Here is a simple approach that uses bootstrapping (this could probably be
improved by using better bootstrap estimates and not ignoring the dependence
between points):
xx <- faithful$eruptions
fit1 <- density(xx)
fit2 <- replicate(10000, { x <- sample(xx, replace=TRUE);
density(x, from=min(fit1$x), to=max(fit1$x))$y } )
fit3 <- apply(fit2, 1, quantile, c(0.025,0.975) )
plot(fit1, ylim=range(fit3))
polygon( c(fit1$x, rev(fit1$x)), c(fit3[1,], rev(fit3[2,])), col='grey',
border=F)
lines(fit1)
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of David Croll
> Sent: Friday, September 03, 2010 9:23 AM
> To: r-help@r-project.org
> Subject: [R] density() with confidence intervals
>
>
>
> Hello R users & R friends,
>
>
> I just want to ask you if density() can produce a confidence interval,
> indicating how "certain" the density() line follows the true frequency
> distribution based on the sample you feed into density().
>
> I've heard of loess.predict(loess(y ~ x), se=TRUE) which gives you a SE
> estimate of the smoothed scatterplot - but density() kernel smoothing
> is not the same as locally-weighted polynomial scatterplot smoothing...
>
>
> Feel free to ask me if I did not put my question into clear words :)
>
>
> Kind regards & thanks in advance,
>
>
> David
> --
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