Prof Brian Ripley wrote:
On Thu, 25 Dec 2008, Oliver Bandel wrote:
....
The apply-functions do bring speed-advantages.
This is not only what I read about it,
I have used the apply-functions and really got
results faster.
The reason is simple: an apply-function does
make in C, what otherwise would be done on the level of R
with for-loops.
Not true of apply(): true of lapply() and hence sapply(). I'll leave
you to check eapply, mapply, rapply, tapply.
So the issue is what is meant by 'the apply() family of functions':
people often mean *apply(), of which apply() is an unusual member, if
one at all.
Conceptually, I think it belongs there. apply(M,1,max) is similar to
tapply(M,row(M),max), etc. The "apply-functions" share a general
split-operate-reassemble set of semantics, and apply _could_ be
implemented as splitting by indices in MARGINS, followed by lapply,
followed by reassembly into a matrix, as in tapply().
In reality, apply() is implemented differently, using aperm() and direct
indexing. This is more efficient, but it shouldn't necessarily change
the way in which we think about it. It is a bit unfortunate that the
most complex mamber of the family has gotten the most basic name, though.
[Historical note: a decade ago lapply was internally a for() loop. I
rewrote it in C in 2000: I also moved apply to C at the same time but it
proved too little an advantage and was reverted. The speed of lapply
comes mainly from reduced memory allocation: for() is also written in C.]
--
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~~~~~~~~~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907
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