PolynomialRegressionModel Class

Represents a polynomial regression model.

Definition

Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C#
public class PolynomialRegressionModel : LinearRegressionModel
Inheritance
Object  →  Model  →  RegressionModel<Double>  →  LinearRegressionModel  →  PolynomialRegressionModel

Remarks

Use the PolynomialRegressionModel class to represent a linear regression model that uses polynomials in one variable.

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.

Constructors

PolynomialRegressionModel(Vector<Double>, Vector<Double>, Int32) Constructs a new PolynomialRegressionModel.
PolynomialRegressionModel(IDataFrame, String, String, Int32) Constructs a new PolynomialRegressionModel.

Properties

AdjustedRSquared Gets the adjusted R Squared value for the regression.
(Inherited from RegressionModel<T>)
AnovaTable Gets the AnovaTable that summarizes the results of this model.
(Inherited from RegressionModel<T>)
BaseFeatureIndex Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model)
CoefficientOfVariation Gets the coefficient of variation for the regression.
(Inherited from LinearRegressionModel)
Computed Gets whether the model has been computed.
(Inherited from Model)
Obsolete.
CovarianceMatrix Gets the covariance matrix of the model parameters.
(Inherited from RegressionModel<T>)
Data Gets an object that contains all the data used as input to the model.
(Inherited from Model)
Degree Gets the degree of the regression polynomial.
DegreesOfFreedom Gets the total degrees of freedom of the data.
(Inherited from RegressionModel<T>)
DependentVariable Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from RegressionModel<T>)
Fitted Gets whether the model has been computed.
(Inherited from Model)
FStatistic Gets the F statistic for the regression.
(Inherited from RegressionModel<T>)
IndependentVariables Gets a matrix whose columns contain the independent variables in the model.
(Inherited from RegressionModel<T>)
InputSchema Gets the schema for the features used for fitting the model.
(Inherited from Model)
Leverage Returns the leverage of each observation.
(Inherited from LinearRegressionModel)
LogLikelihood Gets the log-likelihood that the model generated the data.
(Inherited from RegressionModel<T>)
MaxDegreeOfParallelism Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model)
ModelSchema Gets the collection of variables used in the model.
(Inherited from Model)
NoIntercept Gets or sets whether to include the intercept or constant term in the regression model.
(Inherited from LinearRegressionModel)
NumberOfObservations Gets the number of observations the model is based on.
(Inherited from Model)
ParallelOptions Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model)
Parameters Gets the collection of parameters associated with this model.
(Inherited from RegressionModel<T>)
ParameterValues Gets the values of the parameters associated with this model.
(Inherited from RegressionModel<T>)
PredictedRSquared Gets the predicted R Squared value of the model.
(Inherited from LinearRegressionModel)
Predictions Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from RegressionModel<T>)
Press Gets the predicted residual error sum of squares (PRESS) of the model.
(Inherited from LinearRegressionModel)
PValue Gets the probability corresponding to the F statistic for the regression.
(Inherited from RegressionModel<T>)
Residuals Gets a vector containing the residuals of the model.
(Inherited from RegressionModel<T>)
ResidualSumOfSquares Gets the sum of squares of the residuals of the model.
(Inherited from RegressionModel<T>)
RidgeParameter Gets or sets the coefficient of the squared norm of the regression parameters for ridge regression.
(Inherited from LinearRegressionModel)
RSquared Gets the R Squared value for the regression.
(Inherited from RegressionModel<T>)
StandardError Gets the standard error of the regression.
(Inherited from RegressionModel<T>)
Standardize Gets or sets whether the variables should be standardized prior to computing the regression.
(Inherited from LinearRegressionModel)
Obsolete.
Status Gets the status of the model, which determines which information is available.
(Inherited from Model)
Steps Gets the collection of steps performed in a stepwise regression.
(Inherited from LinearRegressionModel)
StepwiseOptions Gets or sets an object that specifies options for performing stepwise regression.
(Inherited from LinearRegressionModel)
SupportsWeights Indicates whether the model supports case weights.
(Inherited from LinearRegressionModel)
VarianceInflationFactors Returns the Variance Inflation Factor (VIF) for each variable in the model.
(Inherited from LinearRegressionModel)
Weights Gets or sets the actual weights.
(Inherited from Model)

