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GeneralizedLinearModel Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents a generalized linear model.

Inheritance Hierarchy

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SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.DataAnalysis.ModelsRegressionModelDouble
      Extreme.StatisticsGeneralizedLinearModel

Namespace:  Extreme.Statistics
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

Syntax

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C#

VB

C++

F#

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public class GeneralizedLinearModel : RegressionModel<double>

Public Class GeneralizedLinearModel
	Inherits RegressionModel(Of Double)

public ref class GeneralizedLinearModel : public RegressionModel<double>

type GeneralizedLinearModel =  
    class
        inherit RegressionModel<float>
    end

The GeneralizedLinearModel type exposes the following members.

Constructors

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	Name	Description
	GeneralizedLinearModel(IDataFrame, String, String, String)	Constructs a new binomial GeneralizedLinearModel.
	GeneralizedLinearModel(VectorDouble, VectorDouble, MatrixDouble, VectorDouble)	Constructs a new binomial GeneralizedLinearModel.
	GeneralizedLinearModel(VectorDouble, VectorDouble, VectorDouble, VectorDouble)	Constructs a new binomial GeneralizedLinearModel.
	GeneralizedLinearModel(IDataFrame, String, ModelFamily, LinkFunction, String)	Constructs a new GeneralizedLinearModel.
	GeneralizedLinearModel(VectorDouble, MatrixDouble, ModelFamily, LinkFunction, VectorDouble)	Constructs a new GeneralizedLinearModel.
	GeneralizedLinearModel(VectorDouble, IEnumerableVectorDouble, ModelFamily, LinkFunction, VectorDouble)	Constructs a new GeneralizedLinearModel.
	GeneralizedLinearModel(IDataFrame, String, String, ModelFamily, LinkFunction, String)	Constructs a new GeneralizedLinearModel.

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Properties

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	Name	Description
	AdjustedRSquared	Gets the adjusted R Squared value for the regression. (Inherited from RegressionModelT.)
	AnovaTable	Gets the AnovaTable that summarizes the results of this model. (Inherited from RegressionModelT.)
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	CountVariable	Gets or sets the vector that contains the count of the number of trials in a binomial regression.
	CovarianceMatrix	Gets the covariance matrix of the model parameters. (Inherited from RegressionModelT.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DegreesOfFreedom	Gets the total degrees of freedom of the data. (Inherited from RegressionModelT.)
	DependentVariable	Gets a vector that contains the dependent variable that is to be fitted. (Inherited from RegressionModelT.)
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	FStatistic	Gets the F statistic for the regression. (Inherited from RegressionModelT.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model. (Inherited from RegressionModelT.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	LinkFunction	Gets or sets the link function of the model.
	LogLikelihood	Gets the log-likelihood that the model generated the data. (Inherited from RegressionModelT.)
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelFamily	Gets or sets the probability distribution family of the 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.
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from Model.)
	Offset	Gets or sets the offset in a negative binomial regression.
	OffsetVariable	Gets or sets the vector that contains the offset in a negative binomial regression.
	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 RegressionModelT.)
	ParameterValues	Gets the values of the parameters associated with this model. (Inherited from RegressionModelT.)
	Predictions	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from RegressionModelT.)
	PValue	Gets the probability corresponding to the F statistic for the regression. (Inherited from RegressionModelT.)
	Residuals	Gets a vector containing the residuals of the model. (Inherited from RegressionModelT.)
	ResidualSumOfSquares	Gets the sum of squares of the residuals of the model. (Inherited from RegressionModelT.)
	RSquared	Gets the R Squared value for the regression. (Inherited from RegressionModelT.)
	ScaleFittingMethod	Gets or sets a value that indicates how the scale parameter of the model should be estimated.
	ScaleParameter	Gets or sets the scale parameter of the model.
	StandardError	Gets the standard error of the regression. (Inherited from RegressionModelT.)
	Status	Gets the status of the model, which determines which information is available. (Inherited from Model.)
	SupportsWeights	Indicates whether the model supports case weights. (Inherited from Model.)
	Weights	Gets or sets the actual weights. (Inherited from Model.)

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Methods

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	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	Contains	Returns whether another RegressionModelT is nested within this instance. (Inherited from RegressionModelT.)
	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(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	FitCore	Computes the model to the specified input using the specified parallelization options. (Overrides ModelFitCore(ModelInput, ParallelOptions).)
	GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model. (Inherited from RegressionModelT.)
	GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model. (Inherited from RegressionModelT.)
	GetChiSquare	Gets the Pearson chi-square statistic of the model.
	GetCorrectedAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model.
	GetDeviance	Returns the deviance of the computed model.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetInformationMatrix	Calculates the information matrix of the regression.
	GetKernelLogLikelihood	Returns the portion of the log-likelihood of the computed model that depends on the model parameters.
	GetLikelihoodRatioTest	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(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	Predict(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	Predict(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from RegressionModelT.)
	PredictCore(MatrixT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Inherited from RegressionModelT.)
	PredictCore(VectorT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Inherited from RegressionModelT.)
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	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 RegressionModelT.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

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Remarks

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Use the GeneralizedLinearModel to compute a regression model where the distribution of the dependent variable around the mean value may not be normal.

See Also

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Reference

Extreme.Statistics Namespace

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