Class RegressionResults
- All Implemented Interfaces:
Serializable
- Since:
- 3.0
- See Also:
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Field Summary
FieldsModifier and TypeFieldDescriptionprivate static final int
INDEX of Adjusted R Squaredprivate final boolean
boolean flag indicator of whether a constant was includedprivate final double[]
array storing global results, SSE, MSE, RSQ, adjRSQprivate final boolean
boolean flag for variance covariance matrix in symm compressed storageprivate static final int
INDEX of Mean Squared Errorprivate final long
number of observations on which results are basedprivate final double[]
regression slope parametersprivate final int
rank of the solutionprivate static final int
INDEX of R-Squared of regressionprivate static final long
UIDprivate static final int
INDEX of Sum of Squared Errorsprivate static final int
INDEX of Sum of Squares of Modelprivate final double[][]
variance covariance matrix of parameters -
Constructor Summary
ConstructorsModifierConstructorDescriptionprivate
Set the default constructor to private access to prevent inadvertent instantiationRegressionResults
(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
Modifier and TypeMethodDescriptiondouble
Returns the adjusted R-squared statistic, defined by the formuladouble
getCovarianceOfParameters
(int i, int j) Returns the covariance between regression parameters i and j.double
Returns the sum of squared errors (SSE) associated with the regression model.double
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
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[]
Returns a copy of the regression parameters estimates.double
Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).double
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[]
Returns the standard error of the parameter estimates, usually denoted s(bi).double
Returns the sum of squared deviations of the y values about their mean.private double
getVcvElement
(int i, int j) Gets the i-jth element of the variance-covariance matrix.boolean
Returns true if the regression model has been computed including an intercept.
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Field Details
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SSE_IDX
private static final int SSE_IDXINDEX of Sum of Squared Errors- See Also:
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SST_IDX
private static final int SST_IDXINDEX of Sum of Squares of Model- See Also:
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RSQ_IDX
private static final int RSQ_IDXINDEX of R-Squared of regression- See Also:
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MSE_IDX
private static final int MSE_IDXINDEX of Mean Squared Error- See Also:
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ADJRSQ_IDX
private static final int ADJRSQ_IDXINDEX of Adjusted R Squared- See Also:
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serialVersionUID
private static final long serialVersionUIDUID- See Also:
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parameters
private final double[] parametersregression slope parameters -
varCovData
private final double[][] varCovDatavariance covariance matrix of parameters -
isSymmetricVCD
private final boolean isSymmetricVCDboolean flag for variance covariance matrix in symm compressed storage -
rank
private final int rankrank of the solution -
nobs
private final long nobsnumber of observations on which results are based -
containsConstant
private final boolean containsConstantboolean flag indicator of whether a constant was included -
globalFitInfo
private final double[] globalFitInfoarray storing global results, SSE, MSE, RSQ, adjRSQ
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Constructor Details
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RegressionResults
private RegressionResults()Set the default constructor to private access to prevent inadvertent instantiation -
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 estimatesvarcov
- the variance covariance matrix, stored either in a square matrix or as a compressedisSymmetricCompressed
- a flag which denotes that the variance covariance matrix is in symmetric compressed formatnobs
- the number of observations of the regression estimationrank
- the number of independent variables in the regressionsumy
- the sum of the independent variablesumysq
- the sum of the squared independent variablesse
- sum of squared errorscontainsConstant
- true model has constant, false model does not have constantcopyData
- if true a deep copy of all input data is made, if false only references are copied and the RegressionResults become mutable
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Method Details
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getParameterEstimate
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
- ifindex
is not in the interval[0, number of parameters)
.
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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
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getStdErrorOfEstimate
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
- ifindex
is not in the interval[0, number of parameters)
.
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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
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getCovarianceOfParameters
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
-i
th regression parameter.j
-j
th regression parameter.- Returns:
- the covariance of the parameter estimates.
- Throws:
OutOfRangeException
- ifi
orj
is not in the interval[0, number of parameters)
.
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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
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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
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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 returnsDouble.NaN
.- Returns:
- sum of squared deviations of y values
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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
- 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,
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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
- numberOfParameters data pairs
must have been added before invoking this method. If this method is
invoked before a model can be estimated,
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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 returnsDouble.NaN
.- Returns:
- sum of squared deviations of y values
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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]
- 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,
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getAdjustedRSquared
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:
- adjusted R-Squared statistic
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hasIntercept
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 theparameter estimates
.- Returns:
- true if the model has an intercept term
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getVcvElement
private double getVcvElement(int i, int j) Gets the i-jth element of the variance-covariance matrix.- Parameters:
i
- first variable indexj
- second variable index- Returns:
- the requested variance-covariance matrix entry
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