Thetype exposes the following members.
Gets the covariance matrix containing the variances and covariances for all fit parameters.
Gets the number of fit parameters.
Gets a test of the quality of the fit.
Gets the best fit parameter set.
Gets the coefficient of correlation between two fit parameters.
Gets the covariance of two fit parameters.
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.(Inherited from Object.)
Serves as a hash function for a particular type.(Inherited from Object.)
Gets the Type of the current instance.(Inherited from Object.)
Creates a shallow copy of the current Object.(Inherited from Object.)
Get an estimate of a fit parameter.
Returns a string that represents the current object.(Inherited from Object.)
All fit methods in the Meta.Numerics library return their results as an instance of the FitResult class. This includes methods that fit a sample to a distribution (e.g. FitToSample(Sample)), regression methods for bivariate and multivariate data (e.g. LinearLogisticRegression), and least-squares fits of data with error bars to a model function (e.g. FitToFunction(FuncDouble, T, Double, Double)).
A FitResult instance contains not only the parameter values, but also covariances and a goodness-of-fit test.
The vector of best-fit parameter values can be obtained as an array using the Parameters method. Individual parameter values can be obtained using the Parameter(Int32) method; this method gives not only a best-fit value but also an uncertainty by returning a UncertainValue.
The goodness-of-fit test result stored in the GoodnessOfFit measures the quality of the fit. For fits to distributions, it is a Kolmogorov-Smirnov test. For regressions, it is an F-test. For fits to data with error bars, it is a chi-square test.
Fits are done using the maximum likelyhood method, with results corrected for any small-sample bias.