IAt: Stochastic Search Variable Selection (Indicator Variable and...

In abnormally-distributed/Bayezilla: Bayesian Models With JAGS and Several Utilities for Data Exploration and Model Evaluation.

Description

This is the Indicator variables and Adaptive Student’s t-distributions (IAt) model discussed by Knürr, Läärä, and Sillanpää (2011).

Model Specification:

Usage

IAt(formula, data, family = "gaussian", log_lik = FALSE,
  iter = 10000, warmup = 5000, adapt = 5000, chains = 4,
  thin = 2, method = "parallel", cl = makeCluster(2), ...)

Arguments

formula	the model formula
data	a data frame.
family	one of "gaussian", "binomial", or "poisson".
log_lik	Should the log likelihood be monitored? The default is FALSE.
iter	How many post-warmup samples? Defaults to 5000.
warmup	How many warmup samples? Defaults to 5000.
adapt	How many adaptation steps? Defaults to 10000.
chains	How many chains? Defaults to 4.
thin	Thinning interval. Defaults to 2.
method	Defaults to "parallel". For an alternative parallel option, choose "rjparallel" or. Otherwise, "rjags" (single core run).
cl	Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores.
...	Other arguments to run.jags.

Value

A run.jags object

Examples

IAt()

abnormally-distributed/Bayezilla documentation built on Oct. 31, 2019, 1:57 a.m.