A value of r^2 always is based (explicitly or implicitly) on a
comparison of two models, usually nested in the sense that the
model with fewer parameters can be viewed as a specialization
of the model with more parameters. The value of r^2 is then
1 minus the ratio of the smaller to the larger sum of squared
residuals. Thus, there is no such thing as the r^2 from a single
fitted model--one must always think about what model (perhaps an
implicit "null" model) is held out as a standard of comparison.
Once that is determined, the calculation is straightforward, based
on the sums of squared residuals from the two models. This is
particularly appropriate for nls(), which minimizes a sum of squares.
Dave Krantz
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