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.
You can use ls.str() to look into the function environment:
> ls.str(environment(ecdf(x)))
f : num 0
method : int 2
n : int 25
x : num [1:25] -2.215 -1.989 -0.836 -0.820 -0.626 ...
y : num [1:25] 0.04 0.08 0.12 0.16 0.2 0.24 0.28 0.32 0.36 0.4 ...
yleft : num 0
yright : num 1
You can then use get() or mget() within the function environment to
return the requisite values. Something along the lines of the following
within the function percentrank():
percentrank <- function(x, val)
{
env.x <- environment(ecdf(x))
res <- mget(c("x", "y"), env.x)
Ind <- which(sapply(seq(length(res$x)),
function(i) isTRUE(all.equal(res$x[i], val))))
res$y[Ind]
}
Thus:
set.seed(1)
x <- rnorm(25)
> x
[1] -0.62645381 0.18364332 -0.83562861 1.59528080 0.32950777
[6] -0.82046838 0.48742905 0.73832471 0.57578135 -0.30538839
[11] 1.51178117 0.38984324 -0.62124058 -2.21469989 1.12493092
[16] -0.04493361 -0.01619026 0.94383621 0.82122120 0.59390132
[21] 0.91897737 0.78213630 0.07456498 -1.98935170 0.61982575
> percentrank(x, 0.48742905)
[1] 0.56
One other approach, which returns the values and their respective rank
percentiles is:
> cumsum(prop.table(table(x)))
-2.2146998871775 -1.98935169586337 -0.835628612410047
0.04 0.08 0.12
-0.820468384118015 -0.626453810742333 -0.621240580541804
0.16 0.20 0.24
-0.305388387156356 -0.0449336090152308 -0.0161902630989461
0.28 0.32 0.36
0.0745649833651906 0.183643324222082 0.329507771815361
0.40 0.44 0.48
0.389843236411431 0.487429052428485 0.575781351653492
0.52 0.56 0.60
0.593901321217509 0.61982574789471 0.738324705129217
0.64 0.68 0.72
0.782136300731067 0.821221195098089 0.918977371608218
0.76 0.80 0.84
0.9438362106853 1.12493091814311 1.51178116845085
0.88 0.92 0.96
1.59528080213779
1.00
HTH,
Marc Schwartz
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
