Polynomial Regression Model Class
Represents a polynomial regression model.
Definition
Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C#
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
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
Polynomial | Constructs a new PolynomialRegressionModel. |
Polynomial | Constructs a new PolynomialRegressionModel. |
Properties
Adjusted |
Gets the adjusted R Squared value for the regression.
(Inherited from RegressionModel<T>) |
Anova |
Gets the AnovaTable that summarizes the results of this model.
(Inherited from RegressionModel<T>) |
Base |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model) |
Coefficient |
Gets the coefficient of variation for the regression.
(Inherited from LinearRegressionModel) |
Computed |
Gets whether the model has been computed.
(Inherited from Model) Obsolete. |
Covariance |
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. |
Degrees |
Gets the total degrees of freedom of the data.
(Inherited from RegressionModel<T>) |
Dependent |
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>) |
Independent |
Gets a matrix whose columns contain the independent variables in the model.
(Inherited from RegressionModel<T>) |
Input |
Gets the schema for the features used for fitting the model.
(Inherited from Model) |
Leverage |
Returns the leverage of each observation.
(Inherited from LinearRegressionModel) |
Log |
Gets the log-likelihood that the model generated the data.
(Inherited from RegressionModel<T>) |
Max |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model) |
Model |
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) |
Number |
Gets the number of observations the model is based on.
(Inherited from Model) |
Parallel |
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>) |
Parameter |
Gets the values of the parameters associated with this model.
(Inherited from RegressionModel<T>) |
Predicted |
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>) |
Residual |
Gets the sum of squares of the residuals of the model.
(Inherited from RegressionModel<T>) |
Ridge |
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>) |
Standard |
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) |
Stepwise |
Gets or sets an object that specifies options for performing stepwise regression.
(Inherited from LinearRegressionModel) |
Supports |
Indicates whether the model supports case weights.
(Inherited from LinearRegressionModel) |
Variance |
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( |
Computes the model.
(Inherited from Model) Obsolete. |
Contains |
Returns whether another RegressionModel<T> is nested
within this instance.
(Inherited from RegressionModel<T>) |
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
Finalize | Allows 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( |
Fits the model to the data.
(Inherited from Model) |
Fit |
Computes the model to the specified input
using the specified parallelization options.
(Overrides LinearRegressionModel.FitCore(ModelInput, ParallelOptions)) |
Get |
Returns the Akaike information criterion (AIC) value for the model.
(Inherited from RegressionModel<T>) |
Get |
Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from RegressionModel<T>) |
Get |
Gets the Breusch-Godfrey test for serial correlation
in the residuals of the regression model.
(Inherited from LinearRegressionModel) |
Get | Gets the width of the 95% confidence band around the best-fit curve at the specified point. |
Get |
Gets the width of the 95% confidence band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel) |
Get | Gets the width of the confidence band around the best-fit curve at the specified point. |
Get |
Gets the width of the confidence band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel) |
Get |
Returns Cook's distance for each of the observations.
(Inherited from LinearRegressionModel) |
Get |
Returns the deleted residual for each observation
(Inherited from LinearRegressionModel) |
Get |
Returns the DFFITS value for each of the observations.
(Inherited from LinearRegressionModel) |
Get |
Gets the Durbin-Watson statistic for the residuals of the regression.
(Inherited from LinearRegressionModel) |
Get |
Returns the externally studentized residual for each observation.
(Inherited from LinearRegressionModel) |
GetHashCode | Serves as the default hash function. (Inherited from Object) |
Get |
Returns a test to verify that the residuals follow a normal distribution.
(Inherited from LinearRegressionModel) |
Get |
Returns a test to verify that the residuals follow a normal distribution.
(Inherited from LinearRegressionModel) |
Get | Gets the width of the prediction band around the best-fit curve at the specified point. |
Get |
Gets the width of the prediction band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel) |
Get | Gets the width of the prediction band around the best-fit curve at the specified point. |
Get |
Gets the width of the prediction band around the best-fit curve at the specified point.
(Inherited from LinearRegressionModel) |
Get | Returns the regression polynomial as a Polynomial object. |
Get |
Returns the studentized deleted residual for each observation
(Inherited from LinearRegressionModel) |
Get |
Returns the studentized residual for each observation.
(Inherited from LinearRegressionModel) |
GetType | Gets the Type of the current instance. (Inherited from Object) |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
Predict( | Predicts the value of the dependent variable based on the specified value of the independent variable. |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Inherited from RegressionModel<T>) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Inherited from RegressionModel<T>) |
Reset |
Clears all fitted model parameters.
(Inherited from Model) Obsolete. |
Reset |
Clears all fitted model parameters.
(Inherited from Model) |
Set |
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( |
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from RegressionModel<T>) |
ToString | Returns a string that represents the current object. (Inherited from Model) |