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Is the R value used with non linear regression?
Nonlinear regression is an extremely flexible analysis that can fit most any curve that is present in your data. R-squared seems like a very intuitive way to assess the goodness-of-fit for a regression model. Unfortunately, the two just don’t go together. R-squared is invalid for nonlinear regression.
What is r squared in nonlinear regression?
The value R2 quantifies goodness of fit. It is a fraction between 0.0 and 1.0, and has no units. Higher values indicate that the model fits the data better. When R2 equals 0.0, the best-fit curve fits the data no better than a horizontal line going through the mean of all Y values.
How to estimate initial values in non linear models?
When you consider a mathematical model for a phenomenon, you have already assumed a behavior for that phenomenon similar to your model. Therefore, the best way to estimate the values of parameters in your model is to fit some real data on the it.
Is there a strategy that always works in nonlinear least squares?
Thanks you everyone. If there was a strategy that was both good and general — one that always worked – it would already be implemented in every nonlinear least squares program and starting values would be a non-issue.
Are there any good approaches to start values?
For many specific problems or families of problems, some pretty good approaches to starting values exist; some packages come with good start value calculations for specific nonlinear models or with more general approaches that often work but may have to be helped out with more specific functions or direct input of start values.
How to choose initial values for generic fitting function?
Basically my question is for a generic fitting function (could be arbitrarily complicated) which will be nonlinear in the parameters I am trying to estimate, how does one choose the initial values to initialize the fit? I am trying to do nonlinear least squares. Is there any strategy or method? Has this been studied? Any references?