Represents a logistic regression model.
Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (Extreme.Numerics)
Syntax
| Visual Basic (Declaration) |
|---|
Public Class LogisticRegressionModel _ Inherits GeneralLinearModel |
| C# |
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public class LogisticRegressionModel : GeneralLinearModel |
| C++ |
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public ref class LogisticRegressionModel : public GeneralLinearModel |
Methods
| Icon | Type | Description |
|---|---|---|
 | Compute() |
Computes the model.
|
 | ComputeModel() |
Fits the model to the data.
|
| Contains(GeneralLinearModel) |
Returns a value that indicates whether another GeneralLinearModel is nested
within this instance.
|
| Equals(Object) | |
| Finalize() | |
| GetCovarianceMatrix() |
Gets the covariance matrix for the parameters of the regression.
|
| GetHashCode() | Serves as a hash function for a particular type. |
| GetInformationMatrix() |
Calculates the sum-of-squares matrix for the
VariableCollection.
|
| GetLikelihoodRatioTest() |
Returns a test to verify the significance of the logistic model.
|
| GetLikelihoodRatioTest(LogisticRegressionModel) |
Returns a test to verify the significance of the logistic model.
|
| GetLogLikelihood() |
Returns the log-likelihood of the computed model.
|
| GetType() | Gets the Type of the current instance. |
| GetWaldTest() |
Returns the Wald test for all the parameters in the regression.
|
| GetWaldTest(Int32[]()) |
Returns the Wald test for the selected parameters in the regression.
|
| MemberwiseClone() | Creates a shallow copy of the current Object. |
| Predict(Double[]()) |
Predicts the value of the dependent variable based on the specified values of the independent
variables.
|
| Predict(Vector) |
Predicts the value of the dependent variable based on the specified values of the independent
variables.
|
| Predict(Matrix) |
Predicts the values of the dependent variable for multiple collectionso of values
for the independent variables.
|
| ResetComputation() |
Clears all computed model parameters.
|
| SetAnovaModelRow(Int32, String, Double, Double) |
Sets the data of a row in the model's AnovaTable.
|
| SetAnovaRow(AnovaRowType, String, Double, Double) |
Sets the data of a row in the model's AnovaTable.
|
| ToString() |
Constructors
| Icon | Type | Description |
|---|---|---|
| LogisticRegressionModelNew(VariableCollection, String, String[]()) |
Constructs a new LogisticRegressionModel.
|
| LogisticRegressionModelNew(DataTable, String, String[]()) |
Constructs a new LogisticRegressionModel.
|
| LogisticRegressionModelNew(Vector, Matrix) |
Constructs a new SimpleRegressionModel.
|
| LogisticRegressionModelNew(NumericalVariable, NumericalVariable[]()) |
Constructs a new LogisticRegressionModel.
|
Properties
| Icon | Type | Description |
|---|---|---|
 | AdjustedRSquared |
Gets the adjusted R Squared value for the regression.
|
| AnovaTable |
Gets the AnovaTable that summarizes the results of this model.
|
| BestFitParameters |
Gets a vector containing the values of the regression parameters.
|
| CoefficientOfVariation |
Gets the coefficient of variation for the regression.
|
| Computed |
Gets a value that indicates whether the regression model has been computed.
|
| DegreesOfFreedom |
Gets the total degrees of freedom of the data.
|
| DependentVariable |
Gets the dependent variable for the regression model.
|
| DependentVariables |
Gets the collection of dependent variables associated with this model.
|
| FStatistic |
Gets the F statistic for the regression.
|
| IndependentVariables |
Gets the collection of independent variables associated with this model.
|
| InterceptParameterName |
Gets or sets the name of the intercept parameter.
|
| ObservationMatrix |
Gets or sets a Matrix whose rows contain
the observations.
|
| Parameters |
Gets the collection of parameters associated with this model.
|
| PredictedValues |
Gets a vector containing the model's predicted values for the dependent variable.
|
| PValue |
Gets the probability corresponding to the F statistic for the regression.
|
| Residuals |
Gets a vector containing the residuals of the model.
|
| ResidualSumOfSquares |
Gets the sum of squares of the residuals of the model.
|
| RSquared |
Gets the R Squared value for the regression.
|
| StandardError |
Gets the standard error of the regression.
|
| WeightVector |
Gets or sets the weights of the observations in this
model.
|
Remarks
Use the LogisticRegressionModel class to analyze a situation where the outcome
can have two possible values. A logistic regression model tries to express one variable, called the
dependent variable, which can have only two distinct values, as a function of
one or more other variables called independent variables or predictors in a specific form.
Inheritance Hierarchy
See Also
