I noticed that nls treats weights as relative and that the absolute size of the
weights w in
the following script has therefore no influence on the errors of the parameters
reported in the summary
a<-1
b<-3
x<--100:100
y<-a*x+b
yeps<-y+rnorm(length(x),sd=1)
w<-rep(1,length(x))
plot(x,yeps)
lines(x,y)
fit<-nls(yeps~p1*x+p2,start=list(p1=a*1.5,p2=b*1.5),weights=w)
summary(fit)
What is the basic idea behind this behavior which is counterintuitive to me?
The weights in my example are estimates of the absolute measurement error
and I think that scaling the weights by a factor of ten should result in
parameter errors ten times as large.
How do I achieve this behaviour?
Kind regards
Florian Hengstberger
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