Poisson Distribution fitting

I am trying to fit a number of distributions to a set of data, I have used the
fitdristr() funtion for most of the distributions, but Poisson is not one of
the possible distributions. I found somewhee talking about using the gamlss
package, but have been unable to find it again, any help would be greatly
appreciated.

x <- rpois(250, 2.6)
mean(x)

fitdistr(x, dpois, list(lambda=2))

mean(x)/sqrt(250)

On Thu, 25 Aug 2005, Mark Miller wrote:

I am trying to fit a number of distributions to a set of data, I have used 
the
fitdristr() funtion for most of the distributions, but Poisson is not one 
of
the possible distributions. I found somewhee talking about using the gamlss
package, but have been unable to find it again, any help would be greatly
appreciated.

If you mean fitdistr in package MASS, it can be used.  But the mle for a 
Poisson is just the sample mean.

x <- rpois(250, 2.6)
mean(x)

[1] 2.62

fitdistr(x, dpois, list(lambda=2))

   lambda
 2.6203125
(0.1023841)

You can even find the s.e. via mu/sqrt(n), here

mean(x)/sqrt(250)

[1] 0.1657033

I was wandering if anyone cold advise me on a good algorithm to turn a set of
data from its original for into its cumulative form.  I have written a piece
of code that takes the data and does essentially what a histogram function
would do, except add to the new bin the sum in the previous bin.  Once that
is done I divide by the frequency in the last bin plus 1.  I know the ecdf()
function exists in R, but I want to use it to fit the cumulative
distributions to the data and ecdf() produces a non-subscriptable vector and
so fitdistr() cannot be used on it.

Thanks for any help you can give
Mark Miller