BVSelection {spinBayes}

Variable selection for a BVCfit object

Description

Variable selection for a BVCfit object

Usage

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, ...)

Arguments

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

Details

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.

Value

an object of class "BVSelection" is returned, which is a list with components:

• method: method used for identifying important effects

• indices: a list of indices and names of selected variables

• summary: a summary of selected variables

References

Ren, J., Zhou, F., Li, X., Chen, Q., Zhang, H., Ma, S., Jiang, Y., Wu, C. (2020) Semiparametric Bayesian variable selection for gene-environment interactions. Statistics in Medicine, 39(5): 617– 638 doi:10.1002/sim.8434

Barbieri, M.M. and Berger, J.O. (2004). Optimal predictive model selection Ann. Statist, 32(3):870–897

See Also

BVCfit

Examples

data(gExp)
## sparse
spbayes=BVCfit(X, Y, Z, E, clin)
spbayes

selected = BVSelection(spbayes)
selected$indices

## non-sparse
spbayes=BVCfit(X, Y, Z, E, clin, sparse=FALSE)
spbayes

selected = BVSelection(spbayes)
selected