On Thu, 4 Sep 2008, Grant Gillis wrote:
Hello Professor Ripely,
Sorry for not being clear. I posted after a long day of struggling. Also
my toy distance matrix should have been symmetrical.
Simply put I have spatially autocorrelated data collected from many points.
I would like to do a linear regression on these data. To deal with the
autocrrelation I want to resample a subset of my data with replacement but I
need to restrict subsets such that no two locations where data was collected
are closer than Xm apart (further apart than the autocrrelation in the
data).
That is impossible. Resampling with replacement will give duplicated
locations (with a very high probability) and those have distance zero.
If you want a subsample (necessarily without replacement) you have a
hard-core point process on a discrete set. It's possible that the MCMC
methods we used for Strauss processes can be made to work in that case,
but it is also possible that the state space is reducible and so more
elaborate algorithms are needed.
I do think it would be much easier to take autocorrelation into account in
your linear model fit. There are many ways to do that, e.g. MASS::lm.gls,
and in fact uless the correlations are very high OLS is likely to be quite
efficient (but you need to use e.g. a sandwich estimator to get reliable
standard errors).
Thanks for having a look at this for me. I will look up the hard-core
spatial point process.
Grant
2008/9/4 Prof Brian Ripley <[EMAIL PROTECTED]>
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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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics,
http://www.stats.ox.ac.uk/~ripley/<http://www.stats.ox.ac.uk/%7Eripley/>
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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______________________________________________
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.
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
______________________________________________
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.
