Description Usage Arguments Value References Examples
The Bayesian Bridge model of Mallick & Yi (2018). Bridge regression allows
you to utilize different Lp norms for the shape of the prior through the shape parameter
kappa of the power exponential distribution (also known as generalized Gaussian).
Norms of 1 and 2 give the Laplace and Gaussian distributions respectively (corresponding to
the LASSO and Ridge Regression). Norms smaller than 1 are very difficult to estimate,
but have very tall modes at zero and very long, cauchy like tails. Values greater than 2
become increasingly platykurtic, with the uniform distribution arising as it approaches infinity.
Using kappa = 1 yields the New Bayesian LASSO, which is a re-parameterization of the
Bayesian LASSO utilizing a scale mixture of uniform distributions to obtain the Laplacian
priors (Mallick & Yi, 2014).
JAGS has no built in power exponential distribution, so the distribution is parameterized
as a uniform-gamma mixture just as in Mallick & Yi (2018). For generalized linear models
plug-in pseudovariances are used.
Model Specification:
Plugin Pseudo-Variances:
1 2 3 4 |
formula |
the model formula |
data |
a data frame. |
family |
one of "gaussian", "st" (Student-t with nu=3), "binomial", or "poisson". |
kappa |
the Lp norm you wish to utilize. Default is 1.4. |
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 "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. |
a runjags object
Mallick, H. & Yi, N. (2018) Bayesian bridge regression, Journal of Applied Statistics, 45:6, 988-1008, DOI: 10.1080/02664763.2017.1324565
Mallick, H., & Yi, N. (2014). A New Bayesian Lasso. Statistics and its interface, 7(4), 571–582. doi:10.4310/SII.2014.v7.n4.a12
1 | bridge()
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