On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson wrote:
> Using a data.frame x with columns bins and counts:
>
> x <- structure(list(bins = c(3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5,
> 11.5, 12.5, 13.5, 14.5, 15.5), counts = c(1, 1, 2, 3, 6, 18,
> 19, 23, 8, 10, 6, 2, 1)), .Names = c("bi
Hello,
I wanted to know if there was way to convert a histogram of a data-set to a
kernel density estimate directly in R ?
Specifically, I have a histogram [bins, counts] of samples {X1 ...
XN} of a quantized variable X where there is one bin for each level of X,
and I'ld like to directly get a kd
Hi Michael,
Thanks a lot - enclosing the qplot in a print() worked perfectly !
Regards,
-fj
On Thu, Aug 2, 2012 at 7:37 PM, R. Michael Weylandt <
michael.weyla...@gmail.com> wrote:
> On Thu, Aug 2, 2012 at 6:30 PM, Dennis Murphy wrote:
> > On Thu, Aug 2, 2012 at 1:38 PM, Michael Weylandt
> >
>
> On Aug 2, 2012, at 2:37 PM, "firdaus.janoos"
> wrote:
>
> > Hello,
> >
> > I'm having some issues getting a ggplot figure to show up in the knitr
> > output, when placed in a loop.
> >
> > Specifically, I have a loop inside a knit
Hello,
I'm having some issues getting a ggplot figure to show up in the knitr
output, when placed in a loop.
Specifically, I have a loop inside a knitr chunk :
```{r fitting, warning=FALSE, fig.width=10, fig.height=10, fig.keep='high'}
for (t in 1:T)
{
# do a regression of tgt.vals ~ predi
Hello,
I wanted to know if there is a simple way of getting the inverse cdf for a
KDE estimate of a density (using the ks or KernSmooth packages) in R ?
The method I'm using now is to perform a numerical integration of the pdf
to get the cdf and then doing a search for the desired probablity valu
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