
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
# S3 method for multiblock
randboot(object, nrepet = 199, optdim, ...)
A list containing objects of class krandboot
an object of class multiblock created by mbpls
or mbpcaiv
integer indicating the number of repetitions
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
Stéphanie Bougeard (stephanie.bougeard@anses.fr) and Stéphane Dray (stephane.dray@univ-lyon1.fr)
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")
mbpcaiv
, mbpls
,
testdim.multiblock
, as.krandboot
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)
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