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.