Simple Regression Model Class
Definition
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.0.4
public class SimpleRegressionModel : RegressionModel<double>
- Inheritance
- Object → Model → RegressionModel<Double> → SimpleRegressionModel
Remarks
Use the SimpleRegressionModel class to investigate a linear relationship between two variables. The technique used to model such a relationship is called simple linear regression.
SimpleRegressionModel inherits from LinearRegressionModel, but has special constructors that make it easier to create simple regression models. It also defines some members that may be more appropriate for the simple case. For example, the GetRegressionLine() method returns a Polynomial object that represents the resulting regression line.
By setting the Kind property, it is possible to compute linearized versions of non-linear regression functions. When a kind other than linear regression is chosen, a linearization transformation is applied to one or both of the variables before the regression line is computed. The residuals are those of the transformed regression model. The parameter values are transformed back if needed.
Constructors
Simple | Constructs a new SimpleRegressionModel. |
Simple | Constructs a new SimpleRegressionModel. |
Simple | Constructs a new SimpleRegressionModel. |
Simple | Constructs a new SimpleRegressionModel. |
Simple | Constructs a new LinearRegressionModel. |
Simple | Constructs a new LinearRegressionModel. |
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) |
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) |
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) |
Kind | Gets or sets the kind of linearized regression to perform. |
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. |
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>) |
Predictions |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from RegressionModel<T>) |
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>) |
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>) |
Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model) |
Supports |
Indicates whether the model supports case weights.
(Overrides Model.SupportsWeights) |
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 Model.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 95% confidence interval around the best-fit curve at the specified point. |
Get | Gets the confidence interval around the best-fit curve at the specified point. |
Get | Gets the Durbin-Watson statistic for the residuals of the regression. |
Get | Serves as the default hash function. (Inherited from Object) |
Get | Gets the prediction interval 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. |
Get | Returns the regression curve. |
Get | Returns the regression line. |
Get | Gets the Type of the current instance. (Inherited from Object) |
Get | Gets the width of the 95% Working-Hotelling confidence band around the best-fit curve at the specified point. |
Get | Gets the width of the Working-Hotelling confidence band around the best-fit curve at the specified point. |
Memberwise | 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.
(Overrides RegressionModel<T>.PredictCore(Matrix<T>, Boolean)) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Overrides RegressionModel<T>.PredictCore(Vector<T>, Boolean)) |
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) |