multigroup (version 0.4.5)

DGPA: Dual Generalized Procrustes Analysis

Description

Dual Generalized Procrustes Analysis to study multigroup data

Usage

DGPA(Data, Group, ncomp = NULL, Scale = FALSE, graph = FALSE)

Arguments

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

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 groups

exp.var

Percentages of total variance recovered associated with each dimension

References

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.

See Also

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

Examples

# NOT RUN {
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)) 
# }