public class MillerUpdatingRegression extends Object implements UpdatingMultipleLinearRegression
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. 458478 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 and 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.

Modifier and Type  Method and 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.

public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance) throws ModelSpecificationException
numberOfVariables
 number of regressors to expect, not including constantincludeConstant
 include a constant automaticallyerrorTolerance
 zero tolerance, how machine zero is determinedModelSpecificationException
 if numberOfVariables is less than 1
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant) throws ModelSpecificationException
numberOfVariables
 maximum number of potential regressorsincludeConstant
 include a constant automaticallyModelSpecificationException
 if numberOfVariables is less than 1
public boolean hasIntercept()
hasIntercept
in interface UpdatingMultipleLinearRegression
public long getN()
getN
in interface UpdatingMultipleLinearRegression
public void addObservation(double[] x, double y) throws ModelSpecificationException
addObservation
in interface UpdatingMultipleLinearRegression
x
 the array with regressor valuesy
 the value of dependent variable given these regressorsModelSpecificationException
 if the length of x
does not equal
the number of independent variables in the modelpublic void addObservations(double[][] x, double[] y) throws ModelSpecificationException
addObservations
in interface UpdatingMultipleLinearRegression
x
 observations on the regressorsy
 observations on the regressandModelSpecificationException
 if x
is not rectangular, does not match
the length of y
or does not contain sufficient data to estimate the modelpublic void clear()
clear
in interface UpdatingMultipleLinearRegression
public double[] getPartialCorrelations(int in)
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 Yvariable starting with YCORR(IN+1) = partial correlation with the variable in position (IN+1).
in
 how many of the regressors to include (either in canonical
order, or in the current reordered state)public double getDiagonalOfHatMatrix(double[] row_data)
row_data
 returns the diagonal of the hat matrix for this observationpublic int[] getOrderOfRegressors()
public RegressionResults regress() throws ModelSpecificationException
regress
in interface UpdatingMultipleLinearRegression
ModelSpecificationException
  thrown if number of observations is
less than the number of variablespublic RegressionResults regress(int numberOfRegressors) throws ModelSpecificationException
numberOfRegressors
 many of the regressors to include (either in canonical
order, or in the current reordered state)ModelSpecificationException
  thrown if number of observations is
less than the number of variables or number of regressors requested
is greater than the regressors in the modelpublic RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException
regress
in interface UpdatingMultipleLinearRegression
variablesToInclude
 array of variables to include in regressionModelSpecificationException
  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 existCopyright © 2003–2016 The Apache Software Foundation. All rights reserved.