Permutation Bootstrap (Y,T) function for plsRglm
permcoefs.sgpls.CSim( dataRepYtt, ind, nt, modele, family = binomial, 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 |
Numeric vector of the components computed using a bootstrap
resampling or ifbootfail
value if the
bootstrap computation fails.
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(4619) xran=cbind(rbinom(30,1,.2),matrix(rnorm(150),30,5)) permcoefs.sgpls.CSim(xran, ind=sample(1:nrow(xran)), maxcoefvalues=1e5, ifbootfail=rep(NA,3))#> Tb1 Tb2 Tb3 Tb4 Tb5 #> -0.2608434 0.7512146 -4.2041446 -2.2336737 -0.2452805