Somebody already did the job for you. Try fitdistr{MASS} i.e.
x=scan(clipboard)#Read your data from clipboard
sh=(mean(x))^2/var(x)
sc=var(x)/mean(x)
fitdistr(x,gamma, list(shape=sh, scale=sc))
Now you probably know that you have to be carfeul when estimating
distribution parameters from such a small number of observations.
PS: this is a very popular question so in the future before you post a
question try RSiteSearch() i.e. RSiteSearch(fit gamma) gave me 273 hits
Cheers
Francisco
From: Nadja Riedwyl [EMAIL PROTECTED]
To: r-help@stat.math.ethz.ch
Subject: [R] fit data with gammadistribution
Date: Mon, 12 Sep 2005 12:58:00 +0200
hello
my data is
data2:2743 4678 21427 6194 10286 1505 12811 2161 6853 2625 14542
694
11491 14924 28640 17097 2136 5308 3477 91301 11488 3860 64114 14334
by calculating
shape-(mean(data2))^2/var(data2)
scale-var(data2)/mean(data2)
i get the idea what the parameters of the gammadistribution would be.
but if i try using the method mle() i get stock and i don't know, how to
make
it work. can anybody help me? thank you very much, indeed.
Nadja
I tried so fare
ll-function(lambda,alfa)
{n-24
x-data2
-n*alfa*log(lambda)+n*log(gamma(alfa))-(alfa-1)*sum(log(x))+lambda*sum(x)
est-mle(minuslogl=ll,start=list(lambda=29827.51,alfa=0.4954725))
summary(est)
NaN's are produced with optim, i just don't know how to avoid this!
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