Description Usage Arguments Value References Examples
The Bayesian elastic net described by Li and Lin (2010). For the binomial and poisson likelihoods plug-in pseudo-variances are used.
The model structure is given below:
Plugin Pseudo-Variances:
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formula |
the model formula |
data |
a data frame. |
family |
one of "gaussian", "st" (Student-t with nu = 3), "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" 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. |
A run.jags object
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
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