Contents
Why does exponential regression with NLS not work?
The problem seems to be in your data. If you try plot ( y ~ x ) then it does not look exponential: indeed it seems to be slightly faster than linear, especially if you see that there are in fact two points in your data with the x y values 3.710225 7000 so you are trying to fit a convex curve to data which suggests a concave curve. Two suggestions:
How is exponential regression different from linear regression?
Exponential regression is the process of finding the equation of the exponential function ( y = a b x form where a ≠ 0) that fits best for a set of data. In linear regression, we try to find y = b + m x that fits best data. So, exponential regression is non-linear.
When to use a nonlinear or linear regression model?
Now, we’ll focus on the “non” in nonlinear! If a regression equation doesn’t follow the rules for a linear model, then it must be a nonlinear model. It’s that simple! A nonlinear model is literally not linear. The added flexibility opens the door to a huge number of possible forms.
Can you use Levenberg Marquardt for non linear regression?
There is not enough information in your dataset to estimate the coefficient of this non-linear model reliably. There is practically no curvature in your data. If you use Levenberg-Marquardt it gets stuck with parameter values that are more or less equal to the linear fit:
Is it possible to fit exponential decay with NLS?
Trying to fit the exponential decay with nls however leads to sadness and disappointment if you pick a bad initial guess for the rate constant ( α ). This code:
How is the NLS function used in rdocumentation?
The nls function uses a relative-offset convergence criterion that compares the numerical imprecision at the current parameter estimates to the residual sum-of-squares. This performs well on data of the form $$y=f (x, heta) + \\epsilon$$ (with var (eps) > 0 ).
Can a NLS function be used on zero residual data?
To use these is deprecated, as they are available from convInfo now. Do not use nls on artificial “zero-residual” data. The nls function uses a relative-offset convergence criterion that compares the numerical imprecision at the current parameter estimates to the residual sum-of-squares.