mbclusterwise (version 1.0)

mbpls.fast: Multiblock partial least squares

Description

Function to perform a multiblock Partial Least Squares (PLS) of several explanatory blocks $(X_1, \dots, X_K)$ 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 $(X_1, \dots, X_K)$ and to 1 for $X$ and $Y$

H

an integer giving the number of dimensions

Value

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)