# FCPCA: Flury's Common Principal Component Analysis In multigroup: Multigroup Data Analysis

## Description

Common principal component Analysis

## Usage

 `1` ```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.

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

## Examples

 ```1 2 3 4 5``` ```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)) ```

### Example output

```
```

multigroup documentation built on March 26, 2020, 5:50 p.m.