Thanks Bery and Richard.
So how would you suggest to create a histogram function with an added
density plot on top of it?
Here is what I've came up so far (ANY suggestion for improvement will be
welcomed!):
#-------------------------------------------------------------
# function
hist.plus.density <- function(X,..., density_col = "blue", density_lwd = 5,
main)
{
if(missing(main))
{
xname <- paste(deparse(substitute(X), 500), collapse = "\n")
main <- paste("Histogram of", xname)
}
a <- hist(X, main = main , ...)
y <- density(X)$y * max(a$counts / a$density, na.rm = T)
x <- density(X)$x
lines(x = x, y = y, col = density_col, lwd = density_lwd)
return(invisible(a))
}
# example of use:
set.seed(242)
z = rnorm(30)
hist.plus.density(z)
#-------------------------------------------------------------
Thanks,
Tal
----------------Contact
Details:-------------------------------------------------------
Contact me: tal.gal...@gmail.com | 972-52-7275845
Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
www.r-statistics.com (English)
----------------------------------------------------------------------------------------------
On Mon, May 23, 2011 at 6:16 PM, Bert Gunter <gunter.ber...@gene.com> wrote:
> ... Which makes the cumulative (discrete) distribution 1, as it should be.
>
> -- Bert
>
> On Mon, May 23, 2011 at 7:59 AM, Tal Galili <tal.gal...@gmail.com> wrote:
> > Thanks Richard!
> >
> > ----------------Contact
> > Details:-------------------------------------------------------
> > Contact me: tal.gal...@gmail.com | 972-52-7275845
> > Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) |
> > www.r-statistics.com (English)
> >
> ----------------------------------------------------------------------------------------------
> >
> >
> >
> >
> > On Mon, May 23, 2011 at 5:56 PM, <richard.cot...@hsl.gov.uk> wrote:
> >
> >>
> >> > In order to do this I can use the relation between count and density,
> but
> >> I
> >> > would like to know if there is a way for me to predict it upfront.
> >>
> >> In the code for hist.default, you'll see the line
> >>
> >> dens <- counts/(n * diff(breaks))
> >>
> >> > Here is an example:
> >> >
> >> > set.seed(242)
> >> > z = rnorm(30)
> >> > hist_z <- hist(z)
> >> > hist_z$counts / hist_z$density # the relation is 15
> >>
> >> In your example, n is 30, and the breaks are evenly spaced every 0.5.
> >>
> >> Regards,
> >> Richie.
> >>
> >> Mathematical Sciences Unit*
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> > ______________________________________________
> > R-help@r-project.org mailing list
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> > PLEASE do read the posting guide
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>
> --
> "Men by nature long to get on to the ultimate truths, and will often
> be impatient with elementary studies or fight shy of them. If it were
> possible to reach the ultimate truths without the elementary studies
> usually prefixed to them, these would not be preparatory studies but
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>
> -- Maimonides (1135-1204)
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
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