[EMAIL PROTECTED] wrote
[that he has a data set with 9 variables (columns) measured on 2000
individuals (rows) and wants a sample] in which the sum of the
volume of the individuals in that sample >= 100 cubic m.
Let's suppose that this information is held in d, a data frame, and that
the volume column is d$vol.
If sum(d$vol) < 100, there is no sample which satisfies your condition.
If sum(d$vol) >= 100, then d is such a sample as it stands.
If you want the smallest number of rows, then
indices <- order(d$vol, decreasing=TRUE)
gives you the row indices sorted by decreasing volume;
d$vol[indices] => the volumes in decreasing order
cumsum(") => the cumulative sum
sum(" < 100.0) => 1 less than then number of rows you want
so
indices <- order(d$vol, decreasing=TRUE)
d[indices[1:(sum(cumsum(d$vol[indices]) < 100.0) + 1)]]
should be the answer you want.
This is O(n.lg n) where n is the number of rows; in your case n is 2000.
If you don't need the smallest sample, but just any old haphazard answer,
indices <- sample(nrow(d))
d[indices[1:(sum(cumsum(d$vol[indices]) < 100.0) + 1)]]
should be useful.
______________________________________________
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
