Hi,
I use nls to fit Gaussian curves to datasets that are expected to be
Gaussian-shaped:
gauss.fit = nls(y ~ amp*exp(-0.5*(x-x0)^2/theVariance^2) + theNoise,
data = smooth, start = gauss.fit.start)
Some of these datasets are indeed shaped like Gaussians, while others
are not. I would like to use a goodness of fit metric to assess whether
a Gaussian curve is a good fit to the data. I wonder what metric would
be appropriate for this purpose. I saw some discussions on this list
that suggested that R^2 is not meaningful in the non-linear regression
context, and that is why it's not reported in the nls object. Are there
other, more appropriate goodness of fit measures?
Thanks.
Yury
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