Package org.apache.commons.math.stat.regression

Class AbstractMultipleLinearRegression

    • Constructor Detail

      • AbstractMultipleLinearRegression

        public AbstractMultipleLinearRegression()

    • Method Detail

      • isNoIntercept

        public boolean isNoIntercept()
        Returns:
        true if the model has no intercept term; false otherwise
        Since:
        2.2

      • setNoIntercept

        public void setNoIntercept​(boolean noIntercept)
        Parameters:
        noIntercept - true means the model is to be estimated without an intercept term
        Since:
        2.2

      • newSampleData

        public void newSampleData​(double[] data,
                          int nobs,
                          int nvars)

        Loads model x and y sample data from a flat input array, overriding any previous sample.

        Assumes that rows are concatenated with y values first in each row. For example, an input data array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with nobs = 3 and nvars = 2 creates a regression dataset with two independent variables, as below: 

           y   x[0]  x[1]
   --------------
   1     2     3
   4     5     6
   7     8     9

        Note that there is no need to add an initial unitary column (column of 1's) when specifying a model including an intercept term. If isNoIntercept() is true, the X matrix will be created without an initial column of "1"s; otherwise this column will be added.

        Throws IllegalArgumentException if any of the following preconditions fail:

        • data cannot be null
        • data.length = nobs * (nvars + 1)
        • nobs > nvars

        Parameters:
        data - input data array
        nobs - number of observations (rows)
        nvars - number of independent variables (columns, not counting y)
        Throws:
        java.lang.IllegalArgumentException - if the preconditions are not met

      • estimateResiduals

        public double[] estimateResiduals()
        Estimates the residuals, ie u = y - X*b.
        Specified by:
        estimateResiduals in interface MultipleLinearRegression
        Returns:
        The [n,1] array representing the residuals

      • estimateRegressionParametersVariance

        public double[][] estimateRegressionParametersVariance()
        Estimates the variance of the regression parameters, ie Var(b).
        Specified by:
        estimateRegressionParametersVariance in interface MultipleLinearRegression
        Returns:
        The [k,k] array representing the variance of b

      • estimateRegressandVariance

        public double estimateRegressandVariance()
        Returns the variance of the regressand, ie Var(y).
        Specified by:
        estimateRegressandVariance in interface MultipleLinearRegression
        Returns:
        The double representing the variance of y

      • estimateErrorVariance

        public double estimateErrorVariance()
        Estimates the variance of the error.
        Returns:
        estimate of the error variance
        Since:
        2.2

      • estimateRegressionStandardError

        public double estimateRegressionStandardError()
        Estimates the standard error of the regression.
        Returns:
        regression standard error
        Since:
        2.2