[R] number to volume weighted distribution

PIKAL Petr petr.pikal at precheza.cz
Mon Nov 27 10:58:36 CET 2017


Hi Thierry

Thanks for clarifiyng. I spoke about using ecdf because it gives me possibility to calculate proportion of objects below some threshold (quantiles) quite easily.

quantInv <- function(distr, value) ecdf(distr)(value)

> quantInv(x,80)*100
[1] 57.14286

I believe that such value could be calculated from this cumsum construction too. However with real data (hundereds of "x" values), Ecdf and wtd.quantile works quite good so I can live without it (I do not want to reinvent wheel).

Thanks again.
Cheers
Petr


> -----Original Message-----
> From: Thierry Onkelinx [mailto:thierry.onkelinx at inbo.be]
> Sent: Friday, November 24, 2017 10:02 PM
> To: PIKAL Petr <petr.pikal at precheza.cz>
> Cc: Duncan Murdoch <murdoch.duncan at gmail.com>; r-help at r-project.org
> Subject: Re: [R] number to volume weighted distribution
>
> Hi Petr,
>
> I think that Duncan suggests something like this:
>
> x<- c(rep(10,20), rep(300,5), rep(100, 10)) tx <- table(x)
>
> prop.x <- tx / sum(tx)
> vx <- as.integer(names(tx))
> prop.wx <- tx * vx / sum(tx * vx)
>
> plot(ecdf(x))
> plot(vx, cumsum(prop.x), ylim = 0:1)
> plot(vx, cumsum(prop.wx), ylim = 0:1)
>
> Best regards,
>
> Thierry
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN
> BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team
> Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx at inbo.be Kliniekstraat 25, B-1070 Brussel www.inbo.be
>
>
>
>
> 2017-11-24 14:30 GMT+01:00 PIKAL Petr <petr.pikal at precheza.cz>:
> > Hi Duncan
> >
> > I tried Ecdf and/or wtd.quantile from Hmisc and it is working (probably).
> > Ecdf(x, q=.5)
> > Ecdf(x, weights=xw,col=2, add=T, q=.5)
> > wtd.quantile(x)
> >   0%  25%  50%  75% 100%
> >   10   10   10  100  300
> > wtd.quantile(x, weights=xw, type="i/n")
> >       0%      25%      50%      75%     100%
> >  10.0000 138.8667 192.5778 246.2889 300.0000
> >
> > But could you please be more specific in this?
> >
> >> But you seem to be asking about a
> >> definition rather than a function:  it is obtained simply by
> >> normalizing the weights to sum to 1, then evaluating cumulative sums of
> them.
> >
> > Actually, when I correctly calculate weights
> >
> > xw <- (x^3)/sum(x^3)
> > sum(xw) equals to 1
> >
> > but how can I plot ecdf with volume weighted data. Obviously
> >
> > ecdf(x*xw) or ecdf(x^3*xw) is not correct.
> >
> > Cheers
> > Petr
> >
> >> -----Original Message-----
> >> From: Duncan Murdoch [mailto:murdoch.duncan at gmail.com]
> >> Sent: Friday, November 24, 2017 12:36 PM
> >> To: PIKAL Petr <petr.pikal at precheza.cz>; r-help at r-project.org
> >> Subject: Re: [R] number to volume weighted distribution
> >>
> >> On 24/11/2017 6:27 AM, PIKAL Petr wrote:
> >> > Dear all
> >> >
> >> > Strictly speaking it is not R question but as you are the most
> >> > capable persons I
> >> know I give it a try.
> >> >
> >> > I am strugling with recalculation of number weighted to volume
> >> > weighted
> >> distribution.
> >> >
> >> > Suppose I have objects (cubes) with size
> >> >
> >> > x<- c(rep(10,20), rep(100, 10), rep(300,5)) I can get
> >> >
> >> > plot(ecdf(x))
> >> >
> >> > or the number weighted average
> >> >
> >> > mean(x)
> >> > [1] 77.14286
> >> >
> >> > or volume weighted average
> >> > weighted.mean(x, (x/sum(x^3)))
> >> > [1] 204.4444
> >> >
> >> > However I am struggling with volume weighted ecdf.
> >> >
> >> > Can you please give me some hints?
> >>
> >> I believe base R doesn't have a function for this, but Google says it
> >> exists in a couple of packages:  spatstat, Hmisc.  But you seem to be
> >> asking about a definition rather than a function:  it is obtained
> >> simply by normalizing the weights to sum to 1, then evaluating cumulative
> sums of them.
> >>
> >> Duncan Murdoch
> >

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