simul_data_UniYX_gamma {bootPLS}

Data generating function for univariate gamma plsR models

Description

This function generates a single univariate gamma response value Ygamma and a vector of explanatory variables (X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_UniYX_gamma(totdim, ncomp, jvar, lvar, link = "inverse", offset = 0)

Arguments

totdim

Number of columns of the X vector (from ncomp to hardware limits)

ncomp

Number of latent components in the model (to use noise, select ncomp=3)

jvar

First variance parameter

lvar

Second variance parameter

link

Character specification of the link function in the mean model (mu). Currently, "inverse", "log" and "identity" are supported. Alternatively, an object of class "link-glm" can be supplied.

offset

Offset on the linear scale

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a modification of a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

vector

(Ygamma,X_1,\ldots,X_{totdim})

Author(s)

Jeremy Magnanensi, Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

Jérémy Magnanensi, Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576.
Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002), 79-89, doi: 10.1016/S0169-7439(02)00051-5.

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 Compututing, 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.

See Also

simul_data_UniYX

Examples

set.seed(314)
ncomp=rep(3,100)
totdimpos=7:50
totdim=sample(totdimpos,100,replace=TRUE)
l=3.01
#for (l in seq(3.01,15.51,by=0.5)) {
j=3.01
#for (j in seq(3.01,9.51,by=0.5))  {
i=44
#for ( i in 1:100){
set.seed(i)
totdimi<-totdim[i]
ncompi<-ncomp[i]
datasim <- t(replicate(200,simul_data_UniYX_gamma(totdimi,ncompi,j,l)))
#}
#}
#}
pairs(datasim)
rm(i,j,l,totdimi,ncompi,datasim)