Bootstraped simulations for multiblock methods
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 class 'multiblock' randboot(object, nrepet = 199, optdim, ...)
A list containing objects of class krandboot
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. https://doi.org/10.18637/jss.v086.i01
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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