Using this data:
I can do a PCA as thus:
plot(USArrests) otherPCA <- princomp(USArrests)
I can get the new components in
and the proportion of variance explained by components with
But what if I want to know which variables are most explained by what axis. i.e. is PCA1 or PCA2 mostly explained by
murder, how can do do this?
Can I say for instance PCA1 is 80% explained by
I think the loadings help me here, but they show the directionality not the variance explained as i understand it, e.g.
otherPCA$loadings Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Murder 0.995 Assault -0.995 UrbanPop -0.977 -0.201 Rape -0.201 0.974