Small data sets (6-12 values, or a similarly small number of groups) which
don't look nice and symmetric are quite common in my field (analytical
chemistry and biological variants thereof), and often contain outliers or at
least stragglers that I cannot simply discard. One of the things I occasionally
do when I want to see what different assumptions do to my confidence intervals
is to run a quick nonparametric bootstrap, just to get a feel for how
asymmetric the distribution of any estimates might be. At the moment, I'm also
interested in doing that on some historical data to evaluate some proposed
estimators for interlab studies.
boot() is pretty good, but it's obvious that with such small sets, there aren't
really many distinct resampled combinations (eg 92378 for 10 data points). So
I'm really resampling from quite a small population of possible bootstrap
samples. Its surely more efficient to generate all the different (resampled)
combinations of the data set, and use those and their frequencies to get things
like the bootstrap variance exactly. At worst, that'll stop us fooling
ourselves into thinking more replicates will get better info.
A lengthy dig around R-help and CRAN turned up a blank on generating distinct
combinations with resampling, so I've written a couple of routines to generate
the distinct combinations and their frequencies. (They work, though I wouldn't
guarantee great efficiency). But if a chemist (me) can think of it, its pretty
certain that a statistician already has. Before I spend hours polishing code,
is there already something out there I've missed?
Steve Ellison
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