simul_data_complete: Data generating detailed process for multivariate plsR models

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Data generating detailed process for multivariate plsR models

Description

This function generates a single multivariate response value \boldsymbol{Y} and a vector of explinatory variables (X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_complete(totdim, ncomp)

Arguments

totdim	Number of columns of the X vector (from ncomp to hardware limits)
ncomp	Number of latent components in the model (from 2 to 6)

Details

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

Value

simX	Vector of explanatory variables
HH	Dimension of the response \boldsymbol{Y}
eta	See Li et al.
r	See Li et al.
epsilon	See Li et al.
ksi	See Li et al.
f	See Li et al.
z	See Li et al.
Y	See Li et al.

Note

The value of r depends on the value of ncomp :

ncomp	r
2	3
3	3
4	4

Author(s)

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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0169-7439(02)00051-5")}.

See Also

simul_data_YX for data simulation purpose

Examples

simul_data_complete(20,6)                          

dimX <- 6
Astar <- 2
simul_data_complete(dimX,Astar)


dimX <- 6
Astar <- 3
simul_data_complete(dimX,Astar)


dimX <- 6
Astar <- 4
simul_data_complete(dimX,Astar)

rm(list=c("dimX","Astar"))

plsRglm documentation built on March 31, 2023, 11:10 p.m.