AbstractMultipleLinearRegression (Apache Commons Math 3.6.1 API)

java.lang.Object
- org.apache.commons.math3.stat.regression.AbstractMultipleLinearRegression

All Implemented Interfaces:

MultipleLinearRegression

Direct Known Subclasses:

GLSMultipleLinearRegression, OLSMultipleLinearRegression
```
public abstract class AbstractMultipleLinearRegression
extends Object
implements MultipleLinearRegression
```
Abstract base class for implementations of MultipleLinearRegression.

Since:

2.0

Constructor Summary

Constructors
Constructor and Description

AbstractMultipleLinearRegression()

Constructors
Constructor and Description
`AbstractMultipleLinearRegression()`

Method Summary

Methods
Modifier and Type	Method and Description
`protected abstract RealVector`	`calculateBeta()` Calculates the beta of multiple linear regression in matrix notation.
`protected abstract RealMatrix`	`calculateBetaVariance()` Calculates the beta variance of multiple linear regression in matrix notation.
`protected double`	`calculateErrorVariance()` Calculates the variance of the error term.
`protected RealVector`	`calculateResiduals()` Calculates the residuals of multiple linear regression in matrix notation.
`protected double`	`calculateYVariance()` Calculates the variance of the y values.
`double`	`estimateErrorVariance()` Estimates the variance of the error.
`double`	`estimateRegressandVariance()` Returns the variance of the regressand, ie Var(y).
`double[]`	`estimateRegressionParameters()` Estimates the regression parameters b.
`double[]`	`estimateRegressionParametersStandardErrors()` Returns the standard errors of the regression parameters.
`double[][]`	`estimateRegressionParametersVariance()` Estimates the variance of the regression parameters, ie Var(b).
`double`	`estimateRegressionStandardError()` Estimates the standard error of the regression.
`double[]`	`estimateResiduals()` Estimates the residuals, ie u = y - X*b.
`protected RealMatrix`	`getX()`
`protected RealVector`	`getY()`
`boolean`	`isNoIntercept()`
`void`	`newSampleData(double[] data, int nobs, int nvars)` Loads model x and y sample data from a flat input array, overriding any previous sample.
`protected void`	`newXSampleData(double[][] x)` Loads new x sample data, overriding any previous data.
`protected void`	`newYSampleData(double[] y)` Loads new y sample data, overriding any previous data.
`void`	`setNoIntercept(boolean noIntercept)`
`protected void`	`validateCovarianceData(double[][] x, double[][] covariance)` Validates that the x data and covariance matrix have the same number of rows and that the covariance matrix is square.
`protected void`	`validateSampleData(double[][] x, double[] y)` Validates sample data.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - AbstractMultipleLinearRegression
```
public AbstractMultipleLinearRegression()
```
- Method Detail
  - getX
```
protected RealMatrix getX()
```
    Returns:
    the X sample data.
  - getY
```
protected RealVector getY()
```
    Returns:
    the Y sample data.
  - isNoIntercept
```
public boolean isNoIntercept()
```
    Returns:
    true if the model has no intercept term; false otherwise
    Since:
    
    2.2
  - setNoIntercept
```
public void setNoIntercept(boolean noIntercept)
```
    Parameters:
    noIntercept - true means the model is to be estimated without an intercept term
    Since:
    
