simul_data_complete: 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)

Value

simXVector of explanatory variables
HHDimension of the response $\boldsymbol{Y}$
etaSee Li et al.
rSee Li et al.
epsilonSee Li et al.
ksiSee Li et al.
fSee Li et al.
zSee Li et al.
YSee Li et al.

Details

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

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576. http://dx.doi.org/10.1080/03610918508812458 Baibing Li, Julian Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89. http://dx.doi.org/10.1016/S0169-7439(02)00051-5

Examples

Run this code

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"))

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