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klaR Analyzing German Business Cycles

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Data Analysis and Decision Support

Abstract

Decision making often asks for classification. We will present a new R package klaR including functions to build, check, tune, visualize, and compare classification rules. The software is illustrated by means of a case study of prediction of the German economy’s business cycle phases.

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References

  • DIMITRIADOU, E., HORNIK, K., LEISCH, F, MEYER, D., and WEINGESSEL, A. (2005): el071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package version 1.5-6.

    Google Scholar 

  • FRIEDMAN, J.H. (1989): Regularized Discriminant Analysis. Journal of the American Statistical Association, 84, 165–175.

    Article  MathSciNet  Google Scholar 

  • GARCZAREK, U. and WEIHS, C. (2003): Standardizing the Comparison of Partitions. Computational Statistics, 18, 143–162.

    MathSciNet  Google Scholar 

  • HEILEMANN, U. and MÜNCH, J.M. (1996): West german business cycles 1963–1994: A multivariate discriminant analysis. CIRET-Conference in Singapore, CIRET-Studien 50.

    Google Scholar 

  • JOACHIMS, T. (2004): SVMlight. http://svmlight.joachims.org/

    Google Scholar 

  • R DEVELOPMENT CORE TEAM (2004): R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

    Google Scholar 

  • RAABE, N., LUEBKE, K., and WEIHS, C. (2004): KMC/EDAM: A new approach for the visualization of K-Means Clustering results. Technical Report 65/2004, SFB 475, Universität Dortmund.

    Google Scholar 

  • VENABLES, W.N. and RIPLEY, B.D. (2002): Modern Applied Statistics with S, 4th ed. Springer, New York.

    Google Scholar 

  • WEIHS, C. and GARCZAREK, U. (2002): Stability of multivariate representation of business cycles over time. Technical Report 20/2002, SFB 475, Universität Dortmund.

    Google Scholar 

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© 2005 Springer-Verlag Berlin · Heidelberg

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Weihs, C., Ligges, U., Luebke, K., Raabe, N. (2005). klaR Analyzing German Business Cycles. In: Baier, D., Decker, R., Schmidt-Thieme, L. (eds) Data Analysis and Decision Support. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28397-8_36

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