qrcmPen-package: Penalized Quantile Regression Coefficients Modeling

In gianluca-sottile/qrcmPen: Penalized Quantile Regression Coefficients Modeling

Description

This package implements a penalized 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. This package fits lasso qrcm using coordinate descent.

Details

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

The function piqr permits specifying the lasso regression model. The function gof.piqr permits to select the best tuning parameter through AIC, BIC, GIC and GCV criteria. The auxiliary functions summary.piqr, predict.piqr, and plot.piqr 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.

Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010.

Examples

# use simulated data

n <- 1000
x <- rexp(n)
y <- runif(n, 0, 1 + x)
model <- piqr(y ~ x, formula.p = ~ p + I(p^2))

gianluca-sottile/qrcmPen documentation built on May 8, 2019, 9:23 a.m.