Description Usage Arguments Details Value References See Also Examples
Variable selection for a BVCfit object
1 2 3 4 5 6 7 8 | BVSelection(obj, ...)
## S3 method for class 'BVCNonSparse'
BVSelection(obj, burn.in = obj$burn.in,
prob = 0.95, ...)
## S3 method for class 'BVCSparse'
BVSelection(obj, burn.in = obj$burn.in, ...)
|
obj |
BVCfit object. |
... |
other BVSelection arguments |
burn.in |
MCMC burn-in. |
prob |
probability for credible interval, between 0 and 1. e.g. prob=0.95 leads to 95% credible interval |
For class 'BVCSparse', the median probability model (MPM) (Barbieri and Berger 2004) is used to identify predictors that are significantly associated with the response variable. For class 'BVCNonSparse', variable selection is based on 95% credible interval. Please check the references for more details about the variable selection.
an object of class "BVSelection" is returned, which is a list with components:
method |
posterior samples from the MCMC |
indices |
a list of indices and names of selected variables |
summary |
a summary of selected variables |
Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2019) Semi-parametric Bayesian variable selection for gene-environment interactions. https://arxiv.org/abs/1906.01057
Barbieri, M.M. and Berger, J.O. (2004). Optimal predictive model selection Ann. Statist, 32(3):870–897
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