org.apache.commons.math.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 `QRDecompositionImpl` 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
• ### Constructor Summary

Constructors
Constructor and Description
`OLSMultipleLinearRegression()`
• ### Method Summary

All Methods
Modifier and Type Method and Description
`double` `calculateAdjustedRSquared()`
Returns the adjusted R-squared statistic, defined by the formula
`RealMatrix` `calculateHat()`
Compute the "hat" matrix.
`double` `calculateResidualSumOfSquares()`
Returns the sum of squared residuals.
`double` `calculateRSquared()`
Returns the R-Squared statistic, defined by the formula
`double` `calculateTotalSumOfSquares()`
Returns the sum of squared deviations of Y from its mean.
`void` ```newSampleData(double[] y, double[][] x)```
Loads model x and y sample data, overriding any previous sample.
`void` ```newSampleData(double[] data, int nobs, int nvars)```
Loads model x and y sample data from a flat input array, overriding any previous sample.
• ### Methods inherited from class org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression

`estimateErrorVariance, estimateRegressandVariance, estimateRegressionParameters, estimateRegressionParametersStandardErrors, estimateRegressionParametersVariance, estimateRegressionStandardError, estimateResiduals, isNoIntercept, setNoIntercept`
• ### Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### 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:
`java.lang.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 See Also: 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 calculateAdjustedRSquared public double calculateAdjustedRSquared() 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: adjusted R-Squared statistic Since: 2.2 See Also: AbstractMultipleLinearRegression.isNoIntercept() ```
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