I apologize if this is redundant. I've been Googling, searching the archive and reading the help all morning and I am not getting closer to my goal.
I have a series of data( xi, yi). It is not evenly sampled and it is messy (meaning that there is a lot of scatter in the data). I want to fit a normal distribution (i.e. a gaussian) to the data in order to find the center. (The data has a loose "normal" look to it.) I have done this with polynomial fitting in R with nls but want to try it with a Gaussian (I am a student astronomer and have not a lot of experience in statistics). In looking at the fitdistr function, it seems to take as input a bunch of x values and it will fit a gaussian to the histogram. That is not what I need to do, I want to fit a normal distribution to the x,y values and get out the parameters of the fit. I'm fooling with nls but it seems to want the data in some other format or something because it is complaining about "singular gradient". I'm sure this isn't hard and the answer must be out there somewhere but I can't find it. I appreciate any assistance. Cheers, Michael filepath <- system.file("data", infile , package="datasets") mm <-read.table(filepath) mm dmk <- data.frame( x=mm$V1, y=mm$V2) attach(dmk) plot(x,y) #ymk <-nls(y~c*x^2+b*x+a,start=list(a=1,b=1,c=1),trace=TRUE) ymk <-nls(y~a*exp(-x^2/sig),start=list(a=1,sig=1),trace=TRUE) co = coef(ymk) cmk <- list(a=co[[1]], b=co[[2]], c=co[[3]] ) ______________________________________________ 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.