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 [[alternative HTML version deleted]] ______________________________________________
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