bgwqs.fit: Bayesian Grouped WQS Regression

In BayesGWQS: Bayesian Grouped Weighted Quantile Sum 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)


## End(Not run)

BayesGWQS documentation built on Jan. 21, 2022, 1:09 a.m.