I am trying to fit a rank-frequency distribution with 3 unknowns (a, b and k) to a set of data.
This is my data set: y <- c(37047647,27083970,23944887,22536157,20133224, 20088720,18774883,18415648,17103717,13580739,12350767, 8682289,7496355,7248810,7022120,6396495,6262477,6005496, 5065887,4594147,2853307,2745322,454572,448397,275136,268771) and this is the fit I'm trying to do: nlsfit <- nls(y ~ a * x^k * b^x, start=list(a=5,k=1,b=3)) (It's a Yule distribution.) However, I keep getting: "Error in nls(y ~ a * x^k * b^x, start = list(a = 5, k = 1, b = 3)) : singular gradient" I guess this has something to do with the parameter start values. I was wondering, is there a fully automated way of estimating parameters which doesn't need start values close to the final estimates? I know other programs do it, so is it possible in R? Thanks, Andrew Wilson ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
