[R] Fit model to data and use model for data generation
Roberto Perdisci
roberto.perdisci at gmail.com
Thu Jan 25 16:13:46 CET 2007
On 1/25/07, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
> That gives a discrete distribution, which may well matter for small
> samples.
>
> Since density() is returning an equal-weighted mixture of (by default)
> normal distributions, all you need to do is
>
> x.new <- rnorm(n, sample(x, size = n, replace=TRUE), bw)
Prof. Ripley,
I didn't understand why you used
sample(x, size = n, replace=TRUE)
I though the mixture should be computed using all the points in x as
means, like in
x.new <- rnorm(n, x, bw)
Could you explain why you propose
x.new <- rnorm(n, sample(x, size = n, replace=TRUE), bw)
instead?
Could you also briefly say in what sense kde is biased?
thank you very much,
best regards,
Roberto
> where bw is the bandwidth used by density (d$bw in this example).
> (This is known as a 'smoothed bootstrap' in some circles.)
>
>
> > ### Create a bimodal distribution
> > x <- c(rnorm(25, -2, 1), rnorm(50, 3, 2))
> > d <- density(x, n = 1000)
> > plot(d)
> >
> > ### Sample from the distribution and show the two
> > ### distributions are the same
> > x.new <- sample(d$x, size = 100000, # large n for proof of concept
> > replace = TRUE, prob = d$y/sum(d$y))
> > dx.new <- density(x.new)
> > lines(dx.new$x, dx.new$y, col = "blue")
>
> BTW, lines(density(x.news), col = "blue") works here, and you do need to
> remember that a kde is biased. But my solution matches better than yours.
>
> --
> Brian D. Ripley, ripley at stats.ox.ac.uk
> Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
> University of Oxford, Tel: +44 1865 272861 (self)
> 1 South Parks Road, +44 1865 272866 (PA)
> Oxford OX1 3TG, UK Fax: +44 1865 272595
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
More information about the R-help
mailing list