Description Usage Arguments Value References Examples
This is the horseshoe model described by Carvalho et al. (2010), but with the allowance
for a set of covariates that are not penalized. For example, you may wish to include variables such
as age and gender in all models so that the coefficients for the other variables are penalized while
controlling for these. This is a common need in research.
This tends to run very quickly even for larger data sets or larger
numbers of predictors and in my experience is faster and more stable (at least
on the tested data sets!) than the same model implemetned in Stan.
Model Specification:
Plugin Pseudo-Variances:
1 2 3 |
formula |
the model formula |
design.formula |
formula for the design covariates. |
data |
a data frame. |
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. |
design.formula |
formula for the design covariates. |
an rjags object
Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2):465–480.
1 | HSDC()
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