ScottKnott: A Package for Performing the Scott-Knott Clustering Algorithm in R
DOI:
https://doi.org/10.5540/tema.2014.015.01.0003Abstract
Scott-Knott is an hierarchical clustering algorithm used in the ANOVA context, when the researcher is comparing treatment means, with a very important characteristic: it does not present any overlapping in its grouping results. We wrote a code, in R, that performs this algorithm starting from vectors, data.frame, aov or aov.list objects. The results are presented with letters representing groups as well as in graphical way using different colors to differentiate among the distinct groups.References
bibitem{type}
C.S. Bernhardson. Type I error rates when multiple comparison procedure
follow a significant F test of ANOVA.
textit{Biometrics}, textbf{31} (1975), 337-340.
bibitem{graph}
T.J. Boardman and D.R. Moffit. Graphical Monte Carlo Type I error rates for
multiple comparison procedures.
textit{Biometrics}, textbf{27} (1971), 738-744.
bibitem{rel}
S. Bony, N. Pichon, C. Ravel, A. Durix, F. Balfourier and J.J. Guillaumin.
The Relationship between Mycotoxin Synthesis and Isolate Morphology in Fungal
Endophytes of Lolium perenne.
textit{New Phytologist}, textbf{152} (2001), 125-137.
bibitem{poder}
L.C. Borges, D.F. Ferreira. Power and type I errors rate of Scott-Knott,
Tukey and Newman-Keuls tests under normal and no-normal distributions of the
residues.
textit{Revista de Matem'atica e Estat'istica}, textbf{21} (2003), 67-83.
bibitem{cluster}
T. Calinski and L.C.A, Corsten. Clustering Means in ANOVA by Simultaneous
Testing.
textit{Biometrics}, textbf{41} (1985), 39-48.
bibitem{detect}
S.G. Carmer and M.R. Swanson. Detection of differences between means:
a Monte Carlo study of five pairwise multiple comparison procedures.
textit{Agronomy Journal}, textbf{63} (1971), 940-945.
bibitem{evalu}
S.G. Carmer and M.R. Swanson. An evaluation of ten pairwise multiple
comparison procedures by Monte Carlo methods.
textit{Journal American Statistical Association}, textbf{68} (1973), 66-74.
bibitem{cox}
D.R. Cox and E. Spjotvoll. On partitioning means into groups.
textit{Scandinavian Journal of Statistics}, textbf{9} (1982), 147-152.
bibitem{aval}
E.C. Da Silva, D.F. Ferreira and E. Bearzoti. Evaluation of power and type I
error rates of Scott-Knott's test by the method of Monte Carlo.
textit{Ci^encias Agrot'ecnicas.}, textbf{23} (1999), 687-696.
bibitem{half}
A.B. Dilson, S.D. David, J. Kazimierz and W.K. William. Half-sib progeny
evaluation and selection of potatoes resistant to the US8 genotype of
Phytophthora infestans from crosses between resistant and susceptible parents.
textit{Euphytica}, textbf{125} (2002), 129-138.
bibitem{illus}
C.E. Gates and J.D. Bilbro. Illustration of a Cluster Analysis Method
for Mean Separation.
textit{Agron J}, textbf{70} (1978), 462-465.
bibitem{jol}
I.T. Jollife. ``Cluster analysis as multiple comparison method'',
Applied Statistics, North Holland, 1975.
bibitem{symb}
S. Jyotsna, L.W. Zettler, J.W. van Sambeek, Ellersieck, C.J. Starbuck.
Symbiotic Seed Germination and Mycorrhizae of Federally Threatened Platanthera
Praeclara(Orchidaceae).
textit{American Midland Naturalist}, textbf{149} (2003), 104-120.
bibitem{onei}
R. O'Neill and G.B. Wetherill. The present state of multiple comparison
methods (with discution).
textit{Journal of the Royal Statistical Society Series B}, textbf{33} (1971), 218-250.
bibitem{exp}
M.A.P. Ramalho, D.F. Ferreira and Oliveira AC.
``Experimentac c~ao em Gen'etica e Melhoramento de Plantas'',
Editora UFLA, Lavras, 2000.
bibitem{r}
R Development Core Team. textit{R: A Language and Environment for
Statistical Computing.} R Foundation for Statistical Computing, Vienna,
Austria, 2010. ISBN 3-900051-07-0, URL:http://www.R-project.org/.
bibitem{sk}
A.J. Scott and M. Knott. A cluster analysis method for grouping means in the
analysis of variance.
textit{Biometrics}, textbf{30} (1974), 507-512.
bibitem{prin}
R.G.D. Steel and J.H. Torrie.
``Principles and procedures of statistics'', McGraw-Hill, New York, 1980.
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