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PC‐SAS Program for Estimating Hühn's Nonparametric Stability Statistics
Author(s) -
Lu Hsui Ying
Publication year - 1995
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj1995.00021962008700050018x
Subject(s) - nonparametric statistics , stability (learning theory) , statistics , parametric statistics , rank (graph theory) , mathematics , table (database) , statistical hypothesis testing , econometrics , computer science , data mining , machine learning , combinatorics
A program written in the SAS language for personal computers to estimate Hühn's two nonparametric stability statistics is presented. Nonparametric methods proposed by Hühn in the 1970s are based on the ranks of genotypes in each environment and use the idea of homeostasis as a measure of stability. A stable genotype shows similar rankings across environments. Nonparametric stability statistics provide a viable alternative to existing parametric measures based on absolute data. They require no statistical assumptions about the distribution of the phenotypic values and are easy to use. Addition or deletion of one or a few observations is not as likely to cause great variation in the estimates as would be the case for parametric stability measures. For many applications (e.g., selection in breeding and testing programs), the rank orders of genotypes are the most essential information. The program deals with a two‐way table with K genotypes and N environments. The output contains two main features: (i) corrected values and their ranks of genotypes within each environment, and (ii) two nonparametric stability estimates and their test of significance. From the output, one may examine the rankings of genotypes in each environment and look for stability differences among genotypes. This program should make practical the more frequent use of nonparametric stability statistics to investigate genotype ✕ environment interactions in agricultural research.