Bootstrap (Y,T) functions for PLSR

coefs.plsR.CSim(dataset, i)

Arguments

dataset	Dataset with tt
i	Index for resampling

Value

Coefficient of the last variable in the linear regression lm(dataset[i,1] ~ dataset[,-1] - 1) computed using bootstrap resampling.

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(314)
xran=matrix(rnorm(150),30,5)
coefs.plsR.CSim(xran,sample(1:30))
#> dataset[i, -1]4 
#>     -0.09647943