graphs of gamma, normal fit to a histogram are about half as large as they should be

In your example it appears that you are plotting a histogram (on the
frequency
scale) and then superimposing scalar multiples of gamma and Gaussian
densities.

You should just plot a histogram (with frequency=FALSE) and then
superimpose the
densities --- without any scalar multipliers.

If that doesn't work, please provide a minimal *reproducible* (no one but
you
has the ``rwb'' data object) example of the problem that you are having
(as the posting guide requests).

    cheers,

        Rolf Turner



On 16/05/11 17:01, Benjamin Caldwell wrote:

Hmm; still missing something - hist defaults to frequencies, not prob.
densities; and, I thought I'd scaled the fitted lines to the values in the
data frame. Just going with it, I specified freq=FALSE, and the prob density
was of course at a different order of magnitude than the lines.

 What are you trying to hint at?


On Fri, May 13, 2011 at 6:05 PM, Rolf Turner <rolf.turner at xtra.co.nz>wrote:

 On 14/05/11 10:00, Benjamin Caldwell wrote:

Hello,

I'm trying to compare the fit of two distributions, normal and gamma, to
a
histogram of my response variable.


rate<-mean(na.omit(rwb$post.f.crwn.length))/var(na.omit(rwb$post.f.crwn.length))
shape<-rate*mean(na.omit(rwb$post.f.crwn.length))
hist((rwb$post.f.crwn.length), main="rwb$post.f.crwn.length")

lines(seq(0.01,70,0.01),length(rwb$post.f.crwn.length)*dgamma(seq(0.01,70,0.01),shape,rate))

lines(seq(0,70,0.1),length(na.omit(rwb$post.f.crwn.length))*dnorm(seq(0,70,.1),mean(na.omit(rwb$post.f.crwn.length)),sqrt(var(na.omit(rwb$post.f.crwn.length))))

However, the height of the two curves are about 1/3 to 1/4 the height
that
they should be compared to the histogram. Any ideas?

 Yes.  Read the help on "hist"!  (Hint:  Pay particular attention to the
"freq" and/or "probability" arguments.)

   cheers,

       Rolf Turner