ade4 (version 1.7-19)

randboot.multiblock: Bootstraped simulations for multiblock methods

Description

Function to perform bootstraped simulations for multiblock principal component analysis with instrumental variables or multiblock partial least squares, in order to get confidence intervals for some parameters, i.e., regression coefficients, variable and block importances

Usage

# S3 method for multiblock
randboot(object, nrepet = 199, optdim, ...)

Value

A list containing objects of class krandboot

Arguments

object

an object of class multiblock created by mbpls or mbpcaiv

nrepet

integer indicating the number of repetitions

optdim

integer indicating the optimal number of dimensions, i.e., the optimal number of global components to be introduced in the model

...

other arguments to be passed to methods

Author

Stéphanie Bougeard () and Stéphane Dray ()

References

Carpenter, J. and Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.Statistics in medicine, 19, 1141-1164.

Bougeard, S. and Dray S. (2018) Supervised Multiblock Analysis in R with the ade4 Package. Journal of Statistical Software, 86 (1), 1-17. tools:::Rd_expr_doi("10.18637/jss.v086.i01")

See Also

mbpcaiv, mbpls, testdim.multiblock, as.krandboot

Examples

data(chickenk)
Mortality <- chickenk[[1]]
dudiY.chick <- dudi.pca(Mortality, center = TRUE, scale = TRUE, scannf =
FALSE)
ktabX.chick <- ktab.list.df(chickenk[2:5])
resmbpcaiv.chick <- mbpcaiv(dudiY.chick, ktabX.chick, scale = TRUE,
option = "uniform", scannf = FALSE, nf = 4)
## nrepet should be higher for a real analysis
test <- randboot(resmbpcaiv.chick, optdim = 4, nrepet = 10)
test
if(adegraphicsLoaded())
plot(test$bipc)