estimation.pqrBayes: Estimation and estimation accuracy for a pqrBayes object

In pqrBayes: Bayesian Penalized Quantile Regression

Estimation and estimation accuracy for a pqrBayes object

Description

Calculate estimated regression coefficients with estimation accuracy from linear, binary LASSO, group LASSO and quantile VC models, respectively.

Usage

estimation.pqrBayes(object,coefficient,u.grid=NULL,model="linear")

Arguments

object	an object of class ‘pqrBayes’.
coefficient	the vector of quantile regression coefficients under a linear model (i.e., LASSO), binary LASSO and group LASSO or the matrix of true varying coefficients evaluated on the grid points under a varying coefficient model.
u.grid	the vector of grid points under a varying coefficient model. When fitting a linear regression model (i.e., LASSO), binary LASSO or group LASSO, u.grid = NULL.
model	the model to be fitted. Users can choose "linear" for a linear model (i.e., LASSO), "binary" for binary LASSO, "group" for group LASSO or "VC" for a varying coefficient model.

Value

an object of class ‘pqrBayes.est’ is returned, which is a list with components:

error	mean square error or integrated mean square errors and total integrated mean square error.
coeff.est	estimated values of the regression coefficients or the varying coefficients.

See Also

pqrBayes

Examples

## The quantile regression model
data(data)
data = data$data_linear
g=data$g
y=data$y
e=data$e
coeff = data$coeff
fit1=pqrBayes(g,y,u=NULL,e,d = NULL,quant=0.5,spline=NULL,model="linear")
estimation=estimation.pqrBayes(fit1,coeff,model="linear")

pqrBayes documentation built on June 8, 2025, 12:35 p.m.