Thomas M. Parris writes:
> clusplot reports that the first two principal components explain
> 99.7% of the variability.
[...]
>> loadings(pca)
[...]
> Comp.1 Comp.2 Comp.3 Comp.4
> SS loadings 1.00 1.00 1.00 1.00
> Proportion Var 0.25 0.25 0.25 0.25
> Cumulative Var 0.25 0.50 0.75 1.00
This has nothing to do with how much of the variability of the
original data that is captured by each component; it merely measures
the variability in the coefficients of the loading vectors (and they
are standardised to length one in princomp)
What you want to look at is pca$sdev, for instance something like
totvar <- sum(pca$sdev^2)
rbind("explained var" = pca$sdev^2,
"prop. expl. var" = pca$sdev^2/totvar,
"cum.prop.expl.var" = cumsum(pca$sdev^2)/totvar)
Comp.1 Comp.2 Comp.3 Comp.4
explained var 3.4093746 0.5785399 0.011560142 0.0005252824
prop. expl. var 0.8523437 0.1446350 0.002890036 0.0001313206
cum.prop.expl.var 0.8523437 0.9969786 0.999868679 1.0000000000
And as you can see, two comps "explain" 99.7%. :-)
--
Bjørn-Helge Mevik
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