# [R] Dequantizing

Greg Snow 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
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 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
> 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
>
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