UpdatingMultipleLinearRegression (Apache Commons Math 3.6.1 API)

All Known Implementing Classes:

MillerUpdatingRegression, SimpleRegression
```
public interface UpdatingMultipleLinearRegression
```
An interface for regression models allowing for dynamic updating of the data. That is, the entire data set need not be loaded into memory. As observations become available, they can be added to the regression model and an updated estimate regression statistics can be calculated.

Since:

3.0

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`addObservation(double[] x, double y)` Adds one observation to the regression model.
`void`	`addObservations(double[][] x, double[] y)` Adds a series of observations to the regression model.
`void`	`clear()` Clears internal buffers and resets the regression model.
`long`	`getN()` Returns the number of observations added to the regression model.
`boolean`	`hasIntercept()` Returns true if a constant has been included false otherwise.
`RegressionResults`	`regress()` Performs a regression on data present in buffers and outputs a RegressionResults object
`RegressionResults`	`regress(int[] variablesToInclude)` Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object

- Method Detail
  - hasIntercept
```
boolean hasIntercept()
```
    Returns true if a constant has been included false otherwise.
    
    Returns:
    true if constant exists, false otherwise
  - getN
```
long getN()
```
    Returns the number of observations added to the regression model.
    
    Returns:
    Number of observations
  - addObservation
```
void addObservation(double[] x,
                  double y)
                    throws ModelSpecificationException
```
    Adds one observation to the regression model.
    
    Parameters:
    x - the independent variables which form the design matrix
    y - the dependent or response variable
    
    Throws:
    
    ModelSpecificationException - if the length of x does not equal the number of independent variables in the model
  - addObservations
```
void addObservations(double[][] x,
                   double[] y)
                     throws ModelSpecificationException
```
    Adds a series of observations to the regression model. The lengths of x and y must be the same and x must be rectangular.
    
    Parameters:
    x - a series of observations on the independent variables
    y - a series of observations on the dependent variable The length of x and y must be the same
    
    Throws:
    
    ModelSpecificationException - if x is not rectangular, does not match the length of y or does not contain sufficient data to estimate the model
  - clear
```
void clear()
```
    Clears internal buffers and resets the regression model. This means all data and derived values are initialized
  - regress
```
RegressionResults regress()
                          throws ModelSpecificationException,
                                 NoDataException
```
    Performs a regression on data present in buffers and outputs a RegressionResults object
    
    Returns:
    RegressionResults acts as a container of regression output
    
    Throws:
    
    ModelSpecificationException - if the model is not correctly specified
    
    NoDataException - if there is not sufficient data in the model to estimate the regression parameters
  - regress
```
RegressionResults regress(int[] variablesToInclude)
                          throws ModelSpecificationException,
                                 MathIllegalArgumentException
```
    Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object
    
    Parameters:
    variablesToInclude - an array of indices of regressors to include
    
    Returns:
    RegressionResults acts as a container of regression output
    
    Throws:
    
    ModelSpecificationException - if the model is not correctly specified
    
    MathIllegalArgumentException - if the variablesToInclude array is null or zero length

Interface UpdatingMultipleLinearRegression

Method Summary

Method Detail

hasIntercept

getN

addObservation

addObservations

clear

regress

regress