Nonlinear regression: Taylor-made start values


In order to fit a non-linear regression model, e.g. with the nls function in R, you usually need to provide start values in order to obtain reasonable fits. Usually, it does not make sense to use generic start values, since they need to be as close as possible to the true parameter values of the given model instance. Hence, automatic procedures for guessing start values from given data are of interest. In this post I present on general idea for such procedures. It's an idea rather than a procedure itself, because it is not guaranteed to be always applicable and still requires some calculation and creativity from its user to obtain a formula or algorithm for a specific model.

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(This was originally posted on my old wordpress blog and not imported in full text.)