Variable selection in large environmental data sets using principal components analysis

In many large environmental datasets redundant variables can be discarded without the loss of extra variation. Principal components analysis can be used to select those variables that contain the most information. Using an environmental dataset consisting of 36 meteorological variables spanning 37 years, four methods of variable selection are examined along with different criteria levels for deciding on the number of variables to retain. Procrustes analysis, a measure of similarity and bivariate plots are used to assess the success of the alternative variable selection methods and criteria levels in extracting representative variables. The Broken-stick model is a consistent approach to choosing significant principal components and is chosen here as the more suitable criterion in combination with a selection method that requires one principal component analysis and retains variables by starting with selection from the first component.