mbpls.fast: Multiblock partial least squares

In mbclusterwise: Clusterwise Multiblock Analyses

Description

Function to perform a multiblock Partial Least Squares (PLS) of several explanatory blocks (X_1, …, X_K) defined as an object of class ktab (from ade4), to explain a dependent dataset Y defined as an object of class dudi (from ade4). This function is based on the same code and gives the same results as the mbpls function from the ade4 package with additional ones developed for the clusterwise procedure.

Usage

mbpls.fast(dudiY, ktabX, scale = FALSE, option = c("none", "uniform"), H)

Arguments

dudiY	an object of class dudi (from ade4) containing the dependent variable(s)
ktabX	an object of class ktab (from ade4) containing the blocks of explanatory variables
scale	a logical value indicating whether the explanatory variables should be standardized
option	an option for the block weighting (by default, the first option is chosen): none the block weight is equal to the block inertia uniform the block weight is equal to 1/K for (X_1, …, X_K) and to 1 for X and Y
H	an integer giving the number of dimensions

Value

A list containing the following components is returned:

crit.reg	the regression error
lX	a matrix of the global components associated with the whole explanatory dataset (scores of the individuals)
XYcoef	a list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset
intercept	a list of matrices of the regression intercepts of the whole explanatory dataset onto the dependent dataset
fitted	a list of matrices which contain the predicted dependent values

Author(s)

Stephanie Bougeard ([e-mail])

References

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

See Also

cw.multiblock, cw.tenfold, cw.predict, mbpls

Examples

  data(simdata.red) 
  Data.X <- simdata.red[c(1:15, 21:35), 1:10]
  Data.Y <- simdata.red[c(1:15, 21:35), 11:13]
  library(ade4)
  dudiy  <- dudi.pca(df = Data.Y, center = FALSE, scale = FALSE, scannf = FALSE)
  ktabx  <- ktab.data.frame(df = data.frame(Data.X), blocks = c(5,5), 
    tabnames = paste("Tab", c(1:2), sep = "."))
  res    <- mbpls.fast(dudiy, ktabx, scale = FALSE, option = "none", H = 2)

Example output

Loading required package: ade4
Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
Loading required package: kknn
Warning messages:
1: In res$lX[, h]/sqrt(t(res$lX[, h]) %*% res$lX[, h]) :
  Recycling array of length 1 in vector-array arithmetic is deprecated.
  Use c() or as.vector() instead.

2: In res$lX[, h]/sqrt(t(res$lX[, h]) %*% res$lX[, h]) :
  Recycling array of length 1 in vector-array arithmetic is deprecated.
  Use c() or as.vector() instead.

mbclusterwise documentation built on May 2, 2019, 9:19 a.m.