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MULTPERM: a computer package for the analysis of multivariate data using permutation procedures

D E Mercante1, W D Johnson

  • 1Department of Biometry and Genetics, Louisiana State University Medical Center, New Orleans 70112-1393.

Computer Methods and Programs in Biomedicine
|April 1, 1993
PubMed
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MULTPERM offers a nonparametric approach for comparing groups with multiple response variables using exact permutation tests. This user-friendly C program assesses statistical significance by permuting subjects, suitable when traditional method assumptions are unmet.

Area of Science:

  • Biostatistics
  • Computational Biology
  • Data Analysis

Background:

  • Comparing groups with multiple response variables often requires advanced statistical methods.
  • Traditional statistical methods may have assumptions that are not always met in real-world data.
  • Nonparametric methods offer an alternative when distributional assumptions are violated.

Purpose of the Study:

  • To introduce MULTPERM, a C program implementing an exact permutation test for group comparisons.
  • To provide a user-friendly tool for analyzing data with one or more response variables.
  • To assess the statistical significance of group differences using a nonparametric approach.

Main Methods:

  • The program utilizes an exact permutation test.
  • It computes average within-group distances using a generalized distance function.

Related Experiment Videos

  • Statistical significance is determined by comparing observed distances to permuted distributions.

    • Main Results:

    • MULTPERM provides a robust method for group comparison, particularly when traditional assumptions fail.
    • The program facilitates the assessment of significance through permutation of subjects.
    • An approximate test using the beta distribution is included for large sample sizes.

    Conclusions:

    • MULTPERM is a valuable, user-friendly tool for nonparametric group comparisons with multiple response variables.
    • Its permutation test approach is suitable for diverse datasets where traditional methods may be inappropriate.
    • The program's design allows for easy portability across different computing platforms.