[R] nls fitting problems (singularity)

[Douglas Bates]

Often when nls doesn't converge there is a good reason for it.

I'm on a very slow internet connection these days and will not be able 
to look at the data myself but I ask you to bear in mind that, when 
dealing with nonlinear models, there are model/data set combinations for 
which there are no parameter estimates.


Gabor Grothendieck wrote:
> Have a look at optim (which supports a number of different algorithms via
> the method= arg) and segmented in package segmented which does segmented
> regression.
> 
> For example,
> 
> ss <- function(par) {
> 	b <- par[1]; c1 <- par[2]; c2 <- par[3]; d <- par[4]
> 	x <- df1$x; y <- df1$y
> 	sum((y - (d+(x-b)*c1*(x-b<0)+(x-b)*c2*(x-b>=0)))^2)
> }
> optim(sl,ss)
> 
> 
> 
> Karl Knoblick <karlknoblich <at> yahoo.de> writes:
> 
> : 
> : Hallo!
> : 
> : I have a problem with fitting data with nls. The first
> : example with y1 (data frame df1) shows an error, the
> : second works fine.
> : 
> : Is there a possibility to get a fit (e.g. JMP can fit
> : also data I can not manage to fit with R). Sometimes I
> : also got an error singularity with starting
> : parameters.
> : 
> : # x-values
> : x<-c(-1,5,8,11,13,15,16,17,18,19,21,22)
> : # y1-values (first data set)
> : y1=c(-55,-22,-13,-11,-9.7,-1.4,-0.22,5.3,8.5,10,14,20)
> : # y2-values (second data set)
> : y2=c(-92,-42,-15,1.3,2.7,8.7,9.7,13,11,19,18,22)
> : 
> : # data frames
> : df1<-data.frame(x=x, y=y1)
> : df2<-data.frame(x=x, y=y2)
> : 
> : # start list for parameters
> : sl<-list( d=0, b=10, c1=90, c2=20) 
> : 
> : # y1-Analysis - Result: Error in ...  singular
> : gradient
> : nls(y~d+(x-b)*c1*(x-b<0)+(x-b)*c2*(x-b>=0), data=df1,
> : start=sl)
> : # y2-Analysis - Result: working...
> : nls(y~d+(x-b)*c1*(x-b<0)+(x-b)*c2*(x-b>=0), data=df2,
> : start=sl)
> : 
> : # plots to look at data
> : par(mfrow=c(1,2))
> : plot(df1$x,df1$y)
> : plot(df2$x,df2$y)
> : 
> : Perhaps there is another fitting routine? Can anybody
> : help?
> : 
> : Best wishes,
> : Karl
> : 
> : 
> : 	
> : 
> : 	
> : 		
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