blasso: Bayesian LASSO

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

Description

The Bayesian LASSO of Park & Casella (2008). The Bayesian Lasso is equivalent to using independent double exponential (Laplace distribution) priors on the coefficients with a scale of sigma / lambda. However, doing this directly results in slow convergence and poor mixing. The Laplace distribution can be expressed as a scale mixture of normals with an exponential distribution as the scale parameter. This is the method that Park & Casella (2008) utilize and the method that is utilized here. The hierarchical structure of the prior distribution is given below.

Note that for the binomial and poisson likelihood functions plug-in pseudovariances are used.
Model Specification:


Plugin Pseudo-Variances:

Usage

blasso(formula, data, family = "gaussian", lambda.prior = "dmouch",
  log_lik = FALSE, iter = 10000, warmup = 1000, adapt = 2000,
  chains = 4, thin = 1, method = "rjparallel", cl = makeCluster(2),
  ...)

Arguments

formula	the model formula
data	a data frame.
family	one of "gaussian", "st" (Student-t with nu=3), "binomial", or "poisson".
lambda.prior	either "dmouch" (the default) or "gamma"
log_lik	Should the log likelihood be monitored? The default is FALSE.
iter	How many post-warmup samples? Defaults to 10000.
warmup	How many warmup samples? Defaults to 1000.
adapt	How many adaptation steps? Defaults to 2000.
chains	How many chains? Defaults to 4.
thin	Thinning interval. Defaults to 1.
method	Defaults to "rjparallel". For an alternative parallel option, choose "parallel" 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 runjags object

References

Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681-686. Retrieved from http://www.jstor.org/stable/27640090

Examples

blasso()

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