asdev {nsprcomp} | R Documentation |
Additional Explained Standard Deviation
Description
asdev
computes the additional standard deviation explained by each
principal component, taking into account the possible non-orthogonality of
the pseudo-rotation matrix \mathbf{W}
.
Usage
asdev(x, w, center = TRUE, scale. = FALSE)
Arguments
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 |
Details
The additional standard deviation of a component is measured after projecting
the corresponding principal axis to the ortho-complement space spanned by the
previous principal axes. This procedure ensures that the variance explained
by non-orthogonal principal axes is not counted multiple times. If the
principal axes are pairwise orthogonal (e.g. computed using standard PCA),
the additional standard deviations are identical to the standard deviations
of the columns of the scores matrix \mathbf{XW}
.
asdev
is also useful to build a partial PCA model from
\mathbf{W}
, to be completed with additional components computed
using nsprcomp
.
Value
asdev
returns a list with class (nsprcomp, prcomp)
containing the following elements:
sdev |
the additional standard deviation explained by each component |
rotation |
copied from the
input argument |
x |
the scores matrix |
center , scale. |
the centering and scaling used |
xp |
the deflated data matrix corresponding to |
q |
an orthonormal basis for the principal subspace |
Note
The PCA terminology is not consistent across the literature. Given a
zero mean data matrix \mathbf{X}
(with observations as rows) and a
basis \mathbf{W}
of the principal subspace, we define the scores
matrix as \mathbf{Z}=\mathbf{XW}
which contains the principal
components as its columns. The columns of the pseudo-rotation matrix
\mathbf{W}
are called the principal axes, and the elements of
\mathbf{W}
are called the loadings.
References
Mackey, L. (2009) Deflation Methods for Sparse PCA. In Advances in Neural Information Processing Systems (pp. 1017–1024).