Methods

Compute() Computes the model.
(Inherited from Model)
Obsolete.
Compute(ParallelOptions) Computes the model.
(Inherited from Model)
Obsolete.
Contains Returns whether another RegressionModel<T> is nested within this instance.
(Inherited from RegressionModel<T>)
EqualsDetermines whether the specified object is equal to the current object.
(Inherited from Object)
FinalizeAllows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object)
Fit() Fits the model to the data.
(Inherited from Model)
Fit(ParallelOptions) Fits the model to the data.
(Inherited from Model)
FitCore Computes the model to the specified input using the specified parallelization options.
(Overrides LinearRegressionModel.FitCore(ModelInput, ParallelOptions))
GetAkaikeInformationCriterion Returns the Akaike information criterion (AIC) value for the model.
(Inherited from RegressionModel<T>)
GetBayesianInformationCriterion Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from RegressionModel<T>)
GetBreuschGodfreyTest Gets the Breusch-Godfrey test for serial correlation in the residuals of the regression model.
(Inherited from LinearRegressionModel)
GetConfidenceBandwidth(Double) Gets the width of the 95% confidence band around the best-fit curve at the specified point.
GetConfidenceBandwidth(Vector<Double>) Gets the width of the 95% confidence band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel)
GetConfidenceBandwidth(Double, Double) Gets the width of the confidence band around the best-fit curve at the specified point.
GetConfidenceBandwidth(Vector<Double>, Double) Gets the width of the confidence band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel)
GetCooksDistance Returns Cook's distance for each of the observations.
(Inherited from LinearRegressionModel)
GetDeletedResiduals Returns the deleted residual for each observation
(Inherited from LinearRegressionModel)
GetDffits Returns the DFFITS value for each of the observations.
(Inherited from LinearRegressionModel)
GetDurbinWatsonStatistic Gets the Durbin-Watson statistic for the residuals of the regression.
(Inherited from LinearRegressionModel)
GetExternallyStudentizedResiduals Returns the externally studentized residual for each observation.
(Inherited from LinearRegressionModel)
GetHashCodeServes as the default hash function.
(Inherited from Object)
GetNormalityOfResidualsTest() Returns a test to verify that the residuals follow a normal distribution.
(Inherited from LinearRegressionModel)
GetNormalityOfResidualsTest(TestOfNormality) Returns a test to verify that the residuals follow a normal distribution.
(Inherited from LinearRegressionModel)
GetPredictionBandwidth(Double) Gets the width of the prediction band around the best-fit curve at the specified point.
GetPredictionBandwidth(Vector<Double>) Gets the width of the prediction band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel)
GetPredictionBandwidth(Double, Double) Gets the width of the prediction band around the best-fit curve at the specified point.
GetPredictionBandwidth(Vector<Double>, Double) Gets the width of the prediction band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel)
GetRegressionPolynomial Returns the regression polynomial as a Polynomial object.
GetStudentizedDeletedResiduals Returns the studentized deleted residual for each observation
(Inherited from LinearRegressionModel)
GetStudentizedResiduals Returns the studentized residual for each observation.
(Inherited from LinearRegressionModel)
GetTypeGets the Type of the current instance.
(Inherited from Object)
MemberwiseCloneCreates a shallow copy of the current Object.
(Inherited from Object)
Predict(Double) Predicts the value of the dependent variable based on the specified value of the independent variable.
Predict(IDataFrame, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModel<T>)
Predict(Matrix<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModel<T>)
Predict(Vector<T>, ModelInputFormat) Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModel<T>)
PredictCore(Matrix<T>, Boolean) Predicts the value of the dependent variable based on the specified values of the features.
(Inherited from RegressionModel<T>)
PredictCore(Vector<T>, Boolean) Predicts the value of the dependent variable based on the specified values of the features.
(Inherited from RegressionModel<T>)
ResetComputation Clears all fitted model parameters.
(Inherited from Model)
Obsolete.
ResetFit Clears all fitted model parameters.
(Inherited from Model)
SetDataSource Uses the specified data frame as the source for all input variables.
(Inherited from Model)
Summarize() Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model)
Summarize(SummaryOptions) Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from RegressionModel<T>)
ToStringReturns a string that represents the current object.
(Inherited from Model)

See Also