groupAdaEnet: Group+Within Group Selection with Bayesian Adaptive Elastic...

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

Description

This is an adaptation of the frequentist adaptive elastic net of Ghosh (2007, 2011) and Zou & Zhang (2009) to the Bayesian paradigm through a modification of the Bayesian elastic net (Li & Lin, 2010). It is further adapted such that coefficients can be assigned groups in the spirit of the Group LASSO (Yuan & Lin, 2006) and Group Bayesian LASSO (Kyung et al., 2010). Each group receives an independent L1 norm penalty, which is combined with the top level L2 penalty on a coefficient specific basis via the truncated gamma priors.

The model structure is given below:

Usage

groupAdaEnet(X, y, idx, family = "gaussian", log_lik = FALSE,
  iter = 10000, warmup = 5000, adapt = 5000, chains = 4,
  thin = 3, method = "parallel", cl = makeCluster(2), ...)

Arguments

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" (default), "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 5000.
adapt	How many adaptation steps? Defaults to 5000.
chains	How many chains? Defaults to 4.
thin	Thinning interval. Defaults to 3.
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

References

Ghosh, S. (2007) Adaptive Elastic Net: A Doubly Regularized method for variable selection to Achieve Oracle Properties. Tech. Rep. pr07-01, available at http://www.math.iupui.edu/research/preprints.php, IUPUI

Ghosh, S. (2011) On the grouped selection and model complexity of the adaptive elastic net. Statistics and Computing 21, no. 3, 451. https://doi.org/10.1007/s11222-010-9181-4

Kyung, M., Gill, J., Ghosh, M., and Casella, G. (2010). Penalized regression, standard errors, and bayesian lassos. Bayesian Analysis, 5(2):369–411.

Li, Qing; Lin, Nan. (2010) The Bayesian elastic net. Bayesian Anal. 5, no. 1, 151–170. doi:10.1214/10-BA506. https://projecteuclid.org/euclid.ba/1340369796

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

Zou, H.; Zhang, H. (2009) On the adaptive elastic-net with a diverging number of parameters, Ann. Statist. 37 , no. 4, 1733–1751, DOI 10.1214/08-AOS625. MR2533470 (2010j:62210)

Examples

groupAdaEnet()

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