I see nothing here to do with the 'bootstrap', which is sampling with
replacement.
Do you know what you mean exactly by 'randomly sample'? In general the
way to so this is to sample randomly (uniformly, whatever) and reject
samples that do not meet your restriction. For some restrictions there
are more efficient algorithms, but I don't understand yours. (What are
the 'rows'? Do you want to sample rows in space or xy locations? How
come 'dist' is not symmetric?) For some restrictions, an MCMC sampling
scheme is needed, the hard-core spatial point process being a related
example.
On Wed, 3 Sep 2008, Grant Gillis wrote:
Hello List,
I am not sure that I have the correct terminology here (restricted
bootstrap) which may be hampering my archive searches. I have quite a large
spatially autocorrelated data set. I have xy coordinates and the
corresponding pairwise distance matrix (metres) for each row. I would like
to randomly sample some number of rows but restricting samples such that the
distance between them is larger than the threshold of autocorrelation. I
have been been unsuccessfully trying to link the 'sample' function to values
in the distance matrix.
My end goal is to randomly sample M thousand rows of data N thousand times
calculating linear regression coefficients for each sample but am stuck on
taking the initial sample. I believe I can figure out the rest.
Example Question
I would like to radomly sample 3 rows further but withe the restriction that
they are greater than 100m apart
example data:
main data:
y<- c(1, 2, 9, 5, 6)
x<-c( 1, 3, 5, 7, 9)
z<-c(2, 4, 6, 8, 10)
a<-c(3, 9, 6, 4 ,4)
maindata<-cbind(y, x, z, a)
y x x a
[1,] 1 1 1 3
[2,] 2 3 3 9
[3,] 9 5 5 6
[4,] 5 7 7 4
[5,] 6 9 9 4
distance matrix:
row1<-c(0, 123, 567, 89)
row2<-c(98, 0, 345, 543)
row3<-c(765, 90, 0, 987)
row4<-c(654, 8, 99, 0)
dist<-rbind(row1, row2, row3, row4)
[,1] [,2] [,3] [,4]
row1 0 123 567 89
row2 98 0 345 543
row3 765 90 0 987
row4 654 8 99 0
Thanks for all of the help in the past and now
Cheers
Grant
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