Description Usage Arguments Value References See Also Examples
Multigroup PCA algorithm (NIPALS for Multigroup PCA)
1 |
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 |
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.group |
Loadings associated with each group |
score.group |
Scores associated with each group |
loadings.common |
Matrix of common loadings |
score.Global |
Global scores |
cumper.inertigroup |
Cumulative percentage of group components inertia |
cumper.inertiglobal |
Cumulative percentage of global component inertia |
noncumper.inertiglobal |
Percentage of global component inertia |
lambda |
The specific variances of groups |
exp.var |
Percentages of total variance recovered associated with each dimension |
Similarity.Common.Group.load |
Cumulative similarity between group and common loadings |
Similarity.noncum.Common.Group.load |
NonCumulative similarity between group and common loadings |
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.
A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard (2013). Analyses factorielles de donnces structurces en groupes d'individus, Journal de la Societe Francaise de Statistique, 154(3), 44-57.
BGC
, FCPCA
, DCCSWA
, DSTATIS
, DGPA
, summarize
, TBWvariance
, loadingsplot
, scoreplot
, iris
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 | Data = iris[,-5]
Group = iris[,5]
res.mgPCA = mgPCA (Data, Group)
barplot(res.mgPCA$noncumper.inertiglobal)
#----------------
#Similarity index: group loadings are compared to the common structure (first dimension)
Xzero = rep(0, 3)
MIN = min(res.mgPCA$Similarity.noncum.Common.Group.load[[1]][-1, 1])-0.0005
XLAB = paste("Dim1, %",res.mgPCA$noncumper.inertiglobal[1])
plot(Xzero, res.mgPCA$Similarity.noncum.Common.Group.load[[1]][-1, 1], pch=15, ylim=c(MIN, 1),
main="Similarity between groups and common structure", xlab=XLAB, ylab="", xaxt="n")
abline(v=0)
abline(h=seq(MIN, 1, by=0.05), col="black", lty=3)
XX=res.mgPCA$Similarity.noncum.Common.Group.load[[1]][-1, 1, drop=FALSE]
text(Xzero, XX, labels=rownames(XX), pos=4)
#----------------
# Similarity index: group loadings are compared to the common structure (dimensions 1 and 2)
XX1=res.mgPCA$Similarity.noncum.Common.Group.load[[1]][-1, 1]
XX2=res.mgPCA$Similarity.noncum.Common.Group.load[[2]][-1, 1]
simil <- cbind(XX1, XX2)
YLAB = paste("Dim1, %",res.mgPCA$noncumper.inertiglobal[2])
plot(simil, xlab=XLAB, ylab=YLAB, main="Similarity between groups and common structure", pch=20)
text(simil, labels=rownames(simil), cex=1, font.lab=1, pos=3)
#------------------
loadingsplot(res.mgPCA, axes=c(1,2), INERTIE=res.mgPCA$noncumper.inertiglobal)
scoreplot(res.mgPCA, axes=c(1,2))
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.