RegressionResults (patrimoine de base SIRIUS 4.13 API)

java.lang.Object
- fr.cnes.sirius.patrius.math.stat.regression.RegressionResults

All Implemented Interfaces:

Serializable
```
public class RegressionResults
extends Object
implements Serializable
```
Results of a Multiple Linear Regression model fit.

Since:

3.0

Version:

$Id: RegressionResults.java 18108 2017-10-04 06:45:27Z bignon $

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`RegressionResults(double[] parametersIn, double[][] varcov, boolean isSymmetricCompressed, long nobsIn, int rankIn, double sumy, double sumysq, double sse, boolean containsConstantIn, boolean copyData)` Constructor for Regression Results.

Method Summary

All Methods Instance Methods Concrete 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(b_index).
`double[]`	`getStdErrorOfEstimates()` Returns the standard error of the parameter estimates, usually denoted s(b_i).
`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[] parametersIn,
                         double[][] varcov,
                         boolean isSymmetricCompressed,
                         long nobsIn,
                         int rankIn,
                         double sumy,
                         double sumysq,
                         double sse,
                         boolean containsConstantIn,
                         boolean copyData)
```
    Constructor for Regression Results.
    
    Parameters:
    
    parametersIn - 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
    
    nobsIn - the number of observations of the regression estimation
    
    rankIn - 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
    
    containsConstantIn - 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)
```
    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)
```
    Returns the standard error of the parameter estimate at index, usually denoted s(b_index).
    
    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(b_i).
    
    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)
```
    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]
  - getAdjustedRSquared
```
public double getAdjustedRSquared()
```
    Returns the adjusted R-squared statistic, defined by the formula
```
 R²_adj = 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
  - 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 the parameter estimates.
    
    Returns:
    
    true if the model has an intercept term

Class RegressionResults

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

RegressionResults

Method Detail

getParameterEstimate

getParameterEstimates

getStdErrorOfEstimate

getStdErrorOfEstimates

getCovarianceOfParameters

getNumberOfParameters

getN

getTotalSumSquares

getRegressionSumSquares

getErrorSumSquares

getMeanSquareError

getRSquared

getAdjustedRSquared

hasIntercept