qrcmNL-package: Nonlinear Quantile Regression Coefficients Modeling

In qrcmNL: Nonlinear Quantile Regression Coefficients Modeling

Description

This package implements a nonlinear Frumento and Bottai's (2016) method for quantile regression coefficient modeling (qrcm), in which quantile regression coefficients are described by (flexible) parametric functions of the order of the quantile. In the classical qrcm framework the linearity in β(p) and/or in θ could be relaxed at a cost of more complicated expressions for the ojective and the gradient functions. Here, we propose an efficiently algorithm to use more flexible structures for the regression coefficients. With respect to the most famous function nlrq (quantreg package) our main function niqr implements the integrated quantile regression idea of Frumento and Bottai's (2016) for nonlinear functions. As already known, this practice allows to estimate quantiles all at one time and not one at a time.

Details

Package:	qrcmNL
Type:	Package
Version:	1.0
Date:	2016-10-05
License:	GPL-2

The function niqr permits specifying nonlinear basis for each variables. The function test.fit.niqr permits to do goodness of fit. The auxiliary functions summary.niqr, predict.niqr, and plot.niqr can be used to extract information from the fitted model.

Author(s)

Gianluca Sottile

Maintainer: Gianluca Sottile <[e-mail]>

References

Frumento, P., and Bottai, M. (2016). Parametric modeling of quantile regression coefficient functions. Biometrics, 72 (1), pp 74-84, doi:10.1111/biom.12410.

Examples

# use simulated data

n <- 1000
x <- runif(n)
fun <- function(theta, p){
  beta0 <- theta[1] + exp(theta[2]*p)
  beta1 <- theta[3] + theta[4]*p
  cbind(beta0, beta1)}
beta <- fun(c(1,1,1,1), runif(n))
y <- beta[, 1] + beta[, 2]*x
model <- niqr(fun=fun, x0=rep(0, 4), X=cbind(1,x), y=y)

qrcmNL documentation built on Sept. 22, 2017, 9:02 a.m.