Variable selection in large environmental data sets using principal components analysis

Environmetrics

Volumn 10, Issue 1, 1999, Pages 67-77

  King, Jacquelynne R ^a   Jackson, Donald A ^b  

^a PACIFIC BIOLOGICAL STATION   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Discarding variables; Environmental data; Limnology; Principal components analysis; Procrustes analysis; Variable selection

Indexed keywords

ENVIRONMENTAL RESEARCH; PRINCIPAL COMPONENT ANALYSIS;

PROCRUSTES;

EID: 0033036852     PISSN: 11804009     EISSN: None     Source Type: Journal    
DOI: 10.1002/(sici)1099-095x(199901/02)10:1<67::aid-env336>3.0.co;2-0     Document Type: Article

References (12)

1. Frontier, S. (1976). Étude de la decroissance des valeurs propres dans une analyze en composantes principales: comparison avec le modèle de baton brisé. J. Exp. Mar. Bio. Ecol. 25, 67-75.
2. Gibson, A. R., Baker, A. J. and Moeed, A. (1984). Morphometric variation in introduced populations of the common myna (Acridotheres tristis): an application of the jackknife to principal component analysis. Syst. Zool. 33, 408-421.
3. Grossman, G. D., Nickerson, D. M. and Freeman, M. C. (1991). Principal component analyses of assemblage structure data: utility of tests based on eigenvalues. Ecology 72(1), 341-347.
4. Jackson, D. A. (1993). Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology 74, 2201-2214.
5. Jolliffe, I. T. (1972). Discarding variables in a principal component analysis I: artificial data. Appl. Statist. 21, 160-173.
6. King, J. R., Shuter, B. J. and Zimmerman, A. P. (1997). The response of the thermal stratification of South Bay (Lake Huron) to climatic variability. Can. J. Fish. Aquat. Sci. 54, 1873-1882.
7. Krzanowski, W. J. (1987). Selection of variables to preserve multivariate data structure, using principal components. Appl. Statist. 36, 22-33.
8. Legendre, L. and Legendre, P. (1983). Numerical Ecology. Elsevier: Amsterdam.
9. McCabe, G. P. (1984). Principal variables. Technometrics 26, 137-144.
10. SAS (1989). SAS/STAT Guide, version 6·08. SAS Institute, Cary, NC.
11. Slice, D. E. (1994). Generalized Rotational Fitting of n-Dimensional Landmark Data. Revision 11-01-94. State University of New York at Stony-Brook, Stony Brook, NY.
12. Williams, B. K. and Titus, K. (1988). Assessment of sampling stability in ecological applications of discriminant analysis. Ecology 69(4), 1275-1285.