library(stats4) ll <- function(shape, rate) { z <- pgamma(breaks, shape=shape, rate=rate) -sum(counts * log(diff(z))) }
mle(ll, start=list(shape=1, rate=1/mean(breaks))) looks a plausible fit. On Tue, 28 Nov 2006, Thomas Petzoldt wrote: > Hello, > > we have a set of biological cell-size data, which are only available as > frequencies of discrete size classes, because of the high effort of > manual microscopic measurements. > > The lengths are approximately gamma distributed, however the shape of > the distribution is relatively variable between different samples (maybe > it's a mixture in reality). > > Is there any ML fitting (or moment-based) procedure for the gamma > distribution and grouped data already available in R? > > Here is a small example: > > breaks <- c(0, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150) > mids <- c(10, 25, 35, 45, 55, 65, 75, 85, 95, 125) > counts <- c(87, 5, 2, 2, 1, 1, 0, 0, 1, 1) > > Any help is highly appreciated > > Thomas P. > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.