search for: gammadistribut

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*s...

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

I have 'grouped' data, that is in the form of: Interval Median 0-9.9%: -25 10-19.9%: 0 20-29.9%: 3 30-39.9%: 10 40-49.9%: 50 50-59.9%: 200 et cetera and want to fit a three parameter gamma distribution. Does anyone know of an existing routine for doing this (or something similar)? Any help or comment is much appreciated. Regards, M?rten M?rten

Hello all, In the course of trying to implement the CDF of an InverseGammaDistribution, I have run across the need for an igamma() function. Several others have needed this function but the answers I have found so far are not totally clear to me. I'm writing for three reasons: 1) to present a small error in the docs 2) to clarify the approach we are expected to take 3) to r...