optVardim | R Documentation |
This function calculates the variance explained by not necessarily orthogonal principal components, using the optimal projected variance (optVar).
optVardim(B, Z)
B |
a numerical data matrix (usually centered and/or scaled, see details). |
Z |
a numerical loading matrix. |
The principal components are defined by
Y=BZ where B is the centered and/or scaled data matrix and Z is the sparse
loading matrix. The argument B
must then be consistent with the
pre-processing step in sparsePCA
and groupsparsePCA
.
The loading vectors in Z
must be unique norm and linearly independant.
Returns the optimal projected variance of each principal components.
M. Chavent and G. Chavent, Optimal projected variance group-sparse block PCA, submitted, 2020.
pev
, explainedVar
,
sparsePCA
, groupsparsePCA
data(protein) B <- sparsePCA(protein, 2, c(0.5,0.5))$B Z <- sparsePCA(protein, 2, c(0.5,0.5))$Z optVardim(B,Z)
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