Package com.imsl.stat

Class MultipleComparisons

java.lang.Object
com.imsl.stat.MultipleComparisons
All Implemented Interfaces:
Serializable, Cloneable
public class MultipleComparisons extends Object implements Serializable, Cloneable
Performs Student-Newman-Keuls multiple comparisons test.

Class MultipleComparisons performs a multiple comparison analysis of means using the Student-Newman-Keuls method. The null hypothesis is equality of all possible ordered subsets of a set of means. This null hypothesis is tested using the Studentized range of each of the corresponding subsets of sample means. The method is discussed in many elementary statistics texts, e.g., Kirk (1982, pp. 123-125).

See Also:

  • Constructor Details

    • MultipleComparisons

      public MultipleComparisons(double[] means, int df, double stdError)
      Constructor for MultipleComparisons.
      Parameters:
      means - A double array containing the means.
      df - An int scalar containing the degrees of freedom associated with stdError.
      stdError - A double scalar containing the effective estimated standard error of a mean. In fixed effects models, stdError equals the estimated standard error of a mean. For example, in a one-way model \({\rm {stdError}} = \sqrt {s^2 / n}\) where \(s^2\) is the estimate of \(\sigma^2\) and n is the number of responses in a sample mean. In models with random components, use \({\rm {stdError}} = {\rm {sedif}} / \sqrt {2}\) where sedif is the estimated standard error of the difference of two means.

  • Method Details

    • compute

      public final int[] compute()
      Performs Student-Newman-Keuls multiple comparisons test.
      Returns:
      An int array , call it equalMeans indicating the size of the groups of means declared to be equal. Value equalMeans[I] = J indicates the I-th smallest mean and the next J-1 larger means are declared equal. Value equalMeans[I] = 0 indicates no group of means starts with the I-th smallest mean.

    • setAlpha

      public void setAlpha(double alpha)
      Sets the significance level of the test
      Parameters:
      alpha - A double scalar containing the significance level of test. alpha must be in the interval [0.01, 0.10]. Default: alpha = 0.01