multiblock and multigroup Principal Component Analysis
multiblock and multigroup PCA (mbmgPCA)
mbmgPCA( Data, Group, nBlock, Block.name = NULL, ncomp = NULL, niter = NULL, ScaleGroup = FALSE, ScaleDataA = FALSE, ScaleDataB = FALSE, norm = FALSE )
Data |
a numeric (quantitative) matrix or data frame |
Group |
a vector of factors associated with group structure |
nBlock |
a vector of number of variables in each block |
Block.name |
vector of name of blocks |
ncomp |
number of components, if NULL number of components is equal to min(rank(Data), M-1) |
niter |
number of iteration, if NULL number of iteration is equal to 10 |
ScaleGroup |
scaling variables in each group and block, by defalt is FALSE |
ScaleDataA |
scaling variables in each block after group preprocessing, by defalt is FALSE |
ScaleDataB |
scaling variables in each block befor group preprocessing, by defalt is FALSE |
norm |
normalize each block, by defalt is FALSE |
list with the following results:
K.Data |
Block data |
concat.Data |
Concatenated data |
concat.block.Data |
Block concatenated data |
res.iter |
Result of iteration |
CRIT.h |
Maximization criterion for each diemnsion |
CRIT |
Maximization criterion |
crit.group |
Maximization criterion associated with each group |
crit.block |
Maximization criterion associated with each block |
omega |
Weight of each block in construction of common scores |
block.common.loading |
Common loadings for each block |
block.group.loadings |
Partial loadings for each block and group |
similarity |
Similarity among common and partial loadings for each block |
global.scores |
Global scores among blocks |
block.scores |
Scores for each block |
block.group.scores |
Scores for each block and group |
block.scores |
Scores for each block |
global.expvar |
Global explained variance |
cum.exp.var.block.group |
Cumulative explained variance for each block and group |
A. Eslami, E. M. Qannari, A. Kohler and S. Bougeard, Under Review. Multivariate data analysis of multi-groups datasets. Application to sensory analysis, Chemolab, 25, 108-123.
data(wine)
Select=c(which(wine[,2]=="Env1"),which(wine[,2]=="Env2"),which(wine[,2]=="Reference"))
WineData = wine[Select,-c(1,2)]
Group <- as.factor(c(rep("Env1",7), rep("Env2",5), rep("Reference",7)))
nBlock <- c(5, 3, 10, 9)
BlockNames <- c("Olfaction at rest", "Vision", "Olfaction after shaking", "Taste")
res = mbmgPCA(Data = WineData, Group, nBlock , Block.name=BlockNames, ncomp=5)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.