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Patterns of ordination and classification instability resulting from changes in input data order
Author(s) -
Tausch R.J.,
Charlet D.A.,
Weixelman D.A.,
Zamudio D.C.
Publication year - 1995
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.2307/3236404
Subject(s) - ordination , detrended correspondence analysis , data set , cluster (spacecraft) , set (abstract data type) , mathematics , similarity (geometry) , variable (mathematics) , statistics , reciprocal , principal component analysis , data mining , computer science , artificial intelligence , mathematical analysis , linguistics , philosophy , image (mathematics) , programming language
. Random rearrangement of entry order in three data sets often changed ordination and classification results based on Reciprocal Averaging. Results varied with the data set and method used. Eliminating infrequently occurring species largely reduced, but did not always eliminate, the variability. Overall, results appeared related to data set complexity, the type of data or transformation, and the analysis method used. Detrended Correspondence Analysis had the greatest variability of the ordination methods tested. Results from quantitative data were usually more variable than presence/absence data. Variation in cluster analysis was related to the number of tie values in the similarity matrix. Detailed tests using randomization of entry order of individual data sets with each of the programs to be used are needed to individually assess the effects on the results.; Keywords :; Cluster analysis; DECORANA; Ecological group; Entry order; Environmental gradient; TWINSPAN