[R] percentage from density()

William Dunlap wdunlap at tibco.com
Sun Jan 29 22:03:55 CET 2012


If v is your original data,
   v <- c(-20, rep(0,98), 20)
why not use
   mean( -20 < v & v < 2)
as your estimate of the probability that v is in (-20,2)?

Estimating a density is like taking the derivative
of a smooth of the empirical distribution function,
so why not eliminate the middleman instead of integrating
the estimated density?  Any difference between the two
methods tells more about the smoothing used than about
the data involved.  (Not that I am any sort of expert
in this matter.)

Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com 
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Greg Snow
> Sent: Saturday, January 28, 2012 8:12 PM
> To: Duke; r-help at r-project.org
> Subject: Re: [R] percentage from density()
> 
> If you use logspline estimation (logspline package) instead of kernel density estimation then this is
> simple as there are cumulative area functions for logspline fits.
> 
> If you need to do this with kernel density estimates then you can just find the area over your region
> for the kernel centered at each data point and average those values together to get the area under the
> entire density estimate.
> 
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Duke
> Sent: Friday, January 27, 2012 3:45 PM
> To: r-help at r-project.org
> Subject: [R] percentage from density()
> 
> Hi folks,
> 
> I know that density function will give a estimated density for a give
> dataset. Now from that I want to have a percentage estimation for a
> certain range. For examle:
> 
>  > y = density(c(-20,rep(0,98),20))
>  > plot(y, xlim=c(-4,4))
> 
> Now if I want to know the percentage of data lying in (-20,2). Basically
> it should be the area of the curve from -20 to 2. Anybody knows a simple
> function to do it?
> 
> Thanks,
> 
> D.
> 
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