org.apache.commons.math3.stat.regression

## Class OLSMultipleLinearRegression

• All Implemented Interfaces:
MultipleLinearRegression

public class OLSMultipleLinearRegression
extends AbstractMultipleLinearRegression

Implements ordinary least squares (OLS) to estimate the parameters of a multiple linear regression model.

The regression coefficients, b, satisfy the normal equations:

XT X b = XT y

To solve the normal equations, this implementation uses QR decomposition of the X matrix. (See QRDecomposition for details on the decomposition algorithm.) The X matrix, also known as the design matrix, has rows corresponding to sample observations and columns corresponding to independent variables. When the model is estimated using an intercept term (i.e. when isNoIntercept is false as it is by default), the X matrix includes an initial column identically equal to 1. We solve the normal equations as follows:

XTX b = XT y
(QR)T (QR) b = (QR)Ty
RT (QTQ) R b = RT QT y
RT R b = RT QT y
(RT)-1 RT R b = (RT)-1 RT QT y
R b = QT y

Given Q and R, the last equation is solved by back-substitution.

Since:
2.0
Version:
\$Id: OLSMultipleLinearRegression.java 1296570 2012-03-03 03:35:20Z erans \$
• ### Constructor Detail

• #### OLSMultipleLinearRegression

public OLSMultipleLinearRegression()
• ### Method Detail

• #### newSampleData

public void newSampleData(double[] y,
double[][] x)
Loads model x and y sample data, overriding any previous sample. Computes and caches QR decomposition of the X matrix.
Parameters:
y - the [n,1] array representing the y sample
x - the [n,k] array representing the x sample
Throws:
IllegalArgumentException - if the x and y array data are not compatible for the regression
• #### newSampleData

public void newSampleData(double[] data,
int nobs,
int nvars)

Loads model x and y sample data from a flat input array, overriding any previous sample.

Assumes that rows are concatenated with y values first in each row. For example, an input data array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with nobs = 3 and nvars = 2 creates a regression dataset with two independent variables, as below:

y   x[0]  x[1]
--------------
1     2     3
4     5     6
7     8     9

Note that there is no need to add an initial unitary column (column of 1's) when specifying a model including an intercept term. If AbstractMultipleLinearRegression.isNoIntercept() is true, the X matrix will be created without an initial column of "1"s; otherwise this column will be added.

Throws IllegalArgumentException if any of the following preconditions fail:

• data cannot be null
• data.length = nobs * (nvars + 1)
• nobs > nvars

This implementation computes and caches the QR decomposition of the X matrix.

Overrides:
newSampleData in class AbstractMultipleLinearRegression
Parameters:
data - input data array
nobs - number of observations (rows)
nvars - number of independent variables (columns, not counting y)
• #### calculateHat

public RealMatrix calculateHat()

Compute the "hat" matrix.

The hat matrix is defined in terms of the design matrix X by X(XTX)-1XT

The implementation here uses the QR decomposition to compute the hat matrix as Q IpQT where Ip is the p-dimensional identity matrix augmented by 0's. This computational formula is from "The Hat Matrix in Regression and ANOVA", David C. Hoaglin and Roy E. Welsch, The American Statistician, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.

Returns:
the hat matrix
• #### calculateTotalSumOfSquares

public double calculateTotalSumOfSquares()

Returns the sum of squared deviations of Y from its mean.

If the model has no intercept term, 0 is used for the mean of Y - i.e., what is returned is the sum of the squared Y values.

The value returned by this method is the SSTO value used in the R-squared computation.

Returns:
SSTO - the total sum of squares
Since:
2.2
AbstractMultipleLinearRegression.isNoIntercept()
• #### calculateResidualSumOfSquares

public double calculateResidualSumOfSquares()
Returns the sum of squared residuals.
Returns:
residual sum of squares
Since:
2.2
• #### calculateRSquared

public double calculateRSquared()
Returns the R-Squared statistic, defined by the formula
R2 = 1 - SSR / SSTO

where SSR is the sum of squared residuals and SSTO is the total sum of squares
Returns:
R-square statistic
Since:
2.2

Returns the adjusted R-squared statistic, defined by the formula

R2adj = 1 - [SSR (n - 1)] / [SSTO (n - p)]

where SSR is the sum of squared residuals, SSTO is the total sum of squares, n is the number of observations and p is the number of parameters estimated (including the intercept).

If the regression is estimated without an intercept term, what is returned is

1 - (1 - calculateRSquared()) * (n / (n - p))

Returns:
Since:
2.2
AbstractMultipleLinearRegression.isNoIntercept()
• #### newXSampleData

protected void newXSampleData(double[][] x)

Loads new x sample data, overriding any previous data.

The input x array should have one row for each sample observation, with columns corresponding to independent variables. For example, if
x = new double[][] {{1, 2}, {3, 4}, {5, 6}}
then setXSampleData(x) results in a model with two independent variables and 3 observations:
x[0]  x[1]
----------
1    2
3    4
5    6

Note that there is no need to add an initial unitary column (column of 1's) when specifying a model including an intercept term.

This implementation computes and caches the QR decomposition of the X matrix once it is successfully loaded.

Overrides:
newXSampleData in class AbstractMultipleLinearRegression
Parameters:
x - the rectangular array representing the x sample
• #### calculateBetaVariance

protected RealMatrix calculateBetaVariance()

Calculates the variance-covariance matrix of the regression parameters.

Var(b) = (XTX)-1

Uses QR decomposition to reduce (XTX)-1 to (RTR)-1, with only the top p rows of R included, where p = the length of the beta vector.

Specified by:
calculateBetaVariance in class AbstractMultipleLinearRegression
Returns:
The beta variance-covariance matrix