drawlatentOR2: Samples latent variable z in the OR2 model

In bqror: Bayesian Quantile Regression for Ordinal Models

Samples latent variable z in the OR2 model

Description

This function samples the latent variable z from a univariate truncated normal distribution in the OR2 model (ordinal quantile model with exactly 3 outcomes).

Usage

drawlatentOR2(y, x, beta, sigma, nu, theta, tau2, gammacp)

Arguments

y	observed ordinal outcomes, column vector of size (n x 1).
x	covariate matrix of size (n x k) including a column of ones with or without column names.
beta	Gibbs draw of \beta, a column vector of size (k x 1).
sigma	\sigma, a scalar value.
nu	modified latent weight, column vector of size (n x 1).
theta	(1-2p)/(p(1-p)).
tau2	2/(p(1-p)).
gammacp	row vector of cut-points including -Inf and Inf.

Details

This function samples the latent variable z from a univariate truncated normal distribution.

Value

latent variable z of size (n x 1) from a univariate truncated distribution.

References

Albert, J., and Chib, S. (1993). “Bayesian Analysis of Binary and Polychotomous Response Data.” Journal of the American Statistical Association, 88(422): 669–679. DOI: 10.1080/01621459.1993.10476321

Devroye, L. (2014). “Random variate generation for the generalized inverse Gaussian distribution.” Statistics and Computing, 24(2): 239–246. DOI: 10.1007/s11222-012-9367-z

See Also

Gibbs sampling, truncated normal distribution, rtruncnorm

Examples

set.seed(101)
data("data25j3")
y <- data25j3$y
xMat <- data25j3$x
beta <- c(1.810504, 1.850332, 6.181163)
sigma <- 0.9684741
n <- dim(xMat)[1]
nu <- array(5 * rep(1,n), dim = c(n, 1))
theta <- 2.6667
tau2 <- 10.6667
gammacp <- c(-Inf, 0, 3, Inf)
output <- drawlatentOR2(y, xMat, beta, sigma, nu,
theta, tau2, gammacp)

# output
#   1.257096 10.46297 4.138694
#   28.06432 4.179275 19.21582
#   11.17549 13.79059 28.3650 .. soon

bqror documentation built on May 31, 2023, 5:19 p.m.