[R] A random number from any distribution?
Greg Snow
Greg.Snow at imail.org
Mon Dec 14 21:44:57 CET 2009
Look at the logspline package for an alternative.
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of ivan popivanov
> Sent: Saturday, December 12, 2009 7:38 PM
> To: r-help at r-project.org
> Subject: [R] A random number from any distribution?
>
>
> Hello,
>
> I have some data, and I want to generate random numbers following the
> distribution of this data (in other words, to generate a synthetic data
> set sharing the same stats as a given data set). Reading an old thread
> I found the following text:
>
> >If you can compute the quantile function of the distribution (i.e.,
> the
> >inverse of the integral of the pdf), then you can use the probability
> >integral transform: If U is a U(0,1) random variable and Q is the
> quantile
> >function of the distribution F, then Q(U) is a random variable
> distributed
> >as F.
>
> That sounds good, but is there a quick way to do this in R? Let's say
> my data is contained in "ee", I can get the quantiles using:
>
> qq = quantile(ee, probs=(0,1,0.25))
> 0% 25% 50% 75% 100%
> -0.2573385519 -0.0041451053 0.0004538924 0.0049276991 0.1037823292
>
> Then I "know" how to use the above method to generate Q(U) (by looking
> up U in the first row, and then mapping it to a number using the second
> row), but is there an R function that does that? Otherwise I need to
> write my own to lookup the table.
>
> Thanks in advance,
> Ivan
>
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