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  • Extreme.Statistics
    • AnovaModel Class
    • AnovaModelRow Class
    • AnovaRow Class
    • AnovaRowType Enumeration
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    • Kernel Class
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    • KernelDensityBandwidthEstimator Enumeration
    • LinearRegressionModel Class
    • LinkFunction Class
    • LogisticRegressionMethod Enumeration
    • LogisticRegressionModel Class
    • ModelFamily Class
    • NearestCorrelationMatrixAlgorithm Enumeration
    • NonlinearRegressionModel Class
    • OneWayAnovaModel Class
    • OneWayRAnovaModel Class
    • PolynomialRegressionModel Class
    • RegularizedRegressionModel Class
    • ScaleFittingMethod Enumeration
    • SimpleRegressionKind Enumeration
    • SimpleRegressionModel Class
    • Stats Class
    • StepwiseCriterion Enumeration
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    • SumsOfSquaresType Enumeration
    • TestOfHomogeneityOfVariances Enumeration
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    • TwoWayAnovaModel Class
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  • SimpleRegressionModel Class
    • SimpleRegressionModel Constructors
    • Properties
    • Methods

SimpleRegressionModel Class

Extreme Optimization Numerical Libraries for .NET Professional
Represents a linear regression model.
Inheritance Hierarchy

SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.DataAnalysis.ModelsRegressionModelDouble
      Extreme.StatisticsSimpleRegressionModel

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

C#
VB
C++
F#
Copy
public class SimpleRegressionModel : RegressionModel<double>
Public Class SimpleRegressionModel
	Inherits RegressionModel(Of Double)
public ref class SimpleRegressionModel : public RegressionModel<double>
type SimpleRegressionModel =  
    class
        inherit RegressionModel<float>
    end

The SimpleRegressionModel type exposes the following members.

Constructors

  NameDescription
Public methodSimpleRegressionModel(VectorDouble, VectorDouble)
Constructs a new SimpleRegressionModel.
Public methodSimpleRegressionModel(IDataFrame, String, String)
Constructs a new SimpleRegressionModel.
Public methodSimpleRegressionModel(VectorDouble, VectorDouble, SimpleRegressionKind)
Constructs a new SimpleRegressionModel.
Public methodSimpleRegressionModel(IDataFrame, String, String, SimpleRegressionKind)
Constructs a new SimpleRegressionModel.
Public methodSimpleRegressionModel(VectorDouble, VectorDouble, VectorDouble, SimpleRegressionKind, Boolean)
Constructs a new LinearRegressionModel.
Public methodSimpleRegressionModel(IDataFrame, String, String, String, SimpleRegressionKind, Boolean)
Constructs a new LinearRegressionModel.
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Properties

  NameDescription
Public propertyAdjustedRSquared
Gets the adjusted R Squared value for the regression.
(Inherited from RegressionModelT.)
Public propertyAnovaTable
Gets the AnovaTable that summarizes the results of this model.
(Inherited from RegressionModelT.)
Public propertyBaseFeatureIndex
Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model.)
Public propertyComputed Obsolete.
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyCovarianceMatrix
Gets the covariance matrix of the model parameters.
(Inherited from RegressionModelT.)
Public propertyData
Gets an object that contains all the data used as input to the model.
(Inherited from Model.)
Public propertyDegreesOfFreedom
Gets the total degrees of freedom of the data.
(Inherited from RegressionModelT.)
Public propertyDependentVariable
Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from RegressionModelT.)
Public propertyFitted
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyFStatistic
Gets the F statistic for the regression.
(Inherited from RegressionModelT.)
Public propertyIndependentVariables
Gets a matrix whose columns contain the independent variables in the model.
(Inherited from RegressionModelT.)
Public propertyInputSchema
Gets the schema for the features used for fitting the model.
(Inherited from Model.)
Public propertyKind
Gets or sets the kind of linearized regression to perform.
Public propertyLogLikelihood
Gets the log-likelihood that the model generated the data.
(Inherited from RegressionModelT.)
Public propertyMaxDegreeOfParallelism
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.)
Public propertyModelSchema
Gets the collection of variables used in the model.
(Inherited from Model.)
Public propertyNoIntercept
Gets or sets whether to include the intercept or constant term in the regression model.
Public propertyNumberOfObservations
Gets the number of observations the model is based on.
(Inherited from Model.)
Protected propertyParallelOptions
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.)
Public propertyParameters
Gets the collection of parameters associated with this model.
(Inherited from RegressionModelT.)
Public propertyParameterValues
Gets the values of the parameters associated with this model.
(Inherited from RegressionModelT.)
Public propertyPredictions
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from RegressionModelT.)
Public propertyPValue
Gets the probability corresponding to the F statistic for the regression.
(Inherited from RegressionModelT.)
Public propertyResiduals
Gets a vector containing the residuals of the model.
(Inherited from RegressionModelT.)
Public propertyResidualSumOfSquares
Gets the sum of squares of the residuals of the model.
(Inherited from RegressionModelT.)
Public propertyRSquared
Gets the R Squared value for the regression.
(Inherited from RegressionModelT.)
Public propertyStandardError
Gets the standard error of the regression.
(Inherited from RegressionModelT.)
Public propertyStatus
Gets the status of the model, which determines which information is available.
(Inherited from Model.)
Public propertySupportsWeights
Indicates whether the model supports case weights.
(Overrides ModelSupportsWeights.)
Public propertyWeights
Gets or sets the actual weights.
(Inherited from Model.)
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Methods

