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PCFactorExtraction Class

Class implementing the principle components (PC) algorithm for factor extraction when performing factor analysis. Used as a class type parameter for the factor analysis classes.
Inheritance Hierarchy
SystemObject
  CenterSpace.NMath.CorePCFactorExtraction

Namespace:  CenterSpace.NMath.Core
Assembly:  NMath (in NMath.dll) Version: 7.4
Syntax
public class PCFactorExtraction : IFactorExtraction, 
	ICloneable

The PCFactorExtraction type exposes the following members.

Constructors
  NameDescription
Public methodPCFactorExtraction
Constructs a
PCFactorExtraction
object which will extract factors whose eigenvalues are greater than one.
Public methodPCFactorExtraction(FuncDoubleVector, DoubleMatrix, Int32)
Constructs a
PCFactorExtraction
object using the given functor to determine the number of factors to extract. This function takes two argument: the first is the vector of eigenvalues of the covariance matrix, the second is a matrix whose columns contain the corresponding eigenvectors of the covariance matrix.
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Properties
  NameDescription
Public propertyComponentMatrix
Gets the component matrix. This is a matrix whose columns are the eigenvectors of the covariance matrix scaled by the square root of their corresponding eigenvalues.
Public propertyCovarianceMatrix
Gets the covariance matrix whose eigenvalues are computed.
Public propertyEigenvalues
Gets the eigenvalues of the covariance matris.
Public propertyEigenvectors
Gets the eigenvalues of the covariance matrix as columns in a matrix.
Public propertyNumberOfFactorsToExtract
Gets the functor which returns the number of factors to retain given the eigenvalues as its first argument and the eigenvectors as its second argument.
Public propertyVarianceProportions
Gets a vector of proportion of variance explained by each factor. The ith entry corresoponds to the ith factor.
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Methods
  NameDescription
Public methodClone
Returns a deep copy of self.
Public methodExtractFactors
Uses the principle components algorithm for extracting factors from a covariance matrix.
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See Also