Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-06-01T07:52:54.719Z Has data issue: false hasContentIssue false

Comparison of Variety Means Using Cluster Analysis and Dendrograms

Published online by Cambridge University Press:  03 October 2008

I. T. Jolliffe
Affiliation:
Mathematical Institute, University of Kent, Canterbury, Kent, CT2 7NF, England
O. B. Allen
Affiliation:
Departments of Animal and Poultry Science and Mathematics and Statistics, University of Guelph, Guelph, Ontario, NIG 2W1, Canada
B. R. Christie
Affiliation:
Crop Science Department, University of Guelph, Guelph, Ontario, NIG 2W1, Canada

Summary

In many experiments which compare a large number of different treatments or varieties, it is necessary to decide which sub-sets of treatments or varieties do not differ significantly from each other. Multiple comparison procedures provide a commonly used, but much criticized, way of tackling this problem. As an alternative, techniques known collectively as cluster analysis can be used. An advantage of using certain types of cluster analysis is that their results can be displayed as a dendrogram or tree diagram which gives a very useful picture of the overall relationship between treatments or varieties. These procedures can also be extended readily to multivariate observations from each plot.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Carmer, S. G. (1976). Optimal significance levels for application of the least significant difference in crop performance trials. Crop Science 16:9599.CrossRefGoogle Scholar
Carmer, S. G. & Walker, W. M. (1982). Baby bear's dilemma: a statistical tale. Agronomy Journal 74:122124.CrossRefGoogle Scholar
Calinski, T. & Corsten, L. C. A. (1985). Clustering means in ANOVA by simultaneous testing. Biometrics 41:3948.CrossRefGoogle Scholar
Dixon, W. J., Brown, M. B., Engleman, L., Frane, J. W., Hill, M. A., Jennrich, R. I. & Toporek, J. D. (1981). BMDP Statistical Software. Los Angeles: University of California Press.Google Scholar
Everitt, B. (1980). Cluster Analysis. 2nd edition. London: Heinemann.Google Scholar
Faris, M. A. (1981). Ontario forage crop investigation: 1981 report on field trials of varieties and mixtures. Ottawa, Canada: Agriculture Canada.Google Scholar
Fowlkes, E. B. & Mallows, C. L. (1983). A method for comparing two hierarchical clusterings. Journal of the American Statistical Association 78:553584 (including discussion).CrossRefGoogle Scholar
Gates, C. E. & Bilbro, J. D. (1978). Illustration of a cluster analysis method for mean separation. Agronomy Journal 70:462465.CrossRefGoogle Scholar
Johnson, S. B. & Berger, R. D. (1982). On the status of statistics in Phytopathology. Phytopathology 72:10141015.CrossRefGoogle Scholar
Jolliffe, I. T. (1986). Graphical representation of multiple comparisons of means using dendrograms. In Data Analysts and Informatics IV. Amsterdam: North Holland.Google Scholar
Keuls, M. (1952). The use of the ‘studentised range’ in connection with an analysis of variance. Euphytica 1:112122.CrossRefGoogle Scholar
Lin, C. S., Binns, M. R. & Lefkovitch, L. P. (1986). Stability analysis: where do we stand? Crop Science 26:894900.CrossRefGoogle Scholar
Murphy, J. P., Cox, T. S. & Rodgers, D. M. (1986). Cluster analysis of red winter wheat cultivars based upon coefficients of parentage. Crop Science 26:672676.CrossRefGoogle Scholar
Scott, A. J. & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics 30:507512.CrossRefGoogle Scholar
Willavize, S. A., Carmer, S. G. & Walker, W. M. (1980). Evaluation of cluster analysis for comparing treatment means. Agronomy Journal 72:317320.CrossRefGoogle Scholar
Wishart, D. (1978). CLUSTAN User Manual. Edinburgh University.Google Scholar