Peter,
thanks, very nice, this will work for me... could you also help with setting up
the code to run the on liner "approx(sort(x), seq(0,1,,length(x)), q)$y" on the
rows of a data frame using my example above? So if I cbind z and res,
df<-cbind(z,res)
the "x" in your one liner would be the first 4 column values of each row and
"q" is the last (5fth) column value of each row..
thanks again for all the help, Andras Farkas
On Friday, June 16, 2017 4:58 AM, peter dalgaard <pda...@gmail.com> wrote:
It would depend on which one of the 9 quantile definitions you are using. The
discontinuous ones aren't invertible, and the continuous ones won't be either,
if there are ties in the data.
This said, it should just be a matter of setting up the inverse of a piecewise
linear function. To set ideas, try
x <- rnorm(5)
curve(quantile(x,p), xname="p")
The breakpoints for the default quantiles are n points evenly spread on [0,1],
including the endpoints; i.e., for n=5, (0, .25, .5, .75, 1)
So:
x <- rnorm(5)
br <- seq(0, 1, ,5)
qq <- quantile(x, br) ## actually == sort(x)
pfun <- approxfun(qq, br)
(q <- quantile(x, .1234))
pfun(q)
There are variations, e.g. the one-liner
approx(sort(x), seq(0,1,,length(x)), q)$y
-pd
> On 16 Jun 2017, at 01:56 , Andras Farkas via R-help <r-help@r-project.org>
> wrote:
>
> David,
>
> thanks for the response. In your response the quantile function (if I see
> correctly) runs on the columns versus I need to run it on the rows, which is
> an easy fix, but that is not exactly what I had in mind... essentially we can
> remove t() from my original code to make "res" look like this:
>
> res<-apply(z, 1, quantile, probs=c(0.3))
>
> but after all maybe I did not explain myself clear enough so let me try
> again: the known variables to us in what I am trying to do are the data frame
> "z' :
>
> t<-seq(0,24,1)
> a<-10*exp(-0.05*t)
> b<-10*exp(-0.07*t)
> c<-10*exp(-0.1*t)
> d<-10*exp(-0.03*t)
>
> z<-data.frame(a,b,c,d)
>
> and the vector "res":
>
> res<-c(10.000000, 9.296382, 8.642955, 8.036076 ,7.472374, 6.948723,
> 6.462233, 6.010223 ,5.590211
>
> ,5.199896 ,4.837147, 4.499989 ,4.186589, 3.895250 ,3.624397, 3.372570,
> 3.138415, 2.920675
> , 2.718185 ,2.529864 ,2.354708, 2.191786, 2.040233, 1.899247, 1.768084)
>
> and I need to find the probability (probs) , the unknown value, which would
> result in creating "res", ie: the probs=c(0.3), from:
> res<-apply(z, 1, quantile, probs=c(0.3))...
>
>
> a more simplified example assuming :
>
> k<-c(1:100)
> f<-30
> ecdf(k)(f)
>
> would give us the value of 0.3... so same idea as this, but instead of "k" we
> have data frame "z", and instead of "f" we have "res", and need to find the
> value of 0.3... Does that make sense?
>
> much appreciate the help...
>
> Andras Farkas,
>
>
> On Thursday, June 15, 2017 6:46 PM, David Winsemius <dwinsem...@comcast.net>
> wrote:
>
>
>
>
>> On Jun 15, 2017, at 12:37 PM, Andras Farkas via R-help
>> <r-help@r-project.org> wrote:
>>
>> Dear All,
>>
>> we have:
>>
>> t<-seq(0,24,1)
>> a<-10*exp(-0.05*t)
>> b<-10*exp(-0.07*t)
>> c<-10*exp(-0.1*t)
>> d<-10*exp(-0.03*t)
>> z<-data.frame(a,b,c,d)
>>
>> res<-t(apply(z, 1, quantile, probs=c(0.3)))
>>
>>
>>
>> my goal is to do a 'reverse" of the function here that produces "res" on a
>> data frame, ie: to get the answer 0.3 back for the percentile location when
>> I have "res" available to me... For a single vector this would be done using
>> ecdf something like this:
>>
>> x <- rnorm(100)
>> #then I know this value:
>> quantile(x,0.33)
>> #so do this step
>> ecdf(x)(quantile(x,0.33))
>> #to get 0.33 back...
>>
>> any suggestions on how I could to that for a data frame?
>
> Can't you just used ecdf and quantile ecdf?
>
> # See ?ecdf page for both functions
>
>> lapply( lapply(z, ecdf), quantile, 0.33)
> $a
> 33%
> 4.475758
>
> $b
> 33%
> 3.245151
>
> $c
> 33%
> 2.003595
>
>
> $d
> 33%
> 6.173204
> --
>
> David Winsemius
> Alameda, CA, USA
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: pd....@cbs.dk Priv: pda...@gmail.com
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