# DCCSWA: Dual Common Component and Specific Weights Analysis In multigroup: Multigroup Data Analysis

## Description

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

## Usage

 `1` ```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.

`mgPCA`, `FCPCA`, `BGC`, `DSTATIS`, `DGPA`, `summarize`, `TBWvariance`, `loadingsplot`, `scoreplot`, `iris`
 ```1 2 3 4 5``` ```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)) ```