R help - Mar 2003 - density(): obtaining p-values

Dear R-List-Member,

is there a more elegant way to obtain p-values of a vector x, whose
empirical density has been estimated with density(), than summing up the
rectangles as an approximation of the area beneath the empirical
distribution function and interpolating the values of x by using approx()?

pval.emp <- function(x)
  {
   df <- density(x,from=min(x),to=max(x),kernel="gaussian")
   width <- df$x[2]-df$x[1]
   rect <- df$y*width
   cdf.emp <- cumsum(rect)
   approx(df$x,cdf.emp,x)$y
  }

Many thks in advance,
Bernhard




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On Mon, 3 Mar 2003 12:40:37 +0100
"Pfaff, Bernhard" <Bernhard.Pfaff at drkw.com> wrote:
> Dear R-List-Member,
> 
> is there a more elegant way to obtain p-values of a vector x, whose
> empirical density has been estimated with density(), than summing up the
> rectangles as an approximation of the area beneath the empirical
> distribution function and interpolating the values of x by using approx()?
> 
> pval.emp <- function(x)
>   {
>    df <- density(x,from=min(x),to=max(x),kernel="gaussian")
>    width <- df$x[2]-df$x[1]
>    rect <- df$y*width
>    cdf.emp <- cumsum(rect)
>    approx(df$x,cdf.emp,x)$y
>   }
> 
> Many thks in advance,
> Bernhard
> 
>
You may want to refer to this just as "probability" and not
"p-values".  It may not be fruitful to fit a density if what you want
is cumulative probabilities.   Just compute the empirical cumulative
distribution function of the original x:

library(stepfun)
ecdf(x)

-- 
Frank E Harrell Jr              Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat