On Wed, 3 Dec 2008, axionator wrote:
Hi all,
I have a data frame with "clustered" rows as follows:
Cu1 x1 y1 z1 ...
Cu1 x2 y2 z2 ...
Cu1 x3 y3 z3 ... # end of first cluster Cu1
Cu2 x4 y4 z4 ...
Cu2 x5 y5 z5
Cu2 ... # end of second cluster Cu2
Cu3 ...
...
"cluster"-size is 3 in the example above (rows making up a cluster are
always consecutive). Is there any faster way to sample n clusters
(with replacement) from this dataframe and build up a new data frame
out of these sampled clusters? I use the "sample" function and a
for-loop.
Something like this:
cl.samps <- sample( split( df, df$cluster ), n.samps, repl=TRUE )
do.call( rbind, cl.samps )
If you need to identify the samples from which the rows came (versus just
the originating clusters):
cl.samps2 <- lapply( seq(along=cl.samps),
function(x) cbind( cl.samps[[ x ]], new.cluster = x ) )
do.call( rbind, cl.samps2 )
HTH,
Chuck
Thanks in advance
Armin
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:[EMAIL PROTECTED] UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
