mgPLS: Multigroup Partial Least Squares Regression

In multigroup: Multigroup Data Analysis

Description

Multigroup PLS regression

Usage

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.

See Also

mgPCA, mbmgPCA

Examples

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

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