Dr Andrew Wilson <[EMAIL PROTECTED]> writes: > 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?
You don't seem to have an x anywhere. Are you making the (apparently not uncommon) mistake of trying to use a program for fitting nonlinear relations by least squares to fit a probability density? If so, look for fitdistr() instead. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
