public sealed class PrincipalComponentAnalysis : TransformationModel

Public NotInheritable Class PrincipalComponentAnalysis
	Inherits TransformationModel

public ref class PrincipalComponentAnalysis sealed : public TransformationModel

[<SealedAttribute>]
type PrincipalComponentAnalysis =  
    class
        inherit TransformationModel
    end

The PrincipalComponentAnalysis type exposes the following members.

	Name	Description
	PrincipalComponentAnalysis(IDataFrame)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(MatrixDouble)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(Matrix)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(NumericalVariable)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(VectorDouble)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(DataTable, String)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(IDataFrame, String)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(IDataFrame, String)	Constructs a new PrincipalComponentAnalysis object.
	PrincipalComponentAnalysis(VariableCollection, String)	Constructs a new PrincipalComponentAnalysis object.

	Name	Description
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	ComponentMatrix	Gets the matrix whose columns contain the principal components.
	Components	Returns the collection of principal components.
	Computed	Gets whether the regression model has been computed. (Inherited from Model.)
	CumulativeVarianceProportions	Returns the cumulative proportion of the variance that is explained by the components.
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DependentVariables	Gets the collection of dependent variables associated with this model. (Inherited from Model.)
	Features	Gets a matrix that contains the features. (Inherited from TransformationModel.)
	IndependentVariables	Gets the collection of independent variables associated with this model. (Inherited from Model.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	NumberOfComponents	Gets or sets the number of components to retain.
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from MultivariateModel.)
	RowCount	Gets the number of observations.
	ScalingMethod	Gets or sets the method used to scale the variables.
	ScoreMatrix	Gets the matrix of component scores.
	Standardize	Obsolete. Gets or sets whether the variables should be standardized prior to computing the principal components.
	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.)
	VarianceProportions	Returns the proportion of the variance that is explained by each of the components.
	Weights	Gets or sets the actual weights. (Inherited from Model.)

	Name	Description
	Compute	Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Computes the model. (Inherited from Model.)
	Contains	Returns whether another UnivariateModel is nested within this instance. (Inherited from Model.)
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	Fit	Fits the model to the data. (Inherited from Model.)
	Fit(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetPredictions	Returns the predicted values of the variables based on the specified number of components.
	GetPredictionsMatrix	Returns the predicted values of the variables based on the specified number of components as a matrix.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetVarianceThreshold	Returns the number of components needed to explain the requested proportion of the variance in the data.
	InverseTransform	Applies the inverse transformation to a set of observations. (Overrides TransformationModelInverseTransform(MatrixDouble).)
	ResetComputation	Clears all computed 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 Model.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform	Applies the transformation to a set of observations. (Overrides TransformationModelTransform(MatrixDouble).)

Use the PrincipalComponentAnalysis class to obtain information about the components that contribute the most to the variation in a dataset. The data for the analysis can be supplied in a variety of formats, including as a data frame, a DataTable, an array of numerical vectors, or a matrix.

The Standardize property determines whether each variable should be rescaled to have zero mean and unit standard deviation. The Compute(ParallelOptions) method performs the actual calculation.

Once the analysis has been completed, the Components property provides access to a collection of PrincipalComponent objects that provide details about each of the principal components. The components are sorted in order of their contribution to the variance in the data, in descending order. The ComponentMatrix property returns the components as the columns of a matrix. The ScoreMatrix property expresses the observations in terms of the components. The GetPredictions(Int32) method returns the observations if only the specified number of components is taken into account.

The VarianceProportions and CumulativeVarianceProportions summarize the contribution of each component. The GetVarianceThreshold(Double) calculates how many components are needed to explain a certain proportion of the variation.

Numerical Libraries

Reference