mbclusterwise (version 1.0)

mbclusterwise-package: \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}mbclusterwiseClusterwise Multiblock Analyses

Description

\Sexpr[results=rd,stage=build]{tools:::Rd_package_description("#1")}mbclusterwisePerform clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or a regularized method between the two latter ones) associated with a F-fold cross-validation procedure to select the optimal number of clusters and dimensions.

Details

The DESCRIPTION file: \Sexpr[results=rd,stage=build]{tools:::Rd_package_DESCRIPTION("#1")}mbclusterwiseThis package was not yet installed at build time.

\Sexpr[results=rd,stage=build]{tools:::Rd_package_indices("#1")}mbclusterwise Index: This package was not yet installed at build time.

References

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

See Also

ade4

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 = "mbpls", Gamma = NULL, parallel.level = "low")