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Semi-Partial Canonical Correlation Biplot

  • Lee, Bo-Hui (Department of Statistics, Pusan National University) ;
  • Choi, Yong-Seok (Department of Statistics, Pusan National University) ;
  • Shin, Sang-Min (Department of Statistics, Pusan National University)
  • Received : 2012.01.25
  • Accepted : 2012.04.13
  • Published : 2012.06.30

Abstract

Simple canonical correlation biplot is a graphical method to investigate two sets of variables and observations in simple canonical correlation analysis. If we consider the set of covariate variables that linearly affects two sets of variables, we can apply the partial canonical correlation biplot in partial canonical correlation analysis that removes the linear effect of the set of covariate variables on two sets of variables. On the other hand, we consider the set of covariate variables that linearly affect one set of variables but not the other. In this case, if we apply the simple or partial canonical correlation biplot, we cannot clearly interpret other two sets of variables. Therefore, in this study, we will apply the semi-partial canonical correlation analysis of Timm (2002) and remove the linear effect of the set of covariate variables on one set of variables but not the other. And we suggest the semi-partial canonical correlation biplot for interpreting the semi-partial canonical correlation analysis. In addition, we will compare shapes and shape the variabilities of the simple, partial and semi-partial canonical correlation biplots using a procrustes analysis.

Keywords

References

  1. Choi, T.-H. and Choi, Y.-S. (2008). A study on the relationship between skill and competition score factors of KLPGA players using canonical correlation biplot and cluster analysis, The Korean Journal of Applied Statistics, 21, 429-439. https://doi.org/10.5351/KJAS.2008.21.3.429
  2. Choi, T.-H. and Choi, Y.-S. (2010). A study on the relationship between physique, physical fitness and basic skill factors of tennis players in the Korea Tennis Association using the generalized canonical correlation biplot and procrustes analysis, Communications of the Korean Statistical Society, 17, 917-925. https://doi.org/10.5351/CKSS.2010.17.6.917
  3. Choi, T.-H., Choi, Y.-S. and Shin, S. M. (2009). A study on the relationship between player characteristic factors and competitive factors of tennis grand slams competition using canonical correlation biplot and procrustes analysis, The Korean Journal of Applied Statistics, 22, 855-864. https://doi.org/10.5351/KJAS.2009.22.4.855
  4. Choi, Y. S. (2006). Biplot Analysis, Research Institute for Basic Sciences, Series 2 of Basic Sciences, Pusan National University Press.
  5. Choi, Y. S. and Hyun, G. H. (2006).Understanding and Application of Statistical Shape Analysis - Study and Development of Resistant Version of Procrustes Analysis-, Free Academy, Seoul.
  6. Choi, Y. S., Hyun, G. H. and Yun, W. J. (2005). Biplots' variability based on the Procrustes analysis, Journal of the Korean Data Analysis Society, 7, 1925-1933.
  7. Gabriel, K. R. (1971). The biplot graphics display of matrices with applications to principal component analysis, Biometrika, 58, 453-467. https://doi.org/10.1093/biomet/58.3.453
  8. Gower, J. C. and Dijksterhuis, G. B. (2004). Procrustes Problems, Oxford: University Press.
  9. Park, M. and Huh, M. H. (1996a). Canonical correlation biplot, The Korea Communications in Statistics, 3, 11-19.
  10. Park, M. and Huh, M. H. (1996b). Quantification plots for several sets of variables, Journal of the Korea Statistical Society, 25, 599-601.
  11. Timm, N. H. (2002). Applied Multivariate Analysis, Springer, New York.
  12. Yeom, A.-R. and Choi, Y.-S. (2011). Partial canonical correlation biplot, The Korean Journal of Applied Statistics, 24, 559-566. https://doi.org/10.5351/KJAS.2011.24.3.559