reg_simulation3 {MSIMST} | R Documentation |
This simulation study is designed to show the effectiveness of the grouped horseshoe prior for the variable selection and the WFPBB()
function for adjusting survey weights.
reg_simulation3(
N,
ni_lambda,
beta,
beta_b,
dsq,
sigmasq,
delta,
nu,
muz,
rho,
sigmasq_z,
zeta0,
zeta1
)
N |
The number of subjects. |
ni_lambda |
The mean of Poisson distribution. |
beta |
The covariates' coefficients. A 10 by 1 vector. |
beta_b |
The slope of PD response. |
dsq |
A part of covariance parameter. |
sigmasq |
A part of covariance parameter. |
delta |
The skewness parameter. |
nu |
The degree of freedom. |
muz |
The location parameter of the latent/selection variable. |
rho |
The correlation parameter of the latent/selection variable. |
sigmasq_z |
The variance parameter of the latent/selection variable. |
zeta0 |
The intercept term inside the logistic function. |
zeta1 |
The slope term inside the logistic function. |
More details of the design of this simulation study can be found in the vignette. Users can access the vignette by the command vignette(package = "MSIMST")
.
A simulated dataset with the response variable y
, the design matrix X
and the survey weight survey_weight
.
set.seed(100)
output_data <- reg_simulation3(N = 1000,
ni_lambda= 8,
beta = c(rep(1,6),rep(0,4)),
beta_b = 1.5,
dsq = 0.1,
sigmasq = 0.5,
delta = 0.6,
nu = 5.89,
muz = 0,
rho = 36.0,
sigmasq_z = 0.6,
zeta0 = -1.8,
zeta1 = 0.1)
y <- output_data$y
X <- output_data$X
survey_weight <- output_data$survey_weight