Generalized Nonlinear Non-analytic Chi-Square script download - Generalized Nonlinear Non-analytic Chi-Square Generalized Nonlinear Non-analytic Chi-Squar

Generalized Nonlinear Non-analytic Chi-Square Fitting performs chi-square fit with uncertainty estimation when measurement errors are known. fitChiSquare is a generalized chi-square fitting routine for any model function when data measurement errors are known; it returns the model parameters and their uncertainties at the delta chi-square = 1 boundary (68% confidence interval). It also returns the chi-square and degrees of freedom (dof) of the fit. The goodness-of-fit may be estimated by comparing chi-square/dof to 1 (>1 indicates a poor fit). Alternatively, it returns the fit and measurement errors when the model is known - see ErrorUnknown option.Type "help fitChiSquare" or see the header for usage.This function calculates the data variance from reported measurement errors, then calculates the chi-square fit. Then the function finds the projection of the delta chi^2 = 1 contour onto each parameter. In the case that the parameter uncertainties are normally distributed, the delta chi^2 = 1 method gives the 68% confidence limit for the parameters. Monte Carlo or investigations of many data sets should be used to confirm the parameter uncertainties are normally distributed. If one encounters the following error message:Unexpected termination flag 0 in non-estimating variableminimization during uncertainty estimationthis is because the non-varying parameter minimization routine has encountered its iteration or evaluation limit. Raise UncOptions.MaxFunEvals or UncOptions.MaxIter and try again.Note: If the user can not use lsqnonlin (i.e., the optimization toolbox is not installed), the program will use the built-in function fminsearch instead. This may reduce the robustness of the calculation. Requirements: · MATLAB Release: R14SP3

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