    2.2
  - 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 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)
    Parameters: data - input data array nobs - number of observations (rows) nvars - number of independent variables (columns, not counting y) Throws: NullArgumentException - if the data array is null DimensionMismatchException - if the length of the data array is not equal to nobs * (nvars + 1) InsufficientDataException - if nobs is less than nvars + 1
  newYSampleData protected void newYSampleData(double[] y) Loads new y sample data, overriding any previous data. Parameters: y - the array representing the y sample Throws: NullArgumentException - if y is null NoDataException - if y is empty 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. Parameters: x - the rectangular array representing the x sample Throws: NullArgumentException - if x is null NoDataException - if x is empty DimensionMismatchException - if x is not rectangular validateSampleData protected void validateSampleData(double[][] x, double[] y) throws MathIllegalArgumentException Validates sample data. Checks that Neither x nor y is null or empty; The length (i.e. number of rows) of x equals the length of y x has at least one more row than it has columns (i.e. there is sufficient data to estimate regression coefficients for each of the columns in x plus an intercept. Parameters: x - the [n,k] array representing the x data y - the [n,1] array representing the y data Throws: NullArgumentException - if x or y is null DimensionMismatchException - if x and y do not have the same length NoDataException - if x or y are zero-length MathIllegalArgumentException - if the number of rows of x is not larger than the number of columns + 1 validateCovarianceData protected void validateCovarianceData(double[][] x, double[][] covariance) Validates that the x data and covariance matrix have the same number of rows and that the covariance matrix is square. Parameters: x - the [n,k] array representing the x sample covariance - the [n,n] array representing the covariance matrix Throws: DimensionMismatchException - if the number of rows in x is not equal to the number of rows in covariance NonSquareMatrixException - if the covariance matrix is not square estimateRegressionParameters public double[] estimateRegressionParameters() Estimates the regression parameters b. Specified by: estimateRegressionParameters in interface MultipleLinearRegression Returns: The [k,1] array representing b estimateResiduals public double[] estimateResiduals() Estimates the residuals, ie u = y - X*b. Specified by: estimateResiduals in interface MultipleLinearRegression Returns: The [n,1] array representing the residuals estimateRegressionParametersVariance public double[][] estimateRegressionParametersVariance() Estimates the variance of the regression parameters, ie Var(b). Specified by: estimateRegressionParametersVariance in interface MultipleLinearRegression Returns: The [k,k] array representing the variance of b estimateRegressionParametersStandardErrors public double[] estimateRegressionParametersStandardErrors() Returns the standard errors of the regression parameters. Specified by: estimateRegressionParametersStandardErrors in interface MultipleLinearRegression Returns: standard errors of estimated regression parameters estimateRegressandVariance public double estimateRegressandVariance() Returns the variance of the regressand, ie Var(y). Specified by: estimateRegressandVariance in interface MultipleLinearRegression Returns: The double representing the variance of y estimateErrorVariance public double estimateErrorVariance() Estimates the variance of the error. Returns: estimate of the error variance Since: 2.2 estimateRegressionStandardError public double estimateRegressionStandardError() Estimates the standard error of the regression. Returns: regression standard error Since: 2.2 calculateBeta protected abstract RealVector calculateBeta() Calculates the beta of multiple linear regression in matrix notation. Returns: beta calculateBetaVariance protected abstract RealMatrix calculateBetaVariance() Calculates the beta variance of multiple linear regression in matrix notation. Returns: beta variance calculateYVariance protected double calculateYVariance() Calculates the variance of the y values. Returns: Y variance calculateErrorVariance protected double calculateErrorVariance() Calculates the variance of the error term. Uses the formula var(u) = u · u / (n - k) where n and k are the row and column dimensions of the design matrix X. Returns: error variance estimate Since: 2.2 calculateResiduals protected RealVector calculateResiduals() Calculates the residuals of multiple linear regression in matrix notation. u = y - X * b Returns: The residuals [n,1] matrix

Class AbstractMultipleLinearRegression

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

AbstractMultipleLinearRegression

Method Detail

getX

getY

isNoIntercept

setNoIntercept

newSampleData

newYSampleData

newXSampleData

validateSampleData

validateCovarianceData

estimateRegressionParameters

estimateResiduals

estimateRegressionParametersVariance

estimateRegressionParametersStandardErrors

estimateRegressandVariance

estimateErrorVariance

estimateRegressionStandardError

calculateBeta

calculateBetaVariance

calculateYVariance

calculateErrorVariance

calculateResiduals