mbpls {ade4} | R Documentation |
Function to perform a multiblock partial least squares (PLS) of several explanatory blocks (X_1, …, X_k) defined as an object of class ktab
, to explain a dependent dataset $Y$ defined as an object of class dudi
mbpls(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), scannf = TRUE, nf = 2)
dudiY |
an object of class |
ktabX |
an object of class |
scale |
logical value indicating whether the explanatory variables should be standardized |
option |
an option for the block weighting. If |
scannf |
logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
integer indicating the number of kept dimensions |
A list containing the following components is returned:
call |
the matching call |
tabY |
data frame of dependent variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
tabX |
data frame of explanatory variables centered, eventually scaled (if scale=TRUE) and weighted (if option="uniform") |
TL, TC |
data frame useful to manage graphical outputs |
nf |
numeric value indicating the number of kept dimensions |
lw |
numeric vector of row weights |
X.cw |
numeric vector of column weighs for the explanalatory dataset |
blo |
vector of the numbers of variables in each explanatory dataset |
rank |
maximum rank of the analysis |
eig |
numeric vector containing the eigenvalues |
lX |
matrix of the global components associated with the whole explanatory dataset (scores of the individuals) |
lY |
matrix of the components associated with the dependent dataset |
Yc1 |
matrix of the variable loadings associated with the dependent dataset |
cov2 |
squared covariance between lY and TlX |
Tc1 |
matrix containing the partial loadings associated with each explanatory dataset (unit norm) |
TlX |
matrix containing the partial components associated with each explanatory dataset |
faX |
matrix of the regression coefficients of the whole explanatory dataset onto the global components |
XYcoef |
list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset |
bip |
block importances for a given dimension |
bipc |
cumulated block importances for a given number of dimensions |
vip |
variable importances for a given dimension |
vipc |
cumulated variable importances for a given number of dimensions |
Stephanie Bougeard (stephanie.bougeard@anses.fr) and Stephane Dray (stephane.dray@univ-lyon1.fr)
Bougeard, S., Qannari, E.M., Lupo, C. and Hanafi, M. (2011). From multiblock partial least squares to multiblock redundancy analysis. A continuum approach. Informatica, 22(1), 11-26
mbpls
, testdim.multiblock
,
randboot.multiblock
data(chickenk) Mortality <- chickenk[[1]] dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf = FALSE) ktabX.chick <- ktab.list.df(chickenk[2:5]) resmbpls.chick <- mbpls(dudiY.chick, ktabX.chick, scale = TRUE, option = "uniform", scannf = FALSE) summary(resmbpls.chick) if(adegraphicsLoaded()) plot(resmbpls.chick)