org.apache.commons.math3.stat.regression

## Class RegressionResults

• All Implemented Interfaces:
Serializable

public class RegressionResults
extends Object
implements Serializable
Results of a Multiple Linear Regression model fit.
Since:
3.0
Serialized Form
• ### Constructor Summary

Constructors
Constructor and Description
RegressionResults(double[] parameters, double[][] varcov, boolean isSymmetricCompressed, long nobs, int rank, double sumy, double sumysq, double sse, boolean containsConstant, boolean copyData)
Constructor for Regression Results.
• ### Method Summary

Methods
Modifier and Type Method and Description
double getAdjustedRSquared()
Returns the adjusted R-squared statistic, defined by the formula
double getCovarianceOfParameters(int i, int j)
Returns the covariance between regression parameters i and j.
double getErrorSumSquares()
Returns the sum of squared errors (SSE) associated with the regression model.
double getMeanSquareError()
Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
long getN()
Returns the number of observations added to the regression model.
int getNumberOfParameters()
Returns the number of parameters estimated in the model.
double getParameterEstimate(int index)
Returns the parameter estimate for the regressor at the given index.
double[] getParameterEstimates()
Returns a copy of the regression parameters estimates.
double getRegressionSumSquares()
Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
double getRSquared()
Returns the coefficient of multiple determination, usually denoted r-square.
double getStdErrorOfEstimate(int index)
Returns the standard error of the parameter estimate at index, usually denoted s(bindex).
double[] getStdErrorOfEstimates()
Returns the standard error of the parameter estimates, usually denoted s(bi).
double getTotalSumSquares()
Returns the sum of squared deviations of the y values about their mean.
boolean hasIntercept()
Returns true if the regression model has been computed including an intercept.
• ### Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### RegressionResults

public RegressionResults(double[] parameters,
double[][] varcov,
boolean isSymmetricCompressed,
long nobs,
int rank,
double sumy,
double sumysq,
double sse,
boolean containsConstant,
boolean copyData)
Constructor for Regression Results.
Parameters:
parameters - a double array with the regression slope estimates
varcov - the variance covariance matrix, stored either in a square matrix or as a compressed
isSymmetricCompressed - a flag which denotes that the variance covariance matrix is in symmetric compressed format
nobs - the number of observations of the regression estimation
rank - the number of independent variables in the regression
sumy - the sum of the independent variable
sumysq - the sum of the squared independent variable
sse - sum of squared errors
containsConstant - true model has constant, false model does not have constant
copyData - if true a deep copy of all input data is made, if false only references are copied and the RegressionResults become mutable
• ### Method Detail

• #### getParameterEstimate

public double getParameterEstimate(int index)
throws OutOfRangeException

Returns the parameter estimate for the regressor at the given index.

A redundant regressor will have its redundancy flag set, as well as a parameters estimated equal to Double.NaN

Parameters:
index - Index.
Returns:
the parameters estimated for regressor at index.
Throws:
OutOfRangeException - if index is not in the interval [0, number of parameters).
• #### getParameterEstimates

public double[] getParameterEstimates()

Returns a copy of the regression parameters estimates.

The parameter estimates are returned in the natural order of the data.

A redundant regressor will have its redundancy flag set, as will a parameter estimate equal to Double.NaN.

Returns:
array of parameter estimates, null if no estimation occurred
• #### getStdErrorOfEstimate

public double getStdErrorOfEstimate(int index)
throws OutOfRangeException
Returns the standard error of the parameter estimate at index, usually denoted s(bindex).
Parameters:
index - Index.
Returns:
the standard errors associated with parameters estimated at index.
Throws:
OutOfRangeException - if index is not in the interval [0, number of parameters).
• #### getStdErrorOfEstimates

public double[] getStdErrorOfEstimates()

Returns the standard error of the parameter estimates, usually denoted s(bi).

If there are problems with an ill conditioned design matrix then the regressor which is redundant will be assigned Double.NaN.

Returns:
an array standard errors associated with parameters estimates, null if no estimation occurred
• #### getCovarianceOfParameters

public double getCovarianceOfParameters(int i,
int j)
throws OutOfRangeException

Returns the covariance between regression parameters i and j.

If there are problems with an ill conditioned design matrix then the covariance which involves redundant columns will be assigned Double.NaN.

Parameters:
i - ith regression parameter.
j - jth regression parameter.
Returns:
the covariance of the parameter estimates.
Throws:
OutOfRangeException - if i or j is not in the interval [0, number of parameters).
• #### getNumberOfParameters

public int getNumberOfParameters()

Returns the number of parameters estimated in the model.

This is the maximum number of regressors, some techniques may drop redundant parameters

Returns:
number of regressors, -1 if not estimated
• #### getN

public long getN()
Returns the number of observations added to the regression model.
Returns:
Number of observations, -1 if an error condition prevents estimation
• #### getTotalSumSquares

public double getTotalSumSquares()

Returns the sum of squared deviations of the y values about their mean.

This is defined as SSTO here.

If n < 2, this returns Double.NaN.

Returns:
sum of squared deviations of y values
• #### getRegressionSumSquares

public double getRegressionSumSquares()

Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).

This is usually abbreviated SSR or SSM. It is defined as SSM here

Preconditions:

• At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double.NaN is returned.

Returns:
sum of squared deviations of predicted y values
• #### getErrorSumSquares

public double getErrorSumSquares()

Returns the sum of squared errors (SSE) associated with the regression model.

The return value is constrained to be non-negative - i.e., if due to rounding errors the computational formula returns a negative result, 0 is returned.

Preconditions:

• numberOfParameters data pairs must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:
sum of squared errors associated with the regression model
• #### getMeanSquareError

public double getMeanSquareError()

Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.

If there are fewer than numberOfParameters + 1 data pairs in the model, or if there is no variation in x, this returns Double.NaN.

Returns:
sum of squared deviations of y values
• #### getRSquared

public double getRSquared()

Returns the coefficient of multiple determination, usually denoted r-square.

Preconditions:

• At least numberOfParameters observations (with at least numberOfParameters different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:
r-square, a double in the interval [0, 1]

public double getAdjustedRSquared()

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 - getRSquared() ) * (n / (n - p)) 


Returns:
public boolean hasIntercept()
Returns true if the regression model has been computed including an intercept. In this case, the coefficient of the intercept is the first element of the parameter estimates.