OBSOLETE NONLINEAR REGRESSION PACKAGE SYMBOL

NonlinearRegress

As of Version 7.0,

has been superseded by NonlinearModelFit and is part of the built-in Mathematica kernel.

finds numerical values of the parameters pars that make the model expr give a best fit to data as a function of vars and provides diagnostics for the fitting.

finds a best fit and provides diagnostics subject to the constraints cons.

Details Details

To use , you first need to load the Nonlinear Regression Package using .
The data can have the form , where the number of coordinates is equal to the number of variables in the list vars.
The data can also be of the form , with a single coordinate assumed to take values 1, 2, ....
The model expr must yield a numerical value when pars and vars are all numerical.
The estimates of the model parameters give a least-squares fit, minimizing the sum of squared residuals.
returns a list of rules for results and diagnostics specified by the option RegressionReport.
The model expr must yield a numerical value when pars and vars are all numerical.
For constrained models, the constraints cons can contain equations, inequalities, or logical combinations of equations and inequalities.
Parameters are specified using the same syntax as in FindFit.
The results found by may correspond only to a local optimum.
The following options can be given:

AccuracyGoal	Automatic	the accuracy sought
Compiled	Automatic	whether to compile the model
ConfidenceLevel	0.95	confidence level for confidence intervals
Gradient	Automatic	list of gradient functions
MaxIterations	100	maximum number of iterations to use
Method	Automatic	method to use
PrecisionGoal	Automatic	the precision sought
RegressionReport	SummaryReport	fit diagnostics to include
Tolerance	Automatic	numerical tolerance for matrix operations
Weights	Automatic	list of weights for each point or pure function
WorkingPrecision	MachinePrecision	the precision used in internal computations

The options AccuracyGoal, Compiled, , MaxIterations, Method, PrecisionGoal, and WorkingPrecision are the same as for FindFit.
Possible settings for Weights are Automatic, a list of numbers with the same length as data, or a pure function.
With the default setting Weights->Automatic, each data point is given a weight of 1.
With the setting Weights->g, the weight associated with each point is .
The default setting RegressionReport->SummaryReport includes the elements BestFitParameters, ParameterCITable, EstimatedVariance, ANOVATable, AsymptoticCorrelationMatrix and FitCurvatureTable.
For constrained models, RegressionReport values based on approximate normality assumptions may not be valid. When such values are included, the values will be generated along with a warning message.