BayesGWQS (version 0.1.1)

bgwqs.fit: Bayesian Grouped WQS Regression

Description

This function fits a Bayesian grouped weighted quantile sum (BGWQS) regression model.

Usage

bgwqs.fit(
  y,
  x,
  z,
  x.s,
  n.quantiles = 4,
  working.dir,
  n.chains = 1,
  n.iter = 10000,
  n.burnin = 5000,
  n.thin = 1,
  n.adapt = 500,
  DIC = FALSE
)

Arguments

y

A vector containing outcomes.

x

A matrix of component data.

z

A vector or matrix of controlling covariates.

x.s

A vector of the number of components in each index.

n.quantiles

The number of quantiles to apply to the component data.

working.dir

A file path to the directory.

n.chains

The number of Markov chains; must be a positive integer.

n.iter

The number of total iterations per chain, including burn in.

n.burnin

The number of iterations to discard at the beginning.

n.thin

The thinning rate; must be a positive integer.

n.adapt

The number of adaption iterations.

DIC

Logical; whether or not the user desires the function to return DIC.

Value

A list which includes BUGS output, sample chains post-burnin, and convergence test results.

Examples

# NOT RUN {
data("simdata")
group_list <- list(c("pcb_118", "pcb_138", "pcb_153", "pcb_180", "pcb_192"),
                   c("as", "cu", "pb", "sn"),
                   c("carbaryl", "propoxur", "methoxychlor", "diazinon", "chlorpyrifos"))
x.s <- make.x.s(simdata, 3, group_list)
X <- make.X(simdata, 3, group_list)
Y <- simdata$Y
work_dir <- tempdir()
results <- bgwqs.fit(y = Y, x = X, x.s = x.s, n.quantiles=4,
                     working.dir = work_dir,
                     n.chains = 1, n.iter = 10000, n.burnin = 5000, n.thin = 1, n.adapt = 500)

# }
# NOT RUN {
# }