makeRQS: makeRQS

In bcallaway11/qrme: Quantile Regression with Measurement Error

Description

Take the results of the optimization and convert them into a quantile regression object, so we can use all the tools from the quantreg package (e.g. inverting the quantiles). The key step here is rearrangement, because the objective function doesn't impose any ordering – see the discussion in HLLP. We follow HLLP's recommendation and order the QR parameters by what makes the quantiles to be increasing for the mean values of the x's. This means that for any particular value of the x's, the quantile function may not necessarily be increasing in tau. However, we can separately rearrange those as needed. But this gives a way to report the QR parameters.

Usage

makeRQS(params, formla, data, tau)

Arguments

params	an LxK matrix of QR parameters where L is the number of quantiles that parameters have been estimated at and K is the dimension of the covariates.
formla	y ~ x, a formula for the outcome on the regressors
data	a data.frame containing y and x
tau	the vector of quantiles where QR was estimated

Value

rqs object

bcallaway11/qrme documentation built on June 30, 2021, 12:52 p.m.