Description Usage Arguments Details Value References
acor
computes the additional standard correlation explained by each
canonical variable, taking into account the possible nonconjugacy of the
canonical vectors. The result of the analysis is returned as a list of class
nscancor
.
1 2 
x 
a numeric matrix which provides the data from the first domain 
xcoef 
a numeric data matrix with the canonical vectors related to

y 
a numeric matrix which provides the data from the second domain 
ycoef 
a numeric data matrix with the canonical vectors related to

xcenter 
a logical value indicating whether the empirical mean of (each
column of) 
ycenter 
analogous to 
xscale 
a logical value indicating whether the columns of 
yscale 
analogous to 
The additional correlation is measured after projecting the corresponding canonical vectors to the orthocomplement space spanned by the previous canonical variables. This procedure ensures that the correlation explained by nonconjugate canonical vectors is not counted multiple times. See Mackey (2009) for a presentation of generalized deflation in the context of principal component analysis (PCA), which was adapted here to CCA.
acor
is also useful to build a partial CCA model, to be completed with
additional canonical variables computed using nscancor
.
acor
returns a list of class nscancor
containing the
following elements:
cor 
the additional correlation explained by each pair of canonical variables 
xcoef 
copied from the input arguments 
ycoef, ycenter, yscale 
copied from the input arguments 
xp 
the deflated data matrix corresponding to 
yp 
anologous to 
Mackey, L. (2009) Deflation Methods for Sparse PCA. In Advances in Neural Information Processing Systems (pp. 1017–1024).
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