Description Usage Arguments Value References Examples
This is the horseshoe+ model described by Bhadra et al (2017). This is essentially the horseshoe homologue
of the extended LASSO. The specification is identical to the horseshoe except an additional local shrinkage parameter is
added to the model. Note that in the model I use somewhat different names for the paramters. This is because a key parameter
in the model is often called "tau" in the literature (often geared towards Stan), but JAGS uses the precision parameterization
of the normal distribution. The precision is denoted as "tau", so this warranted some new notation. See the model specification below.
Model Specification:
Plugin Pseudo-Variances:
1 2 3 |
formula |
the model formula. |
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 "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. |
an rjags object
Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon. The Horseshoe+ Estimator of Ultra-Sparse Signals. Bayesian Anal. 12 (2017), no. 4, 1105–1131. doi:10.1214/16-BA1028. https://projecteuclid.org/euclid.ba/1474572263
Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2):465–480.
1 | HSplus()
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