pqrBayes (version 1.2.0)

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

Description

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

Usage

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

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.

Arguments

object

an object of class `pqrBayes'.

coefficient

the vector of quantile regression coefficients under a sparse linear model, 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 sparse linear model, binary LASSO or group LASSO, u.grid = NULL.

model

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

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,e,d = NULL,quant=0.5,model="linear")
estimation=estimation.pqrBayes(fit1,coeff,model="linear")