Greg.Snow at imail.org
Thu Nov 20 18:21:23 CET 2008
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
greg.snow at imail.org
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Stavros Macrakis
> Sent: Thursday, November 20, 2008 8:43 AM
> To: r-help at 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
> 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");
> 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?
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