mbpcaiv.fast: Multiblock principal component analysis with instrumental...
In mbclusterwise: Clusterwise Multiblock Analyses

Description Usage Arguments Value Author(s) References See Also Examples

Function to perform a multiblock Redundancy Analysis 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 mbpcaiv function from the ade4 package with additional ones developed for the clusterwise procedure.

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

`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

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

Stephanie Bougeard (stephanie.bougeard@anses.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

cw.multiblock, cw.tenfold, cw.predict, mbpcaiv

  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    <- mbpcaiv.fast(dudiy, ktabx, scale = FALSE, option = "none", H = 2)