multigroup (version 0.4.5)

DCCSWA: Dual Common Component and Specific Weights Analysis

Description

Dual Common Component and Specific Weights Analysis: to find common structure among variables of different groups

Usage

DCCSWA(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

saliences

Each group having a specific contribution to the determination of this common space, namely the salience, for each dimension under study

lambda

The specific variances of groups

exp.var

Percentages of total variance recovered associated with each dimension

References

E. M. Qannari, P. Courcoux, and E. Vigneau (2001). Common components and specific weights analysis performed on preference data. Food Quality and Preference, 12(5-7), 365-368.

A. Eslami (2013). Multivariate data analysis of multi-group datasets: application to biology. University of Rennes I.

See Also

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

Examples

# NOT RUN {
Data = iris[,-5]
Group = iris[,5]
res.DCCSWA = DCCSWA(Data, Group, graph=TRUE)
loadingsplot(res.DCCSWA, axes=c(1,2))
scoreplot(res.DCCSWA, axes=c(1,2)) 
# }