First, I would rename your function "samples" to "mksample"
to avoid confusion with the R function "sample".
Next, I would modify the function so that you are returning
a list of samples, instead of a list containing a list of
samples:
mksamples = function(data,num)
lapply(1:num,function(i) sample(data,n,replace=TRUE))
Now, using your example:
set.seed(123)
n = 10
data = rnorm(n=10,mean=5,sd=2)
data[1] = 100
obj = mksamples(data,1000)
We can count the number of the samples in obj which do
not contain the value 100 as
sum(sapply(obj,function(x)!any(x == 100)))
[1] 350
Hope this helps!
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
[email protected]
On Tue, 16 Nov 2010, ufuk beyaztas wrote:
thank you very much for your idea,
if i write code as;
my data name is data.
samples<-function(data,num){
resamples<-lapply(1:num,function(i) sample(data,n,replace=TRUE))
list(resamples=resamples)}
n=10
data<-rnorm(n=10,mean=5,sd=2)
data[1]=100
obj<-samples(data,1000)
i generate 1000 sample, i did not use 'boot'. 100 is a outlier in the data
set and same stuation, some of samples not contain , some of samples contain
once and some of them contain many times. Now can you tell me how i count
how many samples are there not contain any outlier in the 1000 samples?
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