selection: Variable selection for a Blend object

In Blend: Robust Bayesian Longitudinal Regularized Semiparametric Mixed Models

Variable selection for a Blend object

Description

Variable selection for a Blend object

Usage

selection(obj, sparse)

Arguments

obj	Blend object.
sparse	logical flag. If TRUE, spike-and-slab priors will be used to shrink coefficients of irrelevant covariates to zero exactly.

Details

If sparse, the median probability model (MPM) (Barbieri and Berger, 2004) is used to identify predictors that are significantly associated with the response variable. Otherwise, variable selection is based on 95% credible interval. Please check the references for more details about the variable selection.

Value

an object of class ‘selection’ is returned, which is a list with component:

method	posterior samples from the MCMC
indices	a list of indices and names of selected variables
summary	a summary of selected variables

References

Ren, J., Zhou, F., Li, X., Ma, S., Jiang, Y. and Wu, C. (2023). Robust Bayesian variable selection for gene-environment interactions. Biometrics, 79(2), 684-694 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/biom.13670")}

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

See Also

Blend

Examples

data(dat)
## sparse
fit = Blend(y,x,t,J,kn,degree)
selected=selection(fit,sparse=TRUE)
selected


## non-sparse
fit = Blend(y,x,t,J,kn,degree,sparse="FALSE")
selected=selection(fit,sparse=FALSE)
selected

Blend documentation built on April 3, 2025, 10:36 p.m.