Skip to main content
SpringerLink
Log in
Book cover

Data Analysis and Decision Support pp 335–343Cite as

klaR Analyzing German Business Cycles

  • Chapter

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.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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 

Download references

Author information

Authors and Affiliations

  1. Fachbereich Statistik, Universität Dortmund, D-44221, Dortmund, Germany

    Claus Weihs, Uwe Ligges, Karsten Luebke & Nils Raabe

Authors
  1. Claus Weihs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uwe Ligges
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karsten Luebke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nils Raabe
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Konrad-Wachsmann-Allee 1, 03046, Cottbus, Germany

    Daniel Baier (Chair of Marketing and Innovation Management) (Chair of Marketing and Innovation Management)

  2. Department of Business Administration and Economics, Bielefeld University, Universitätsstr. 25, 33615, Bielefeld, Germany

    Reinhold Decker (Chair of Marketing) (Chair of Marketing)

  3. Computer Based New Media Group (CGNM), Institute for Computer Science, University of Freiburg, Georges-Köhler-Allee 51, 79110, Freiburg, Germany

    Lars Schmidt-Thieme

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin · Heidelberg

About this chapter

Cite this chapter

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

Download citation

Publish with us

Policies and ethics

Search

Navigation