I'm coming late to this, but this *does* need a correction
just for the archives !
>>>>> "MS" == Marc Schwartz <[EMAIL PROTECTED]>
>>>>> on Sat, 01 Dec 2007 13:33:21 -0600 writes:
MS> On Sat, 2007-12-01 at 18:40 +0000, David Winsemius wrote:
>> David Winsemius <[EMAIL PROTECTED]> wrote in
>> news:[EMAIL PROTECTED]:
>>
>> > "tom soyer" <[EMAIL PROTECTED]> wrote in
>> > news:[EMAIL PROTECTED]:
>> >
>> >> John,
>> >>
>> >> The Excel's percentrank function works like this: if one has a number,
>> >> x for example, and one wants to know the percentile of this number in
>> >> a given data set, dataset, one would type =percentrank(dataset,x) in
>> >> Excel to calculate the percentile. So for example, if the data set is
>> >> c(1:10), and one wants to know the percentile of 2.5 in the data set,
>> >> then using the percentrank function one would get 0.166, i.e., 2.5 is
>> >> in the 16.6th percentile.
>> >>
>> >> I am not sure how to program this function in R. I couldn't find it as
>> >> a built-in function in R either. It seems to be an obvious choice for
>> >> a built-in function. I am very surprised, but maybe we both missed it.
>> >
>> > My nomination for a function with a similar result would be ecdf(),
the
>> > empirical cumulative distribution function. It is of class "function"
>> so
>> > efforts to index ecdf(.)[.] failed for me.
I think you did not understand ecdf() !!!
It *returns* a function,
that you can then apply to old (or new) data; see below
MS> You can use ls.str() to look into the function environment:
>> ls.str(environment(ecdf(x)))
MS> f : num 0
MS> method : int 2
MS> n : int 25
MS> x : num [1:25] -2.215 -1.989 -0.836 -0.820 -0.626 ...
MS> y : num [1:25] 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 ...
MS> yleft : num 0
MS> yright : num 1
MS> You can then use get() or mget() within the function environment to
MS> return the requisite values. Something along the lines of the following
MS> within the function percentrank():
MS> percentrank <- function(x, val)
MS> {
MS> env.x <- environment(ecdf(x))
MS> res <- mget(c("x", "y"), env.x)
MS> Ind <- which(sapply(seq(length(res$x)),
MS> function(i) isTRUE(all.equal(res$x[i], val))))
MS> res$y[Ind]
MS> }
sorry Marc, but "Yuck !!"
- this percentrank() only works when you apply it to original x[i] values
- only works for 'val' of length 1
- is a complicated hack
and absolutely unneeded (see below)
MS> Thus:
MS> set.seed(1)
MS> x <- rnorm(25)
>> x
MS> [1] -0.62645381 0.18364332 -0.83562861 1.59528080 0.32950777
MS> [6] -0.82046838 0.48742905 0.73832471 0.57578135 -0.30538839
MS> [11] 1.51178117 0.38984324 -0.62124058 -2.21469989 1.12493092
MS> [16] -0.04493361 -0.01619026 0.94383621 0.82122120 0.59390132
MS> [21] 0.91897737 0.78213630 0.07456498 -1.98935170 0.61982575
>> percentrank(x, 0.48742905)
MS> [1] 0.56
[gives 0.52 in my version of R ]
Well, that is *THE SAME* as using ecdf() the way you
should have used it :
ecdf(x)(0.48742905)
{in two lines, that is
mypercR <- ecdf(x)
mypercR(0.48742905)
which maybe easier to understand, if you have never used the
nice concept that underlies all of
approxfun(), splinefun() or ecdf()
}
You can also use
ecdf(x)(x)
and indeed check that it is identical to the convoluted
percentrank() function above :
> ecdf(x)(0.48742905)
[1] 0.52
> ecdf(x)(x)
[1] 0.20 0.44 0.12 1.00 0.48 0.16 0.56 0.72 0.60 0.28 0.96 0.52 0.24 0.04 0.92
[16] 0.32 0.36 0.88 0.80 0.64 0.84 0.76 0.40 0.08 0.68
> all(ecdf(x)(x) == sapply(x, function(v) percentrank(x,v)))
[1] TRUE
>
Regards (and apologies for my apparent indignation ;-)
by the author of ecdf() ,
Martin Maechler, ETH Zurich
MS> One other approach, which returns the values and their respective rank
MS> percentiles is:
>> cumsum(prop.table(table(x)))
[...... snip ........]
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