Description Usage Arguments Details Note See Also
peav
computes the percentage of the explained _additional_ variance of each
principal component, taking into account the possible non-orthogonality of
the pseudo-rotation matrix W.
1 |
x |
a numeric data matrix with the observations as rows |
w |
a numeric data matrix with the principal axes as columns |
center |
a logical value indicating whether the empirical mean of
|
scale. |
a logical value indicating whether the columns of |
The explained additional variance is computed using asdev
and
divided by the total variance of the data to obtain percentages.
sum(peav(x, w))
is equal to one if W is an orthonormal
basis, e.g. the rotation matrix of a standard PCA.
peav
is useful to compare the solutions of various constrained PCA
methods w.r.t. standard PCA.
The method produces different results than the "percentage explained
variance" (pev
) computed by the spca
function from the
elasticnet
package.
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