UpdatingMultipleLinearRegression (Apache Commons Math 3.3 API)

org.apache.commons.math3.stat.regression

## Interface UpdatingMultipleLinearRegression

- 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
  Version:
    $Id: UpdatingMultipleLinearRegression.java 1392342 2012-10-01 14:08:52Z psteitz $

- - ### 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

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