org.apache.commons.math.stat.regression
Class AbstractMultipleLinearRegression

java.lang.Object
  extended by org.apache.commons.math.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
AbstractMultipleLinearRegression()
           
 
Method Summary
 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.
 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.
 void setNoIntercept(boolean noIntercept)
           
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

AbstractMultipleLinearRegression

public AbstractMultipleLinearRegression()
Method Detail

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:

Parameters:
data - input data array
nobs - number of observations (rows)
nvars - number of independent variables (columns, not counting y)
Throws:
IllegalArgumentException - if the preconditions are not met

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


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