This is a wrapper for the RGCCA::rgcca
function for computing MCOA.
mcoa(X, ncomp = 2, scale = FALSE, verbose = FALSE, ...)
list
of input blocks.
integer
number of components to extract.
logical
indicating if variables should be scaled.
logical
indicating if diagnostic information should be printed.
additional arguments for RGCCA.
multiblock
object including relevant scores and loadings. Relevant plotting functions: multiblock_plots
and result functions: multiblock_results
.
MCOA resembles GCA and MFA in that it creates a set of reference scores, for which each block's individual scores should correlate maximally too, but also the variance within each block should be taken into account. A single component solution is equivalent to a PCA on concatenated blocks scaled by the so called inverse inertia.
Le Roux; B. and H. Rouanet (2004). Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis. Dordrecht. Kluwer: p.180.
Greenacre, Michael and Blasius, Jörg (editors) (2006). Multiple Correspondence Analysis and Related Methods. London: Chapman & Hall/CRC.
Overviews of available methods, multiblock
, and methods organised by main structure: basic
, unsupervised
, asca
, supervised
and complex
.
Common functions for computation and extraction of results and plotting are found in multiblock_results
and multiblock_plots
, respectively.