simul_data_UniYX {plsRglm} | R Documentation |
Data generating function for univariate plsR models
Description
This function generates a single univariate response value Y
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(totdim, ncomp)
Arguments
totdim |
Number of columns of the X vector (from |
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
vector |
|
Author(s)
Frédéric Bertrand
frederic.bertrand@utt.fr
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.
See Also
simul_data_YX
and simul_data_complete
for generating multivariate data
Examples
simul_data_UniYX(20,6)
dimX <- 6
Astar <- 2
simul_data_UniYX(dimX,Astar)
(dataAstar2 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar2,5,NK=100, verbose=FALSE)))
dimX <- 6
Astar <- 3
simul_data_UniYX(dimX,Astar)
(dataAstar3 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar3,5,NK=100, verbose=FALSE)))
dimX <- 6
Astar <- 4
simul_data_UniYX(dimX,Astar)
(dataAstar4 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar4,5,NK=100, verbose=FALSE)))
rm(list=c("dimX","Astar","dataAstar2","dataAstar3","dataAstar4"))