Package org.hipparchus.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.

    • Method Summary

      All Methods Instance Methods Abstract Methods 
      Modifier and Type Method 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 MathIllegalArgumentException
        Adds one observation to the regression model.
        Parameters:
        x - the independent variables which form the design matrix
        y - the dependent or response variable
        Throws:
        MathIllegalArgumentException - if the length of x does not equal the number of independent variables in the model

      • addObservations

        void addObservations​(double[][] x,
                     double[] y)
              throws MathIllegalArgumentException
        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:
        MathIllegalArgumentException - 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​(int[] variablesToInclude)
                   throws 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:
        MathIllegalArgumentException - if the model is not correctly specified
        MathIllegalArgumentException - if the variablesToInclude array is null or zero length