cw.predict: Prediction procedure for clusterwise multiblock analyses
In mbclusterwise: Clusterwise Multiblock Analyses

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

Function to perform the prediction of new observations by means of clusterwise multiblock analysis

1	cw.predict(Xnew, res.cw)

`Xnew`	a data frame containing new observation values for the explanatory variables
`res.cw`	a list of results created by the function `cw.multiblock`

A list containing the following components is returned:

`clusternew`	a vector containing the new observation assignation to the G expected clusters (when G>1 only)
`Ypred.cr`	a matrix that contain the predicted dependent values associated with the centered and scaled data for each of the G clusters
`Ypred.raw`	a matrix that contain the predicted dependent values associated with the raw data for each of the G clusters

Stephanie Bougeard (stephanie.bougeard@anses.fr)

Bougeard, S., Abdi, H., Saporta, G., Niang, N., Submitted, Clusterwise analysis for multiblock component methods.

cw.multiblock, cw.tenfold

 
  data(simdata.red) 
  Data.X      <- simdata.red[c(1:10, 21:30), 1:10]
  Data.Y      <- simdata.red[c(1:10, 21:30), 11:13]
  Data.X.test <- simdata.red[c(16:20, 36:40), 1:10]
  ## Note that the options (INIT=2) and (parallel.level = "low") are chosen to quickly
  ## illustrate the function. 
  ## For real data, instead choose (INIT=20) to avoid local optima and (parallel.level = "high")
  ## to improve the computing speed.
  res.cw      <- cw.multiblock(Y = Data.Y, X = Data.X, blo = c(5, 5), option = "none", G = 2,
                  H = 1, INIT = 2, method = "mbpls", Gamma = NULL, parallel.level = "low")
  rescw.pred  <- cw.predict(Data.X.test, res.cw)