Bootstrap (Y,T) function for plsRglm — coefs.sgpls.CSim • bootPLS

A function passed to boot to perform bootstrap.

coefs.sgpls.CSim(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = binomial,
  maxcoefvalues,
  ifbootfail
)

Arguments

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

Value

Numeric vector of the components computed using a bootstrap resampling or ifbootfail value if the bootstrap computation fails.

References

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, doi:10.3389/fams.2021.693126
.

Author

Jérémy Magnanensi, Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

Examples

set.seed(4619)
xran=cbind(rbinom(30,1,.2),matrix(rnorm(150),30,5))
coefs.sgpls.CSim(xran, ind=sample(1:nrow(xran)), 
maxcoefvalues=1e5, ifbootfail=rep(NA,3))
#>       Tb1       Tb2       Tb3       Tb4       Tb5 
#> 0.5350061 0.4016055 0.2119392 0.3488254 0.2612697