XYNonlinearFitFunctionConfidenceBandPlotData(Boolean, Boolean, Double, String, NonlinearFitDocument, Int32, Int32, IVariantToVariantTransformation, Int32, IVariantToVariantTransformation, Int32, Double, MatrixDouble) Constructor

public XYNonlinearFitFunctionConfidenceBandPlotData(
	bool isPredictionBand,
	bool isLowerBand,
	double confidenceLevel,
	string fitDocumentIdentifier,
	NonlinearFitDocument fitDocument,
	int fitElementIndex,
	int dependentVariableIndex,
	IVariantToVariantTransformation dependentVariableTransformation,
	int independentVariableIndex,
	IVariantToVariantTransformation independentVariableTransformation,
	int numberOfFittedPoints,
	double sigmaSquare,
	Matrix<double> covarianceMatrixTimesSigmaSquare
)

Parameters

isPredictionBand  Boolean

If true, the prediction band is displayed instead of the confidence band. The prediction band is a little wider, because the sigma of the data points give an additional contribution.

isLowerBand  Boolean

True if this data present the lower confidence (or prediction) band; true if the data represent the upper confidence (or prediction) band.

confidenceLevel  Double

A number greater than 0 and less than 1 representing the confidence level. Usual values are e.g. 0.95, 0.99, 0.999.

fitDocumentIdentifier  String

The fit document identifier.

fitDocument  NonlinearFitDocument

The fit document. The document will be cloned before stored in this instance.

fitElementIndex  Int32

Index of the fit element.

dependentVariableIndex  Int32

Index of the dependent variable of the fit element.

dependentVariableTransformation  IVariantToVariantTransformation

Transformation, which is applied to the result of the fit function to be then shown in the plot. Can be null.

independentVariableIndex  Int32

Index of the independent variable of the fit element.

independentVariableTransformation  IVariantToVariantTransformation

Transformation, which is applied to the x value before it is applied to the fit function. Can be null.

numberOfFittedPoints  Int32

Number of points that were used for fitting. Needed to calculate the Student's distribution quantile.

sigmaSquare  Double

Mean square difference between data points and fitting curve = sumChiSquare/(n-r).

covarianceMatrixTimesSigmaSquare  MatrixDouble

A matrix, representing sigma²(A*At)^-1, which are the covariances of the parameter.

Reference