Hi out there,
imagine you have a dataset (x,y) with errors f,
so that each y_i is y_i +- f_i. This is the normal case for almost all
measurements, since one quantity y can only be measured with a certain
accuracy f.
x<-c(1,2,3)
y<-c(1.1,0.8,1.3)
f<-c(0.2,0.2,0.2)
plot(x,y) #whereas every y has the uncertainty of f
If I now perform a nls-fit (and force the data through (0,0) to have
only one fitting parameter)
n<-nls(y~a*x,start=list(a=1))
summary(n)
I end up with an estimate of "a" of 1.4 +- 0.06 as standard error of the
fit.
In this case the error gives only the accuracy of the fit itself, but
does not include the measurement errors in y: f (e.g. error bars).
How is it possible to take them into account as well? I know that there
is the chi-squared test, where the goodness of the fit is calculated,
but this does not include the errors itself.
There should be an easy solution, since this is a common problem in
science, but I haven't found a solution for R yet.
Any suggestions or solutions?
Thanks in advance!
-- Markus
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