Package edu.cmu.tetrad.regression

Class LogisticRegression

java.lang.Object
edu.cmu.tetrad.regression.LogisticRegression
All Implemented Interfaces:
TetradSerializable, Serializable
public class LogisticRegression extends Object implements TetradSerializable
Implements a logistic regression algorithm based on a Javascript implementation by John Pezzullo. That implementation together with a description of logistic regression and some examples appear on his web page ...

See also Applied Logistic Regression, by D.W. Hosmer and S. Lemeshow. 1989, John Wiley and Sons, New York which Pezzullo references. In particular see pages 27-29.

Version:
$Id: $Id
Author:
Frank Wimberly
See Also:

  • Constructor Details

    • LogisticRegression

      public LogisticRegression(DataSet dataSet)
      A mixed data set. The targets of regresson must be binary. Regressors must be continuous or binary. Other variables don't matter.
      Parameters:
      dataSet - a DataSet object

  • Method Details

    • serializableInstance

      public static LogisticRegression serializableInstance()
      Generates a simple exemplar of this class to test serialization.
      Returns:
      a LogisticRegression object

    • regress

      public LogisticRegression.Result regress(DiscreteVariable x, List<Node> regressors)
      x must be binary; regressors must be continuous or binary.
      Parameters:
      x - a DiscreteVariable object
      regressors - a List object
      Returns:
      a LogisticRegression.Result object

    • getAlpha

      public double getAlpha()

      Getter for the field alpha.

      Returns:
      the alpha level.

    • setAlpha

      public void setAlpha(double alpha)
      Sets the alpha level.
      Parameters:
      alpha - a double

    • setRows

      public void setRows(int[] rows)

      Setter for the field rows.

      Parameters:
      rows - an array of objects