Represents a polynomial regression model.
Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (Extreme.Numerics)
Syntax
| Visual Basic (Declaration) |
|---|
Public Class PolynomialRegressionModel _ Inherits LinearRegressionModel |
| C# |
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public class PolynomialRegressionModel : LinearRegressionModel |
| C++ |
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public ref class PolynomialRegressionModel : public LinearRegressionModel |
Methods
| Icon | Type | Description |
|---|---|---|
 | Compute() |
Computes the model.
|
 | ComputeModel() |
Fits the model to the data.
|
| Contains(GeneralLinearModel) |
Returns a value that indicates whether another GeneralLinearModel is nested
within this instance.
|
| Equals(Object) | |
| Finalize() | |
| GetCorrelationMatrix() |
Gets the correlation matrix for the independent variables in the model.
|
| GetCovarianceMatrix() |
Gets the covariance matrix for the independent variables in the model.
|
| GetDurbinWatsonStatistic() |
Gets the Durbin-Watson statistic for the residuals of the regression.
|
| GetHashCode() | Serves as a hash function for a particular type. |
| GetNormalityOfResidualsTest() |
Returns a test to verify that the residuals follow a normal distribution.
|
| GetNormalityOfResidualsTest(TestOfNormality) |
Returns a test to verify that the residuals follow a normal distribution.
|
| GetRegressionPolynomial() |
Returns the regression polynomial as a Polynomial object.
|
| GetSumOfSquaresMatrix() |
Calculates the sum-of-squares matrix for the
VariableCollection.
|
| GetType() | Gets the Type of the current instance. |
| MemberwiseClone() | Creates a shallow copy of the current Object. |
| Predict(Double[]()) |
Predicts the value of the dependent variable based on the specified values of the independent
variables.
|
| Predict(Vector) |
Predicts the value of the dependent variable based on the specified values of the independent
variables.
|
| Predict(Matrix) |
Predicts the values of the dependent variable for multiple collectionso of values
for the independent variables.
|
| ResetComputation() |
Clears all computed model parameters.
|
| SetAnovaModelRow(Int32, String, Double, Double) |
Sets the data of a row in the model's AnovaTable.
|
| SetAnovaRow(AnovaRowType, String, Double, Double) |
Sets the data of a row in the model's AnovaTable.
|
| ToString() |
Constructors
| Icon | Type | Description |
|---|---|---|
| PolynomialRegressionModelNew(VariableCollection, String, String, Int32) |
Constructs a new PolynomialRegressionModel.
|
| PolynomialRegressionModelNew(DataTable, String, String, Int32) |
Constructs a new PolynomialRegressionModel.
|
| PolynomialRegressionModelNew(NumericalVariable, NumericalVariable, Int32) |
Constructs a new PolynomialRegressionModel.
|
| PolynomialRegressionModelNew(Double[](), Double[](), Int32) |
Constructs a new PolynomialRegressionModel.
|
| PolynomialRegressionModelNew(Vector, Vector, Int32) |
Constructs a new PolynomialRegressionModel.
|
Properties
| Icon | Type | Description |
|---|---|---|
 | AdjustedRSquared |
Gets the adjusted R Squared value for the regression.
|
| AnovaTable |
Gets the AnovaTable that summarizes the results of this model.
|
| BestFitParameters |
Gets a vector containing the values of the regression parameters.
|
| CoefficientOfVariation |
Gets the coefficient of variation for the regression.
|
| Computed |
Gets a value that indicates whether the regression model has been computed.
|
| Degree |
Gets the degree of the regression polynomial.
|
| DegreesOfFreedom |
Gets the total degrees of freedom of the data.
|
| DependentVariable |
Gets the dependent variable for the regression model.
|
| DependentVariables |
Gets the collection of dependent variables associated with this model.
|
| FStatistic |
Gets the F statistic for the regression.
|
| IndependentVariables |
Gets the collection of independent variables associated with this model.
|
| InterceptParameterName |
Gets or sets the name of the intercept parameter.
|
| NoIntercept |
Gets or sets a value that indicates whether to include the intercept or constant term in the
regression model.
|
| ObservationMatrix |
Gets or sets a Matrix whose rows contain
the observations.
|
| Parameters |
Gets the collection of parameters associated with this model.
|
| PredictedValues |
Gets a vector containing the model's predicted values for the dependent variable.
|
| PValue |
Gets the probability corresponding to the F statistic for the regression.
|
| Residuals |
Gets a vector containing the residuals of the model.
|
| ResidualSumOfSquares |
Gets the sum of squares of the residuals of the model.
|
| RSquared |
Gets the R Squared value for the regression.
|
| StandardError |
Gets the standard error of the regression.
|
| WeightVector |
Gets or sets the weights of the observations in this
model.
|
Remarks
Use the PolynomialRegressionModel class to represent a linear regression
model that uses polynomials in one or more variables.
PolynomialRegressionModel inherits from LinearRegressionModel, but has special constructors that make it easier to create polynomial regression models in one variable. It also defines some members that may be more appropriate for the simple case. For example, the GetRegressionPolynomial() method returns a Polynomial object that represents the resulting regression curve.
Inheritance Hierarchy
System.Object
Extreme.Statistics.GeneralLinearModel
Extreme.Statistics.LinearRegressionModel
Extreme.Statistics.PolynomialRegressionModel
Extreme.Statistics.GeneralLinearModel
Extreme.Statistics.LinearRegressionModel
Extreme.Statistics.PolynomialRegressionModel
