mbclusterwise (version 1.0)

cw.multiblock: Clusterwise multiblock analyses

Description

Function to perform a clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or clusterwise regularized multiblock regression) of several explanatory blocks $(X_1, \dots, X_K)$ to explain a dependent dataset $Y$.

Usage

cw.multiblock(Y, X, blo, option = c("none", "uniform"), G, H, INIT = 20,  method = c("mbpls", "mbpcaiv", "mbregular"), Gamma = NULL,  parallel.level = c("high", "low"))

Arguments

Y

a matrix or data frame containing the dependent variable(s)

X

a matrix or data frame containing the explanatory variables

blo

a vector of the numbers of variables in each explanatory dataset

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 $Y$

G

an integer giving the expected number of clusters

H

an integer giving the expected number of dimensions of the component-based model

INIT

an integer giving the number of initializations required for the clusterwise analysis (20 by default)

method

an option for the multiblock method to be applied (by default, the first option is chosen): mbpls multiblock Partial Least Squares is applied mbpcaiv multiblock Redundancy Analysis is applied mbregular multiblock regularized regression is applied

Gamma

a numeric value of the regularization parameter for the multiblock regularized regression comprised between 0 and 1 (NULL by default). The value (Gamma=0) leads to multiblock Redundancy Analysis and (Gamma=1) to multiblock PLS

parallel.level

Level of parallel computing, i.e. initializations are carried out simultaneously (high by default) high includes all the processing units of your computer low includes only two processing units of your computer

Value

References

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

See Also

cw.tenfold, cw.predict

Examples

  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]
  ## 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 = "mbpcaiv", Gamma = NULL, parallel.level = "low")