Package dev.nm.stat.regression.linear

Class LMProblem

java.lang.Object
dev.nm.stat.regression.linear.LMProblem
Direct Known Subclasses:
ConstrainedLASSOProblem, GLMProblem, LARSProblem, LogisticProblem, UnconstrainedLASSOProblem
public class LMProblem extends Object
This is a linear regression or a linear model (LM) problem. Linear regression models the relationship between a scalar variable y and one or more variables denoted as X. In linear regression, models of the unknown parameters are estimated from the data using linear functions. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of y is an affine function of y.
See Also:

  • Constructor Details

    • LMProblem

      public LMProblem(Vector y, Matrix X, boolean intercept, Vector weights)
      Constructs a linear regression problem.
      Parameters:
      y - the dependent variables
      X - the factors
      intercept - true if to additionally add an intercept term to the linear regression
      weights - the weights assigned to each observation

    • LMProblem

      public LMProblem(Vector y, Matrix X, Vector weights)
      Constructs a linear regression problem, assuming a constant term (the intercept).
      Parameters:
      y - the dependent variables
      X - the factors
      weights - the weights assigned to each observation

    • LMProblem

      public LMProblem(Vector y, Matrix X, boolean intercept)
      Constructs a linear regression problem, assuming equal weights to all observations.
      Parameters:
      y - the dependent variables
      X - the factors
      intercept - true if to additionally add an intercept term to the linear regression

    • LMProblem

      public LMProblem(Vector y, Matrix X)
      Constructs a linear regression problem, assuming
      • a constant term (the intercept)
      • equal weights assigned to all observations
      Parameters:
      y - the dependent variables
      X - the factors

    • LMProblem

      public LMProblem(LMProblem that)
      Copy constructor.
      Parameters:
      that - another LMProblem

  • Method Details

    • nObs

      public int nObs()
      Gets the number of observations.
      Returns:
      the number of observations

    • nFactors

      public int nFactors()
      Gets the number of factors, including the intercept if any.
      Returns:
      the number of factors, including the intercept if any

    • y

      public ImmutableVector y()
      Gets the response vector, the regressands, the dependent variables.
      Returns:
      the response vector

    • wy

      public ImmutableVector wy()
      Gets the weighted response vector.
      Returns:
      the weighted response vector

    • A

      public ImmutableMatrix A()
      Gets the regressor matrix.
      Returns:
      the regressor matrix

    • wA

      public ImmutableMatrix wA()
      Gets the weighted regressor matrix.
      Returns:
      the weighted regressor matrix

    • intercept

      public boolean intercept()
      Checks if an intercept term is added to the linear regression.
      Returns:
      true if an intercept term is added

    • weights

      public ImmutableVector weights()
      Gets the weights assigned to each observation.
      Returns:
      the weights assigned to each observation

    • nExogenousFactors

      public int nExogenousFactors()
      Gets the number of factors, excluding the intercept.
      Returns:
      the number of factors, excluding the intercept

    • X

      public ImmutableMatrix X()
      Gets the factor matrix.

      Note: the automatically appended intercept is not included.

      Returns:
      the factor matrix.

    • invOfwAtwA

      public ImmutableMatrix invOfwAtwA()
      (wA' * wA)-1
      Returns:
      (wA' * wA)-1

    • checkInputs

      protected void checkInputs()
      Checks whether this LMProblem instance is valid.
      Throws:
      IllegalArgumentException - if this problem is invalid