  NameDescription
Public methodCompute Obsolete.
Computes the model.
(Inherited from Model.)
Public methodCompute(ParallelOptions) Obsolete.
Computes the model.
(Inherited from Model.)
Public methodContains
Returns whether another RegressionModelT is nested within this instance.
(Inherited from RegressionModelT.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodFit
Fits the model to the data.
(Inherited from Model.)
Public methodFit(ParallelOptions)
Fits the model to the data.
(Inherited from Model.)
Protected methodFitCore
Computes the model to the specified input using the specified parallelization options.
(Overrides ModelFitCore(ModelInput, ParallelOptions).)
Public methodGetAkaikeInformationCriterion
Returns the Akaike information criterion (AIC) value for the model.
(Inherited from RegressionModelT.)
Public methodGetBayesianInformationCriterion
Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from RegressionModelT.)
Public methodGetConfidenceInterval(Double)
Gets the 95% confidence interval around the best-fit curve at the specified point.
Public methodGetConfidenceInterval(Double, Double)
Gets the confidence interval around the best-fit curve at the specified point.
Public methodGetDurbinWatsonStatistic
Gets the Durbin-Watson statistic for the residuals of the regression.
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetPredictionInterval(Double)
Gets the prediction interval around the best-fit curve at the specified point.
Public methodGetPredictionInterval(Double, Double)
Gets the width of the prediction band around the best-fit curve at the specified point.
Public methodGetRegressionCurve
Returns the regression curve.
Public methodGetRegressionLine
Returns the regression line.
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodGetWorkingHotellingConfidenceBandwidth(VectorDouble)
Gets the width of the 95% Working-Hotelling confidence band around the best-fit curve at the specified point.
Public methodGetWorkingHotellingConfidenceBandwidth(VectorDouble, Double)
Gets the width of the Working-Hotelling confidence band around the best-fit curve at the specified point.
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodPredict(Double)
Predicts the value of the dependent variable based on the specified value of the independent variable.
Public methodPredict(IDataFrame, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModelT.)
Public methodPredict(MatrixT, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModelT.)
Public methodPredict(VectorT, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
(Inherited from RegressionModelT.)
Protected methodPredictCore(MatrixDouble, Boolean)
Predicts the value of the dependent variable based on the specified values of the features.
(Overrides RegressionModelTPredictCore(MatrixT, Boolean).)
Protected methodPredictCore(VectorDouble, Boolean)
Predicts the value of the dependent variable based on the specified values of the features.
(Overrides RegressionModelTPredictCore(VectorT, Boolean).)
Public methodResetComputation Obsolete.
Clears all fitted model parameters.
(Inherited from Model.)
Public methodResetFit
Clears all fitted model parameters.
(Inherited from Model.)
Public methodSetDataSource
Uses the specified data frame as the source for all input variables.
(Inherited from Model.)
Public methodSummarize
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model.)
Public methodSummarize(SummaryOptions)
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from RegressionModelT.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Model.)
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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.

See Also

Reference

Extreme.Statistics Namespace

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