multigroup (version 0.4.5)

FCPCA: Flury's Common Principal Component Analysis

Description

Common principal component Analysis

Usage

FCPCA(Data, Group, Scale = FALSE, graph = FALSE)

Arguments

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

Value

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

References

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.

See Also

mgPCA, DGPA, DCCSWA, DSTATIS, BGC, summarize, TBWvariance, loadingsplot, scoreplot, iris

Examples

# NOT RUN {
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)) 
# }