plsRglm (version 0.7.6)

simul_data_YX: Data generating function 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_YX(totdim, ncomp)

Arguments

totdim

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

ncomp

Number of latent components in the model (from 2 to 6)

Value

• vector
  $(Y_1,\ldots,Y_r,X_1,\ldots,X_{totdim})$

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a R

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

See Also

simul_data_complete for highlighting the simulations parameters

Examples

simul_data_YX(20,6)                          

dimX <- 6
Astar <- 2

(dataAstar2 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plspm)
resAstar2 <- plsreg2(dataAstar2[,4:9],dataAstar2[,1:3],nc=5)
resAstar2$Q2


dimX <- 6
Astar <- 3

(dataAstar3 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plspm)
resAstar3 <- plsreg2(dataAstar3[,4:9],dataAstar3[,1:3],nc=5)
resAstar3$Q2


dimX <- 6
Astar <- 4

(dataAstar4 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plspm)
resAstar4 <- plsreg2(dataAstar4[,5:10],dataAstar4[,1:4],nc=5)
resAstar4$Q2


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