[R] nls & fitting
Joerg van den Hoff
j.van_den_hoff at fz-rossendorf.de
Mon May 22 09:57:26 CEST 2006
Lorenzo Isella wrote:
> Dear All,
> I may look ridiculous, but I am puzzled at the behavior of the nls with
> a fitting I am currently dealing with.
> My data are:
>
> x N
> 1 346.4102 145.428256
> 2 447.2136 169.530634
> 3 570.0877 144.081627
> 4 721.1103 106.363316
> 5 894.4272 130.390552
> 6 1264.9111 36.727069
> 7 1788.8544 52.848587
> 8 2449.4897 25.128742
> 9 3464.1016 7.531766
> 10 4472.1360 8.827367
> 11 6123.7244 6.600603
> 12 8660.2540 4.083339
>
> I would like to fit N as a function of x according to a function
> depending on 9 parameters (A1,A2,A3,mu1,mu2,mu3,myvar1,myvar2,myvar3),
> namely
> N ~
> (log(10)*A1/sqrt(2*pi)/log(myvar1)*exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1))
>
> +log(10)*A2/sqrt(2*pi)/log(myvar2)*exp(-((log(x/mu2))^2)/2/log(myvar2)/log(myvar2))
>
> +log(10)*A3/sqrt(2*pi)/log(myvar3)*exp(-((log(x/mu3))^2)/2/log(myvar3)/log(myvar3)))
>
> (i.e. N is to be seen as a sum of three "bells" whose parameters I need
> to determine).
>
>
> So I tried:
> out<-nls(N ~
> (log(10)*A1/sqrt(2*pi)/log(myvar1)*exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1))
>
> +log(10)*A2/sqrt(2*pi)/log(myvar2)*exp(-((log(x/mu2))^2)/2/log(myvar2)/log(myvar2))
>
> +log(10)*A3/sqrt(2*pi)/log(myvar3)*exp(-((log(x/mu3))^2)/2/log(myvar3)/log(myvar3)))
> ,start=list(A1 = 85,
> A2=23,A3=4,mu1=430,mu2=1670,mu3=4900,myvar1=1.59,myvar2=1.5,myvar3=1.5 )
> ,algorithm = "port"
> ,control=list(maxiter=20000,tol=10000)
> ,lower=c(A1=0.1,A2=0.1,A3=0.1,mu1=0.1,mu2=0.1,mu3=0.1,myvar1=0.1,myvar2=0.1,myvar3=0.1)
> )
>
> getting the error message:
> Error in nls(N ~ (log(10) * A1/sqrt(2 * pi)/log(myvar1) *
> exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1)) + :
> Convergence failure: singular convergence (7)
>
>
> I tried to adjust tol & maxiter, but unsuccessfully.
> If I try fitting N with only two "bells", then nls works:
>
> out<-nls(N ~
> (log(10)*A1/sqrt(2*pi)/log(myvar1)*exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1))
>
> +log(10)*A2/sqrt(2*pi)/log(myvar2)*exp(-((log(x/mu2))^2)/2/log(myvar2)/log(myvar2))
> )
> ,start=list(A1 = 85, A2=23,mu1=430,mu2=1670,myvar1=1.59,myvar2=1.5 )
> ,algorithm = "port"
> ,control=list(maxiter=20000,tol=10000)
> ,lower=c(A1=0.1,A2=0.1,mu1=0.1,mu2=0.1,myvar1=0.1,myvar2=0.1)
> )
>
> out
> Nonlinear regression model
> model: N ~ (log(10) * A1/sqrt(2 * pi)/log(myvar1) *
> exp(-((log(x/mu1))^2)/2/log(myvar1)/log(myvar1)) + log(10) *
> A2/sqrt(2 * pi)/log(myvar2) *
> exp(-((log(x/mu2))^2)/2/log(myvar2)/log(myvar2)))
> data: parent.frame()
> A1 A2 mu1 mu2 myvar1 myvar2
> 84.920085 40.889968 409.656404 933.081936 1.811560 2.389215
> residual sum-of-squares: 2394.876
>
> Any idea about how to get nls working with the whole model?
> I had better luck with the nls.lm package, but it does not allow to
> introduce any constrain on my fitting parameters.
> I was also suggested to try other packages like optim to do the same
> fitting, but I am a bit unsure about how to set up the problem.
> Any suggestions? BTW, I am working with R Version 2.2.1
>
> Lorenzo
>
> ______________________________________________
> R-help at 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
apart from the fact that fitting 9 parameters to 12 points quite
genereally will not yield satisfactory results (at least estimates will
have huge uncertainties), total failure in your case seems obviouus if
you plot your data: it's not even obvious where the three peaks (means
of the gaussians) should be: all below x=2000 or is there one peak at
about x=4500 and one of the 'peaks' below x=2000 is spurious? if you
can't decide, nls has problems. moreover: how should reliable estimates
of the standard deviations (width) of the gaussian result if the peaks
essentially consist of exactly one point?
in short: I believe, you either need much more data points or you must
put in substantial a priori information (e.g. either means or standard
deviations of the gaussians).
More information about the R-help
mailing list