Flury's Common Principal Component Analysis
Common principal component Analysis
FCPCA(Data, Group, Scale = FALSE, graph = FALSE)
Data |
a numeric matrix or data frame |
Group |
a vector of factors associated with group structure |
Scale |
scaling variables, by default is False. By default data are centered within groups. |
graph |
should loading and component be plotted |
list with the following results:
Data |
Original data |
Con.Data |
Concatenated centered data |
split.Data |
Group centered data |
Group |
Group as a factor vector |
loadings.common |
Matrix of common loadings |
lambda |
The specific variances of group |
exp.var |
Percentages of total variance recovered associated with each dimension |
B. N. Flury (1984). Common principal components in k groups. Journal of the American Statistical Association, 79, 892-898.
A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard (2013). General overview of methods of analysis of multi-group datasets, Revue des Nouvelles Technologies de l'Information, 25, 108-123.
mgPCA, DGPA, DCCSWA, DSTATIS, BGC, summarize, TBWvariance, loadingsplot, scoreplot, iris
Data = iris[,-5] Group = iris[,5] res.FCPCA = FCPCA(Data, Group, graph=TRUE) loadingsplot(res.FCPCA, axes=c(1,2)) scoreplot(res.FCPCA, axes=c(1,2))
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