Re: [R] Computing a CDF or many quantiles
Also look at ecdf() from package "stepfun". HTH, Jerome Asselin __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Computing a CDF or many quantiles
On September 10, 2003 04:03 pm, Kevin S. Van Horn wrote:
>
> Your method looks like a naive reimplementation of integration, and
> won't work so well for distributions that have the great majority of the
> probability mass concentrated in a small fraction of the sample space.
> I was hoping for something that would retain the adaptability of
> integrate().
Yesterday, I've suggested to use approxfun(). Did you consider my
suggestion? Below is an example.
N <- 500
x <- rexp(N)
y <- rank(x)/(N+1)
empCDF <- approxfun(x,y)
xvals <- seq(0,4,.01)
plot(xvals,empCDF(xvals),type="l",
xlab="Quantile",ylab="Cumulative Distribution Function")
lines(xvals,pexp(xvals),lty=2)
legend(2,.4,c("Empirical CDF","Exact CDF"),lty=1:2)
It's possible to tune in some parameters in approxfun() to better match
your personal preferences. Have a look at help(approxfun) for details.
HTH,
Jerome Asselin
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Re: [R] Computing a CDF or many quantiles
Your method looks like a naive reimplementation of integration, and
won't work so well for distributions that have the great majority of the
probability mass concentrated in a small fraction of the sample space.
I was hoping for something that would retain the adaptability of
integrate().
(Ted Harding) wrote:
If that's all you want to do, then a very straightfoward approach should
be OK. I illustrate with a truncated normal distribution on [-1,1]:
x <- (-1)+(0.001*(0:2000));pdf<-dnorm(x); pdf<-pdf/(sum(pdf)*0.001)
CDF<-cumsum(pdf)*0.001
plot(x,pdf,ylim=c(0,1),type="l");lines(x,CDF)
Quantiles:
N=10;e<-CDF[1];
for(i in (0:10)){
j<-max(which(CDF<=i/N+e));print(c(x[j],CDF[j]))
}
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RE: [R] Computing a CDF or many quantiles
On 09-Sep-03 Kevin S. Van Horn wrote:
> Given f, a pdf over a finite interval, is there any existing R function
> that
> can efficiently tabulate the cumulative distribution function for f, or
> produce all N+1 quantiles of the form i/N? "Efficiently" here means
> better than doing repeated integrations for each point.
If that's all you want to do, then a very straightfoward approach should
be OK. I illustrate with a truncated normal distribution on [-1,1]:
x <- (-1)+(0.001*(0:2000));pdf<-dnorm(x); pdf<-pdf/(sum(pdf)*0.001)
CDF<-cumsum(pdf)*0.001
plot(x,pdf,ylim=c(0,1),type="l");lines(x,CDF)
Quantiles:
N=10;e<-CDF[1];
for(i in (0:10)){
j<-max(which(CDF<=i/N+e));print(c(x[j],CDF[j]))
}
[1] -1.00 0.0003543119
[1] -0.75000.09992753
[1] -0.539 0.132
[1] -0.350 0.2999169
[1] -0.172 0.4003052
[1] 0.000 0.5002921
[1] 0.172 0.6002703
[1] 0.349 0.7000831
[1] 0.538 0.868
[1] 0.749 0.9000725
[1] 1 1
(a bit approximate here owing to not adopting a slightly more subtle
approach to the first step).
Tabulation should be extremely straightforward.
Is this what you mean?
Best wishes,
Ted.
E-Mail: (Ted Harding) <[EMAIL PROTECTED]>
Fax-to-email: +44 (0)870 167 1972
Date: 09-Sep-03 Time: 23:28:53
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Re: [R] Computing a CDF or many quantiles
Perhaps approx() or approxfun() can help you create an efficient CDF. HTH, Jerome On September 9, 2003 01:38 pm, Kevin S. Van Horn wrote: > Content-Length: 417 > Status: R > X-Status: N > > Given f, a pdf over a finite interval, is there any existing R function > that can efficiently tabulate the cumulative distribution function for > f, or produce all N+1 quantiles of the form i/N? "Efficiently" here > means better than doing repeated integrations for each point. > > __ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
[R] Computing a CDF or many quantiles
Given f, a pdf over a finite interval, is there any existing R function that can efficiently tabulate the cumulative distribution function for f, or produce all N+1 quantiles of the form i/N? "Efficiently" here means better than doing repeated integrations for each point. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
