In R I use nls to do a nonlinear least-squares fit. How then do I plot the model function using the values of the coefficients that the fit provided?
(Yes, this is a very naive question from an R relative newbie.)
In R I use nls to do a nonlinear least-squares fit. How then do I plot the model function using the values of the coefficients that the fit provided?
(Yes, this is a very naive question from an R relative newbie.)
I know what you want (I'm a Scientist). This isn't it, but at least shows how to use 'curve' to plot your fitting function over any range, and the curve will be smooth. Using the same data set as above:
or,
or,
or whatever (without setting up a sequence of evaluation points first).
I'm a rudimentary R programmer, so I don't know how to implement (elegantly) something like ReplaceAll ( /. ) in Mathematica that one would use to replace occurrences of the symbolic parameters in the model, with the fitted parameters. This first step works although it looks horrible:
It leaves you with a separate 'model' (as a character string), that you might be able to make use of with the fitted parameters ... cleanly (NOT digging out a, b, c) would simply use nonlinFit ... not sure how though.
The function "curve" will plot functions for you.
Using the first example from
?nls
and following the example I pointed you to line by line achieves the following:The
predict
method for anls
argument is automatically returning the fittedy
values. Alternatively, you add a step and doand pass
yFitted
in the second argument tolines
instead. The result looks like this:Or if you want a "smooth" curve, what you do is to simply repeat this but evaluate the function at more points:
coef(x) returns the coefficients for regression results x.
For example.