qrcmNL-package: Nonlinear Quantile Regression Coefficients Modeling

In gianluca-sottile/qrcmNL: Nonlinear Quantile Regression Coefficients Modeling

Description

This package implements a nonlinear Frumento and Bottai's (2015) method for quantile regression coefficient modeling (qrcm), in which quantile regression coefficients are described by (flexible) parametric functions of the order of the quantile.

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. (2015). Parametric modeling of quantile regression coefficient functions. Biometrics, 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)

gianluca-sottile/qrcmNL documentation built on May 6, 2019, 6:01 p.m.