On Tue, 14 Apr 2009, S Ellison wrote:
Sorting with an appropriate algorithm is nlog(n), so it's very hard to
get the 'exact' median any faster.
There actually are linear-time algorithms for the median, but n has to be very
large before they are worth using, and by then you have to start consi
There is a slightly faster algorithm in my quantreg package, see
kuantile()
but this is only significant when sample sizes are very large. In
your case
you really need a wrapper that keeps the loop over columns within some
lower level language.
url:www.econ.uiuc.edu/~rogerRog
there is function rowMedians in Bioconductor package Biobase which works
for numeric matrices and might help.
Matthias
Dimitris Rizopoulos wrote:
S Ellison wrote:
Sorting with an appropriate algorithm is nlog(n), so it's very hard to
get the 'exact' median any faster. However, if you can cope
S Ellison wrote:
Sorting with an appropriate algorithm is nlog(n), so it's very hard to
get the 'exact' median any faster. However, if you can cope with a less
precise median, you could use a binary search between max(x) and min(x)
with low tolerance or comparatively few iterations. In native R,
Sorting with an appropriate algorithm is nlog(n), so it's very hard to
get the 'exact' median any faster. However, if you can cope with a less
precise median, you could use a binary search between max(x) and min(x)
with low tolerance or comparatively few iterations. In native R, though,
that isn;t
Hi there,
I got a data frame with more than 200k columns. How could I get median of each
column fast? mapply is the fastest function I know for that, it's not yet
satisfied though.
It seems function "median" in R calculates median by "sort" and "mean". I am
wondering if there is another funct
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