Hello, I would like to reinforce my anova results using PCA i.e. which factor are most important because they explain most of the variance (i.e. signal) of my 2^k*r experiment. However, I get the following error while trying to run PCA:
> throughput.prcomp <- > prcomp(~No_databases+Partitioning+No_middlewares+Queue_size,data=throughput) Error in prcomp.formula(~No_databases + Partitioning + No_middlewares + : PCA applies only to numerical variables What is the most R-like concise way to map/transform those factor values into numerical values in a suitable way for PCA analysis? My first attempt would be: # C++ "style" throughput$No_databases_num <- (throughput$No_databases == 1) ? -1 : 1 throughput$Partitioning_num <- (throughput$Partitioning == "sharding") ? -1 : 1 etc. How can I do this in the R way? Would these -1, 1 be sensible for a PCA analysis or it just doesn't matter? How about a factor for which I have 3 levels? -1, 0 and 1? Many thanks in advance, Best regards, Giovanni ______________________________________________ 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.
