Hello to everyone, I am starting to work on classification procedures. I usualy do a principal component analysis (PCA) as a previous step in order to reduce variables and after I apply a cluster procedure. My question is if it will be better to use raw variables instead of use principal components obtained from these variables since the original variables keep all the variability.
Now i am thinking to use a variable group analysis (VGA) and a correlation analysis together in order to identify which of my original variables could explain differences on my data better, and after apply a cluster analysis on selected variables. What do you think about it? What would be better: work with PCA or with raw variables. Thanks in advance. Manuel ----- Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://www.nabble.com/Principal-components-vs.-raw-variables-tp22824280p22824280.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
