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)
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Xnew |
a data frame containing new observation values for the explanatory variables |
res.cw |
a list of results created by the function |
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.
1 2 3 4 5 6 7 8 9 10 11 12 |
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)
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