Description Usage Arguments Value References Examples
This implements the normal-exponential-gamma "hyperlasso" of Griffin & Brown (2011). This model has independent normal priors
on each coefficient, whose precision is modeled by independent, predictor specific, exponential distributions. The exponential
distributions in turn have their respective rate parameters modeled through independent gamma(.5, 1 / lambda^2) distributions.
Lambda is a single top-level hyperparameter here given a gamma(0.50 , 0.20) prior.
The model specification is given below:
Model Specification:
1 2 3 4 | negLASSODC(formula, design.formula, data, family = "gaussian",
log_lik = FALSE, iter = 10000, warmup = 1000, adapt = 2000,
chains = 4, thin = 1, method = "parallel", cl = makeCluster(3),
...)
|
formula |
the model formula |
design.formula |
formula for the design covariates. |
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 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 another parallel option, choose "rjparallel" or "rjags" for a single core run. |
cl |
Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to 3. |
... |
Other arguments to run.jags. |
A run.jags object
Griffin, J. E. and Brown, P. J. (2011), Bayesian Hyper‐LASSOs With Non-Convex Penalization. Australian & New Zealand Journal of Statistics, 53: 423-442. doi:10.1111/j.1467-842X.2011.00641.x
1 |
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