blassoDC: Bayesian Lasso with unpenalized design covariates

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), but with the allowance for a set of covariates that are not penalized. For example, you may wish to include variables such as age and gender in all models so that the coefficients for the other variables are penalized while controlling for these. This is a common need in research.

Note only the Gaussian likelihood is provided because the Bayesian LASSO requires conditioning on the error variance, which GLM-families do not have. If you need to use the LASSO for a poisson or binomial regression, I suggest taking a look at extLASSODC
Alternatively, utilizing bridgeDC with kappa = 1 yields the New Bayesian LASSO, which is a re-parameterization of the Bayesian LASSO utilizing a scale mixture of uniform distributions to obtain the Laplacian priors (Mallick & Yi, 2014).

Usage

blassoDC(formula, design.formula, data, log_lik = FALSE, iter = 10000,
  warmup = 1000, adapt = 2000, chains = 4, thin = 1,
  method = "parallel", cl = makeCluster(2), ...)

Arguments

formula	the model formula.
design.formula	formula for the design covariates.
data	a data frame.
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 "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.

Details

Model Specification:

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

Mallick, H., & Yi, N. (2014). A New Bayesian Lasso. Statistics and its interface, 7(4), 571–582. doi:10.4310/SII.2014.v7.n4.a12

Examples

blassoDC()

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