pqrBayes (version 1.1.2)

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

Description

Calculate estimated regression coefficients with estimation accuracy from linear 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 linear model 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 (quantile) regression model or group LASSO, u.grid = NULL.

model

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

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")