R help - Sep 2005 - Strange Result using weightedMedian

Dear all,
I found a strange result using R's weightedMedian function.
Consider the following:
> x <- c (0.2, 0.3, 0.5)
> w <- c (1,1,2)
> weightedMedian(x,w)
> 0.3666
In cases like above, when the weights are integers, one could argue that  
the weighted
median should be the same as the standard median with the elements  
repeated according to their weights. This is trivially true for the mean.
In the example above, we simply double the occurrence of the 0.5 entry
> x1 <- c(0.2, 0.3, 0.5, 0.5)
> median(x1)  0.4
Does anyone know the answer to that inconsistency?
It must have to do with the interpolated version.
If you switch of the interpolation you get:
> weightedMedian(x,w,interpolate=FALSE)
> 0.4
However, I prefer the interpolated version since it is continuous with  
respect to the weights. Is there a interpolated version of the  
weightedMedian which does not show this inconsistency?


All the best,
  Oliver

Oliver Duerr <Oliver.Duerr <at> genedata.com> writes:
> 
> Dear all,
> I found a strange result using R's weightedMedian function
....

I could not find weightedMedian on CRAN; I know there is such a function in 
some Java lib, googling gave a reference to limma (Linear Models for Microarray 
Data by Gordon Smyth and others).

Best contact the authors about the subject.

Dieter

weightedMedian is in the R.basic packag.
To download see:
See http://www.maths.lth.se/help/R/R.classes/#1.%20Introduction

Best,
  Oliver

Oliver Duerr wrote:
> Dear all,
> I found a strange result using R's weightedMedian function.
> Consider the following:
> 
> 
>>x <- c (0.2, 0.3, 0.5)
>>w <- c (1,1,2)
>>weightedMedian(x,w)
>>0.3666
> 
> 
> In cases like above, when the weights are integers, one could argue that  
> the weighted
> median should be the same as the standard median with the elements  
> repeated according to their weights. This is trivially true for the mean.
> In the example above, we simply double the occurrence of the 0.5 entry
> 
> 
>>x1 <- c(0.2, 0.3, 0.5, 0.5)
>>median(x1)  0.4
> 
> 
> Does anyone know the answer to that inconsistency?
> It must have to do with the interpolated version.
> If you switch of the interpolation you get:
> 
>>weightedMedian(x,w,interpolate=FALSE)
>>0.4
> 
> 
> However, I prefer the interpolated version since it is continuous with  
> respect to the weights. Is there a interpolated version of the  
> weightedMedian which does not show this inconsistency?
> 
> 
> All the best,
>   Oliver
By the way:

 > library(Hmisc)
 > wtd.quantile(x,w)
    0%   25%   50%   75%  100%
0.200 0.275 0.400 0.500 0.500

Also see the type argument to wtd.quantile

-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University