Description Usage Arguments Value References Examples
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). For the binomial and poisson likelihoods
plug-in pseudo-variances are used.
The model structure is given below:
Plugin Pseudo-Variances:
1 2 3 4 |
formula |
the model formula |
data |
a data frame. |
family |
one of "gaussian", "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". 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. |
A run.jags object
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
Li, Qing; Lin, Nan. The Bayesian elastic net. Bayesian Anal. 5 (2010), no. 1, 151–170. doi:10.1214/10-BA506. https://projecteuclid.org/euclid.ba/1340369796
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)
1 | adaEnet()
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