pqrBayes.select {pqrBayes} | R Documentation |
Variable selection for a pqrBayes object
Description
Variable selection for a pqrBayes object
Usage
pqrBayes.select(object,sparse=T,model="linear")
Arguments
object |
a pqrBayes object. |
sparse |
logical flag. If TRUE, the sparse model is used for variable selection. The default value is TRUE. |
model |
the model to be fitted. Users can also choose "linear" for a 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.
Value
an object of class ‘select’ is returned, which includes the indices of the selected predictors (e.g. genetic factors).
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 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
Examples
## The quantile regression model
data(data)
data = data$data_linear
g=data$g
y=data$y
e=data$e
fit1=pqrBayes(g,y,u=NULL,e,d = NULL,quant=0.5,spline=NULL,model="linear")
sparse=TRUE
select=pqrBayes.select(obj = fit1,sparse = sparse,model="linear")
## The quantile varying coefficient model
data(data)
data = data$data_varying
g=data$g
y=data$y
u=data$u
e=data$e
spline = list(kn=2,degree=2)
fit1=pqrBayes(g,y,u,e,d = NULL,quant=0.5,spline = spline,model="VC")
sparse=TRUE
select=pqrBayes.select(obj = fit1,sparse = sparse,model="VC")
select
## Non-sparse example with VC model
sparse <- FALSE
spline <- list(kn = 2, degree = 2)
fit2 <- pqrBayes(
g = g, y = y, u = u, e = e, d = NULL,
quant = 0.5,
spline = spline,
sparse = sparse,
model = "VC"
)
select <- pqrBayes.select(obj = fit2, sparse = FALSE, model = "VC")
select