Machine Learning with R Cookbook - Second Edition

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Another principal component analysis function is princomp. In this function, the calculation is performed by using eigen on a correlation or covariance matrix instead of a single value decomposition used in the prcomp function. In general practice, using prcomp is preferable; however, we cover how to use princomp here:

1. First, use princomp to perform PCA:

    > swiss.princomp = princomp(swiss,
    + center = TRUE,
    + scale  = TRUE)
    >swiss.princomp
    Output
    Call:
    princomp(x = swiss, center = TRUE, scale = TRUE)
    
    Standard deviations:
       Comp.1    Comp.2    Comp.3    Comp.4    Comp.5 
    42.896335 21.201887  7.587978  3.687888  2.721105 
    
     5 variables and 47 observations. 

2. You can then obtain the summary information:

 > summary(swiss.princomp) Output Importance ...