Package org.hipparchus.stat.regression

Class MillerUpdatingRegression

java.lang.Object
org.hipparchus.stat.regression.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: Algorithm AS 274:
 Least Squares Routines to Supplement Those of Gentleman

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

    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.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • 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 Details

    • 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

    • addObservation

      public void addObservation(double[] x, double y) throws MathIllegalArgumentException
      Adds an observation to the regression model.
      Specified by:
      addObservation in interface UpdatingMultipleLinearRegression
      Parameters:
      x - the array with regressor values
      y - the value of dependent variable given these regressors
      Throws:
      MathIllegalArgumentException - if the length of x does not equal the number of independent variables in the model

    • addObservations

      public void addObservations(double[][] x, double[] y) throws MathIllegalArgumentException
      Adds multiple observations to the model.
      Specified by:
      addObservations in interface UpdatingMultipleLinearRegression
      Parameters:
      x - observations on the regressors
      y - observations on the regressand
      Throws:
      MathIllegalArgumentException - if x is not rectangular, does not match the length of y or does not contain sufficient data to estimate the model

    • 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() throws MathIllegalArgumentException
      Conducts a regression on the data in the model, using all regressors.
      Specified by:
      regress in interface UpdatingMultipleLinearRegression
      Returns:
      RegressionResults the structure holding all regression results
      Throws:
      MathIllegalArgumentException - - thrown if number of observations is less than the number of variables

    • 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