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 \(\mathbf{W}\).
peav(x, w, center = TRUE, scale. = FALSE)a numeric data matrix with the observations as rows
a numeric data matrix with the principal axes as columns
a logical value indicating whether the empirical mean of
x should be subtracted. Alternatively, a vector of length equal to
the number of columns of x can be supplied. The value is passed to
scale.
a logical value indicating whether the columns of x
should be scaled to have unit variance before the analysis takes place. The
default is FALSE for consistency with prcomp. Alternatively,
a vector of length equal to the number of columns of x can be
supplied. The value is passed to scale.
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 \(\mathbf{W}\) 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.