The logspline package has tools for estimating a density function for interval censored data (the old methods), you could use those to estimate the density of your data, then compare that density to the theoretical density.
-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] 801.408.8111 > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of Stavros Macrakis > Sent: Thursday, November 20, 2008 8:43 AM > To: r-help@r-project.org > Subject: [R] Dequantizing > > I have some data measured with a coarsely-quantized clock. Let's say > the real data are > > q<- sort(rexp(100,.5)) > > The quantized form is floor(q), so a simple quantile plot of one > against the other can be calculated using: > > plot(q,type="l"); points(floor(q),col="red") > > which of course shows the characteristic stair-step. I would like to > smooth the quantized form back into an approximation of the underlying > data. > > The simplest approach I can think of adds a uniform random variable of > the size of the quantization: > > plot(q,type="l"); points(floor(q),col="red"); > points(floor(q)+runif(100,0,1),col="blue") > > This gives pretty good results for uniform distributions, but less > good for others (like exponential). Is there a better > interpolation/smoothing function for cases like this, either Monte > Carlo as above or deterministic? > > Thanks, > > -s > > ______________________________________________ > R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
