Calculating R^2 for a nonlinear least squares fit

Question

Suppose I have x values, y values, and expected y values f (from some nonlinear best fit curve).

How can I compute R^2 in R? N

Answer 1

Sounds like f are your predicted values. So the distance from them to the actual values devided by n * variance of y

so something like

1-sum((y-f)^2)/(length(y)*var(y))

should give you a quasi rsquared value, so long as your model is reasonably close to a linear model and n is pretty big.