boot
to perform bootstrap.R/bootstrap_functions.R
permcoefs.plsRglm.CSim.Rd
Permutation bootstrap (Y,T) function for PLSGLR
A function passed to boot
to perform bootstrap.
permcoefs.plsRglm.CSim( dataRepYtt, ind, nt, modele, family = NULL, maxcoefvalues, ifbootfail )
dataRepYtt | Dataset with tt components to resample |
---|---|
ind | indices for resampling |
nt | number of components to use |
modele | type of modele to use, see plsRglm. Not used, please specify the family instead. |
family | glm family to use, see plsRglm |
maxcoefvalues | maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples |
ifbootfail | value to return if the estimation fails on a bootstrap sample |
estimates on a bootstrap sample or ifbootfail
value if the
bootstrap computation fails.
Numeric vector of the components computed using a permutation resampling.
A new bootstrap-based stopping criterion in PLS component construction,
J. Magnanensi, M. Maumy-Bertrand, N. Meyer and F. Bertrand (2016), in The Multiple Facets of Partial Least Squares and Related Methods,
doi: 10.1007/978-3-319-40643-5_18
A new universal resample-stable bootstrap-based stopping criterion for PLS component construction,
J. Magnanensi, F. Bertrand, M. Maumy-Bertrand and N. Meyer, (2017), Statistics and Computing, 27, 757–774.
doi: 10.1007/s11222-016-9651-4
New developments in Sparse PLS regression, J. Magnanensi, M. Maumy-Bertrand, N. Meyer and F. Bertrand, (2021), Frontiers in Applied Mathematics and Statistics, accepted.
Jérémy Magnanensi, Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
set.seed(314) library(plsRglm) data(aze_compl, package="plsRglm") Xaze_compl<-aze_compl[,2:34] yaze_compl<-aze_compl$y dataset <- cbind(y=yaze_compl,Xaze_compl) modplsglm <- plsRglm::plsRglm(y~.,data=dataset,4,modele="pls-glm-logistic")#> ____************************************************____#> Error in is.data.frame(data): objet 'dataset' introuvable#> Error in cbind(y = modplsglm$RepY, modplsglm$tt): objet 'modplsglm' introuvablepermcoefs.plsRglm.CSim(dataRepYtt, sample(1:nrow(dataRepYtt)), 4, family = binomial, maxcoefvalues=10, ifbootfail=0)#> Error in permcoefs.plsRglm.CSim(dataRepYtt, sample(1:nrow(dataRepYtt)), 4, family = binomial, maxcoefvalues = 10, ifbootfail = 0): objet 'dataRepYtt' introuvable