Package org.hipparchus.stat.regression

Class MillerUpdatingRegression

  • All Implemented Interfaces:
    UpdatingMultipleLinearRegression
    public class MillerUpdatingRegression
extends Object
implements UpdatingMultipleLinearRegression
    This class is a concrete implementation of the UpdatingMultipleLinearRegression interface.

    The algorithm is described in: 

     Algorithm AS 274: Least Squares Routines to Supplement Those of Gentleman
 Author(s): Alan J. Miller
 Source: Journal of the Royal Statistical Society.
 Series C (Applied Statistics), Vol. 41, No. 2
 (1992), pp. 458-478
 Published by: Blackwell Publishing for the Royal Statistical Society
 Stable URL: http://www.jstor.org/stable/2347583

    This method for multiple regression forms the solution to the OLS problem by updating the QR decomposition as described by Gentleman.

    • Constructor Summary

      Constructors 
      Constructor Description
      MillerUpdatingRegression​(int numberOfVariables, boolean includeConstant)
      Primary constructor for the MillerUpdatingRegression.
      MillerUpdatingRegression​(int numberOfVariables, boolean includeConstant, double errorTolerance)
      This is the augmented constructor for the MillerUpdatingRegression class.

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      void addObservation​(double[] x, double y)
      Adds an observation to the regression model.
      void addObservations​(double[][] x, double[] y)
      Adds multiple observations to the model.
      void clear()
      As the name suggests, clear wipes the internals and reorders everything in the canonical order.
      double getDiagonalOfHatMatrix​(double[] row_data)
      Gets the diagonal of the Hat matrix also known as the leverage matrix.
      long getN()
      Gets the number of observations added to the regression model.
      int[] getOrderOfRegressors()
      Gets the order of the regressors, useful if some type of reordering has been called.
      double[] getPartialCorrelations​(int in)
      In the original algorithm only the partial correlations of the regressors is returned to the user.
      boolean hasIntercept()
      A getter method which determines whether a constant is included.
      RegressionResults regress()
      Conducts a regression on the data in the model, using all regressors.
      RegressionResults regress​(int numberOfRegressors)
      Conducts a regression on the data in the model, using a subset of regressors.
      RegressionResults regress​(int[] variablesToInclude)
      Conducts a regression on the data in the model, using regressors in array Calling this method will change the internal order of the regressors and care is required in interpreting the hatmatrix.

    • Constructor Detail

      • MillerUpdatingRegression

        public MillerUpdatingRegression​(int numberOfVariables,
                                boolean includeConstant,
                                double errorTolerance)
                         throws MathIllegalArgumentException
        This is the augmented constructor for the MillerUpdatingRegression class.
        Parameters:
        numberOfVariables - number of regressors to expect, not including constant
        includeConstant - include a constant automatically
        errorTolerance - zero tolerance, how machine zero is determined
        Throws:
        MathIllegalArgumentException - if numberOfVariables is less than 1

      • MillerUpdatingRegression

        public MillerUpdatingRegression​(int numberOfVariables,
                                boolean includeConstant)
                         throws MathIllegalArgumentException
        Primary constructor for the MillerUpdatingRegression.
        Parameters:
        numberOfVariables - maximum number of potential regressors
        includeConstant - include a constant automatically
        Throws:
        MathIllegalArgumentException - if numberOfVariables is less than 1

    • Method Detail

      • hasIntercept

        public boolean hasIntercept()
        A getter method which determines whether a constant is included.
        Specified by:
        hasIntercept in interface UpdatingMultipleLinearRegression
        Returns:
        true regression has an intercept, false no intercept

      • getN

        public long getN()
        Gets the number of observations added to the regression model.
        Specified by:
        getN in interface UpdatingMultipleLinearRegression
        Returns:
        number of observations

      • clear

        public void clear()
        As the name suggests, clear wipes the internals and reorders everything in the canonical order.
        Specified by:
        clear in interface UpdatingMultipleLinearRegression

      • getPartialCorrelations

        public double[] getPartialCorrelations​(int in)
        In the original algorithm only the partial correlations of the regressors is returned to the user. In this implementation, we have 
         corr =
 {
   corrxx - lower triangular
   corrxy - bottom row of the matrix
 }
 Replaces subroutines PCORR and COR of:
 ALGORITHM AS274  APPL. STATIST. (1992) VOL.41, NO. 2

        Calculate partial correlations after the variables in rows 1, 2, ..., IN have been forced into the regression. If IN = 1, and the first row of R represents a constant in the model, then the usual simple correlations are returned.

        If IN = 0, the value returned in array CORMAT for the correlation of variables Xi & Xj is: 

         sum ( Xi.Xj ) / Sqrt ( sum (Xi^2) . sum (Xj^2) )

        On return, array CORMAT contains the upper triangle of the matrix of partial correlations stored by rows, excluding the 1's on the diagonal. e.g. if IN = 2, the consecutive elements returned are: (3,4) (3,5) ... (3,ncol), (4,5) (4,6) ... (4,ncol), etc. Array YCORR stores the partial correlations with the Y-variable starting with YCORR(IN+1) = partial correlation with the variable in position (IN+1).

        Parameters:
        in - how many of the regressors to include (either in canonical order, or in the current reordered state)
        Returns:
        an array with the partial correlations of the remainder of regressors with each other and the regressand, in lower triangular form

      • getDiagonalOfHatMatrix

        public double getDiagonalOfHatMatrix​(double[] row_data)
        Gets the diagonal of the Hat matrix also known as the leverage matrix.
        Parameters:
        row_data - returns the diagonal of the hat matrix for this observation
        Returns:
        the diagonal element of the hatmatrix

      • getOrderOfRegressors

        public int[] getOrderOfRegressors()
        Gets the order of the regressors, useful if some type of reordering has been called. Calling regress with int[]{} args will trigger a reordering.
        Returns:
        int[] with the current order of the regressors

      • regress

        public RegressionResults regress​(int numberOfRegressors)
                          throws MathIllegalArgumentException
        Conducts a regression on the data in the model, using a subset of regressors.
        Parameters:
        numberOfRegressors - many of the regressors to include (either in canonical order, or in the current reordered state)
        Returns:
        RegressionResults the structure holding all regression results
        Throws:
        MathIllegalArgumentException - - thrown if number of observations is less than the number of variables or number of regressors requested is greater than the regressors in the model

      • regress

        public RegressionResults regress​(int[] variablesToInclude)
                          throws MathIllegalArgumentException
        Conducts a regression on the data in the model, using regressors in array Calling this method will change the internal order of the regressors and care is required in interpreting the hatmatrix.
        Specified by:
        regress in interface UpdatingMultipleLinearRegression
        Parameters:
        variablesToInclude - array of variables to include in regression
        Returns:
        RegressionResults the structure holding all regression results
        Throws:
        MathIllegalArgumentException - - thrown if number of observations is less than the number of variables, the number of regressors requested is greater than the regressors in the model or a regressor index in regressor array does not exist