Dear Helpers, I need to fit a gamma function on a distribution. I want to use the Method of the Least Squares for minimizing the sum of squared residuals (SSE). I don't know how to do this. I guess I need to calculate the best fit parameter values and then somehow comparing my empirical distribution with the theorical one (in this case gamma). This is what I've done so far:
"rate" is the name of my distribution (12000 data point) mean<- mean(rate) # mean of the empirical distribution var <-var(rate) # variance of the empirical distribution l.est <- mean/var # lambda estimate (scale param.) a.est <- (mean^2)/var # alfa estimate (shape param.) should I create a random gamma distribution and then comparing it with my data? But how? And then Is there a way to visualize in a graph the fitting with gamma? Thanks a lot for your help!! Alessandra -- View this message in context: http://www.nabble.com/Least-Squares-Method-tp23872037p23872037.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
