Dual Generalized Procrustes Analysis
Dual Generalized Procrustes Analysis to study multigroup data
DGPA(Data, Group, ncomp = NULL, Scale = FALSE, graph = FALSE)
Data |
a numeric matrix or data frame |
Group |
a vector of factors associated with group structure |
ncomp |
number of components, if NULL number of components is equal to 2 |
Scale |
scaling variables, by defalt 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 groups |
exp.var |
Percentages of total variance recovered associated with each dimension |
J. Gower (1975). Generalized procrustes analysis. Psychometrika, 40(1), 3-51.
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.
@references A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard (2013). Analyses factorielles de donnees structurees en groupes d'individus, Journal de la Societe Francaise de Statistique, 154(3), 44-57.
mgPCA, FCPCA, DCCSWA, DSTATIS, BGC, summarize, TBWvariance, loadingsplot, scoreplot, iris
Data = iris[,-5] Group = iris[,5] res.DGPA = DGPA(Data, Group, graph=TRUE) loadingsplot(res.DGPA, axes=c(1,2)) scoreplot(res.DGPA, axes=c(1,2))
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