statistics for categorical surveys—a new strategy for multivariate classification and determining variable importance统计数据分类查看一个新战略多元分类和确定变量的重要性.pdf

Sustainability 2010, 2, 533-550; doi:10.3390/su2020533 OPEN ACCESS sustainability ISSN 2071-1050 /journal/sustainability Article Statistics for Categorical Surveys—A New Strategy for Multivariate Classification and Determining Variable Importance Alexander Herr CSIRO Sustainable Ecosystems, Gungahlin Homestead, Bellenden Street, GPO Box 284, Crace, ACT 2601, Canberra, Australia; E-Mail: alexander.herr@csiro.au; Tel.: +61-2-6242-1542; Fax: +61-2-6242-1705. Received: 20 December 2009 / Accepted: 9 February 2010 / Published: 10 February 2010 Abstract : Surveys can be a rich source of information. However, the extraction of underlying variables from the analysis of mixed categoric and numeric survey data is fraught with complications when using grouping techniques such as clustering or ordination. Here I present a new strategy to deal with classification of households into clusters, and identification of cluster membership for new households. The strategy relies on probabilistic methods for identifying variables underlying the clusters. It incorporates existing methods that (i) help determine the optimal cluster number, (ii) directly identify variables underlying clusters, and (iii) identify the variables important for classifying new cases into existing clusters. The strategy uses the R statistical software, which is freely accessible to anyone. Keywords: nominal; cluster; typology; statistics; data analysis; deci