I want to preform a weigted least squares fit with LsqFit.jl
The documentation ( Tutorial · LsqFit.jl (julianlsolvers.github.io)) does have a section on it, but no example of curve_fit()
actually being called with a weight-vector. I have tried the following, with one of the last two lines commented out at a time:
using LsqFit
model(ys, p) = @. p[1]*ys+p[2]
xdata = 1:100
ydata = xdata + randn(length(xdata))
wt = xdata
# This line throws a method error
curve_fit(R_model, xdata, ydata, [1.0, 1.0], wt=wt)
# This line throws a dimension error, but seems to misunderstand what I want
curve_fit(R_model, xdata, ydata, [1.0, 1.0], wt)
I don’t know if I am passing the arguments in the wrong way, but I can not find an example for how to do it. Anyone know how?