Description Usage Arguments Value References Examples
The shape adaptive shrinkage prior of Sillanpää & Mutshinda (2011). This is essentially bridge regression, but with a
shape parameter describing the Lp norm that is allowed to vary rather than stay fixed at a single value. The generalized gaussian
prior is parameterized in the manner of Mallick, H. & Yi (2018) rather than the method originally described in Sillanpää & Mutshinda (2011).
Analytically, this makes no difference, but computationally, it is much faster and more stable. This function has the further allowance for a
set of covariates that are not penalized. For example, you may wish to include variables such as age and gender so that the coefficients for
the other variables are penalized while controlling for these. This is a common need in research.
The benefit of the shape adaptive shrinkage prior is that one need not pick a specific norm. Hence, if there is uncertainty over
whether or not one wishes to choose the L1 norm (LASSO) or L2 norm (Ridge), this integrates over a reasonable range of values. The gamma
prior for the norm has an expected value of 1.4, which gives a reasonable compromise between the LASSO and Ridge.
Model Specification:
Plugin Pseudo-Variances:
1 2 3 4 |
formula |
the model formula |
design.formula |
formula for the design covariates. |
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 "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
Sillanpää, S., & Mutshinda, C., (2011) Bayesian shrinkage analysis of QTLs under shape-adaptive shrinkage priors, and accurate re-estimation of genetic effects. Heredity volume 107, pages 405–412. doi: 10.1038/hdy.2011.37
1 | saspDC()
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