Description Usage Arguments Value References Examples
View source: R/groupNegLASSO.R
This implements the normal-exponential-gamma "hyperlasso" of Griffin & Brown (2011) adapted here to the Kyung et al.'s (2010) group Bayesian LASSO.
This model has normal priors
on each coefficient, whose precision is modeled by group specific exponential distributions.
The exponential
distributions in turn have their respective rate parameters modeled through
independent group-level gamma(k_g * 0.50, 1 / lambda^2) distributions, where k_g is the number of predictors
in group g. If there is no grouping then this reduces to the negLASSO
.
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 | groupNegLASSO(X, y, idx, family = "gaussian", log_lik = FALSE,
iter = 10000, warmup = 1000, adapt = 2000, chains = 4,
thin = 1, method = "parallel", cl = makeCluster(3), ...)
|
X |
the model matrix. Construct this manually with model.matrix()[,-1] |
y |
the outcome variable |
idx |
the group labels. Should be of length = to ncol(model.matrix()[,-1]) with the group assignments for each covariate. Please ensure that you start numbering with 1, and not 0. |
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
Kyung, M., Gill, J., Ghosh, M., and Casella, G. (2010). Penalized regression, standard errors, and Bayesian lassos. Bayesian Analysis, 5(2):369–411.
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
Yuan, Ming; Lin, Yi (2006). Model Selection and Estimation in Regression with Grouped Variables. Journal of the Royal Statistical Society. Series B (statistical Methodology). Wiley. 68 (1): 49–67. doi:10.1111/j.1467-9868.2005.00532.x
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