I'm rather doubtful that you can improve on the uniform jittering
strategy
you originally considered. It would require intimate knowledge about
the non-uniformity of the density in the spacings between your
quantized version.
But if you really _knew_ the parent distribution
then something l
> 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")
>
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
Another approach:
? jitter
plot(jitter(q, factor=1),type="l")
factor = 1 by default but can get increased so the spaces get filled
in to your satisfaction:
plot(q,type="l"); points( jitter(floor(q), factor=2) ,col="red")
plot(q,type="l"); points( jitter(floor(q), factor=3), col="red")
I su
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 sho
5 matches
Mail list logo