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 <[email protected]
> <mailto:[email protected]>> 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
>
>
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