 -1.70257274 0.46238255
7 -0.88501561 0.89484429
8 1.14466282 0.34193875
9 0.58827457 0.15923694
10 -0.79532232 -1.44193770 )
then i generate the 1000 bootstrap sample with this data set, some
of them
not contain these outliers, some of them contain once and some of them
contain many time... Now i want to count how many samples not
contain these
outliers.
Thank so much any idea!
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