pqrBayes (version 1.2.0)

pqrBayes.select: Variable selection for a pqrBayes object

Description

Variable selection for a pqrBayes object

Usage

pqrBayes.select(object,prior="SS",model="linear")

Value

an object of class `select' is returned, which includes the indices of the selected predictors (e.g. genetic factors).

Arguments

object

a pqrBayes object.

prior

the prior used in the pqrBayes function. Users can choose "SS" for the spike-and-slab prior, "HS" for the horseshoe prior, "HS+" for the horseshoe plus prior, "RHS" for the regularized horseshoe prior and "Laplace" for the Laplace prior. The default value is "SS".

model

the model to be fitted. Users can also choose "linear" for a sparse linear model, "VC" for a varying coefficient model or "group" for group LASSO.

Details

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

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 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

pqrBayes

Examples

## The sparse quantile regression model
data(data)
data = data$data_linear
g=data$g
y=data$y
e=data$e

fit1=pqrBayes(g,y,e,d = NULL,quant=0.5,model="linear")
select=pqrBayes.select(obj = fit1,prior = "SS",model="linear")

## The quantile varying coefficient model
data(data)
data = data$data_varying
g=data$g
y=data$y
e=data$e
fit1=pqrBayes(g,y,e,d = NULL,quant=0.5,model="VC")
select=pqrBayes.select(obj = fit1,prior = "SS",model="VC")
select

# \donttest{
## Non-sparse example with VC model
fit2 <- pqrBayes(
  g = g, y = y, e = e, d = NULL,
  quant = 0.5,
  prior= "Laplace",
  model = "VC"
)

select <- pqrBayes.select(obj = fit2, prior = "SS", model = "VC")
select
# }