Generalized
Represents a generalized linear 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 GeneralizedLinearModel : RegressionModel<double>- Inheritance
- Object → Model → RegressionModel<Double> → GeneralizedLinearModel
Remarks
Use the GeneralizedLinearModel to compute a regression model where the distribution of the dependent variable around the mean value may not be normal.
Constructors
| Generalized | Constructs a new binomial GeneralizedLinearModel. |
| Generalized | Constructs a new binomial GeneralizedLinearModel. |
| Generalized | Constructs a new binomial GeneralizedLinearModel. |
| Generalized | Constructs a new GeneralizedLinearModel. |
| Generalized | Constructs a new GeneralizedLinearModel. |
| Generalized | Constructs a new GeneralizedLinearModel. |
| Generalized | Constructs a new GeneralizedLinearModel. |
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. |
| Count | Gets or sets the vector that contains the count of the number of trials in a binomial regression. |
| 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) |
| Link | Gets or sets the link function of the model. |
| 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 or sets the probability distribution family of the 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) |
| Offset | Gets or sets the offset in a negative binomial regression. |
| Offset | Gets or sets the vector that contains the offset in a negative binomial regression. |
| 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>) |
| Scale | Gets or sets a value that indicates how the scale parameter of the model should be estimated. |
| Scale | Gets or sets the scale parameter of the model. |
| 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.
(Inherited from Model) |
| 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 Pearson chi-square statistic of the model. |
| Get | Returns the Akaike information criterion (AIC) value for the model. |
| Get | Returns the deviance of the computed model. |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| Get | Calculates the information matrix of the regression. |
| Get | Returns the portion of the log-likelihood of the computed model that depends on the model parameters. |
| Get | Returns a test to verify the significance of the logistic model. |
| 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 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) |