[R] fitting a gaussian to some x,y data

[Michael Koppelman]

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]] )