ScottKnott-package: The ScottKnott Clustering Algorithm

ScottKnott-packageR Documentation

The ScottKnott Clustering Algorithm


The Scott & Knott clustering algorithm is a very useful clustering algorithm widely used as a multiple comparison method in the Analysis of Variance context, as for example Gates and Bilbro (1978), Bony et al. (2001), Dilson et al. (2002) and Jyotsna et al. (2003).

It was developed by Scott, A.J. and Knott, M. (Scott and Knott, 1974). All methods used up to that date as, for example, the t-test, Tukey, Duncan, Newman-Keuls procedures, have overlapping problems. By overlapping we mean the possibility of one or more treatments to be classified in more than one group, in fact, as the number of treatments reach a number of twenty or more, the number of overlappings could increse as reaching 5 or greater what makes almost impossible to the experimenter to really distinguish the real groups to which the means should belong. The Scott & Knott method does not have this problem, what is often cited as a very good quality of this procedure.

The Scott & Knott method make use of a clever algorithm of cluster analysis, where, starting from the the whole group of observed mean effects, it divides, and keep dividing the sub-groups in such a way that the intersection of any two groups formed in that manner is empty.

Using their own words 'we study the consequences of using a well-known method of cluster analysis to partition the sample treatment means in a balanced design and show how a corresponding likelihood ratio test gives a method of judging the significance of difference among groups abtained'.

Many studies, using the method of Monte Carlo, suggest that the Scott Knott method performs very well compared to other methods due to fact that it has high power and type I error rate almost always in accordance with the nominal levels. The ScottKnott package performs this algorithm starting either from vectors, matrices or data.frames joined as default, a aov, aovlist, lm and lmer resulting object of previous analysis of variance. The results are given in the usual way as well as in graphical way using thermometers with diferent group colors.

In a few words, the test of Scott & Knott is a clustering algorithm used as an one of the alternatives where multiple comparizon procedures are applied with a very important and almost unique characteristic: it does not present overlapping in the results.

As of version 1.2-8, the ScottKnott package is able to analyze unbalanced data based on the article ‘Adjusting the Scott-Knott cluster analyzes for unbalanced designs’ by Conrado et al.


Enio Jelihovschi (
Jose Claudio Faria (
Ivan Bezerra Allaman (


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Conrado, T. V., Ferreira, D. F., Scapim, C. A., and Maluf, W. R. "Adjusting the Scott-Knott cluster analyses for unbalanced designs." Crop Breeding and Applied Biotechnology 17.1 (2017): 1-9.

ScottKnott documentation built on Aug. 31, 2023, 1:06 a.m.