# mgPLS: Multigroup Partial Least Squares Regression In multigroup: Multigroup Data Analysis

## Description

Multigroup PLS regression

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```mgPLS( DataX, DataY, Group, ncomp = NULL, Scale = FALSE, Gcenter = FALSE, Gscale = FALSE ) ```

## Arguments

 `DataX` a numeric matrix or data frame associated with independent dataset `DataY` a numeric matrix or data frame associated with dependent dataset `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 `Gcenter` global variables centering, by defalt is FALSE. `Gscale` global variables scaling, by defalt is FALSE.

## Value

list with the following results:

 `DataXm` Group X data `DataYm` Group Y data `Concat.X` Concatenated X data `Concat.Y` Concatenated Y data `coefficients` Coefficients associated with X data `coefficients.Y` Coefficients associated with regressing Y on Global components X `Components.Global` Conctenated Components for X and Y `Components.Group` Components associated with groups in X and Y `loadings.common` Common vector of loadings for X and Y `loadings.Group` Group vector of loadings for X and Y `expvar` Explained variance associated with global components X `cum.expvar.Group` Cumulative explained varaince in groups of X and Y `Similarity.Common.Group.load` Cumulative similarity between group and common loadings `Similarity.noncum.Common.Group.load` NonCumulative similarity between group and common loadings

## References

A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard (2013). Multi-group PLS regressMathematics and Statistics, Springer Proceedings (ed), New Perspectives in Partial Least Squares and Related Methods, 56, 243-255.

A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard (2014). Algorithms for multi-group PLS. Journal of Chemometrics, 28(3), 192-201.

`mgPCA`, `mbmgPCA`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```data(oliveoil) DataX = oliveoil[,2:6] DataY = oliveoil[,7:12] Group = as.factor(oliveoil[,1]) res.mgPLS = mgPLS (DataX, DataY, Group) barplot(res.mgPLS\$noncumper.inertiglobal) #----- Regression coefficients #res.mgPLS\$coefficients[[2]] #----- Similarity index: group loadings are compared to the common structure (in X and Y spaces) XX1= res.mgPLS\$Similarity.noncum.Common.Group.load\$X[[1]][-1, 1, drop=FALSE] XX2=res.mgPLS\$Similarity.noncum.Common.Group.load\$X[[2]][-1, 1, drop=FALSE] simX <- cbind(XX1, XX2) YY1=res.mgPLS\$Similarity.noncum.Common.Group.load\$Y[[1]][-1, 1, drop=FALSE] YY2=res.mgPLS\$Similarity.noncum.Common.Group.load\$Y[[2]][-1, 1, drop=FALSE] simY <- cbind(YY1,YY2) XLAB = paste("Dim1, %",res.mgPLS\$noncumper.inertiglobal[1]) YLAB = paste("Dim1, %",res.mgPLS\$noncumper.inertiglobal[2]) plot(simX[, 1], simX[, 2], pch=15, xlim=c(0, 1), ylim=c(0, 1), main="Similarity indices in X space", xlab=XLAB, ylab=YLAB) abline(h=seq(0, 1, by=0.2), col="black", lty=3) text(simX[, 1], simX[, 2], labels=rownames(simX), pos=2) plot(simY[, 1], simY[, 2], pch=15, xlim=c(0, 1), ylim=c(0, 1), main="Similarity indices in Y space", xlab=XLAB, ylab=YLAB) abline(h=seq(0, 1, by=0.2), col="black", lty=3) text(simY[, 1], simY[, 2], labels=rownames(simY), pos=2) ```

### Example output

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