generate_data: Data generating function according to SIMP

In yanbowisc/SIMP: Bayesian Simultaneous Partial Envelope Model

Data generating function according to SIMP

Description

Data generating function according to SIMP

Usage

generate_data(
  muX1C.tru,
  muY.tru,
  beta1C.tru,
  beta1D.tru,
  beta2.tru,
  gamma.tru,
  SigmaCD.tru,
  SigmaYX.tru,
  K,
  mu2,
  SigmaX2,
  n,
  r,
  pc,
  pd,
  p2,
  ...
)

Arguments

muX1C.tru	a vector of length p_C. The true value of mu1C.
muY.tru	a vector of length r. The true value of muY.
beta1C.tru	a p_C by r matrix. The true value of beta1C.
beta1D.tru	a p_D by r matrix. The true value of beta1D.
beta2.tru	a p_2 by r matrix. The true value of beta2.
gamma.tru	a p_D by p_C matrix. The true value of gamma.
SigmaCD.tru	a p_C by p_C matrix. The true value of SigmaCD.
SigmaYX.tru	a r by r matrix. The true value of SigmaYX.
K	a positive integer. The K in the discrete uniform0,1,...,K-1 for the generation of X1D.
mu2	a vector of length p_2. The true value of mean of Normal distribution for the generation of X2.
SigmaX2	a p_2 by p_2 matrix. The true value of Covariance matrix of Normal distribution for the generation of X2.
n	Sample size.
r	Dimension of response Y.
pc	Dimension of X1C, the continuous part of the predictors of interest.
pd	Dimension of X1D, the discrete part of the predictors of interest.
p2	Dimension of X2, predictors of not main interest.
...	Other parameters needed

Examples

## Not run: 
r <- 8
pc <- 8
pd <- 2
p2 <- 2
p = pc + pd + p2
K <- 3
mu2 <- c(2, 5)
dx.tru <- 6
dy.tru <- 2
n <- 300
set.seed(2)
if (p2 > 0){
  SigmaX2 <- rinvwish(p2, diag(1, p2), p2)
}else{
  SigmaX2 <- 0
}
all_pars <- generate_par(r, pc, pd, p2, dx.tru, dy.tru)
 dat <- do.call(generate_data, c(all_pars, 
 list(K = K, mu2 = mu2, SigmaX2 = SigmaX2, n = n, r = r, pc = pc, pd = pd, p2 = p2)))

## End(Not run)

yanbowisc/SIMP documentation built on Oct. 30, 2022, 1:33 a.m.