qrcm-package: Quantile Regression Coefficients Modeling
In qrcm: Quantile Regression Coefficients Modeling

Description Details Author(s) References Examples

This package implements Frumento and Bottai's (2016, 2017) method for quantile regression coefficient modeling (qrcm), in which quantile regression coefficients are described by (flexible) parametric functions of the order of the quantile. The package includes a generalization to longitudinal data (Frumento et al 2021). Special functions can be used to eliminate quantile crossing (Sottile and Frumento 2021).

Package:	qrcm
Type:	Package
Version:	3.0
Date:	2021-01-29
License:	GPL-2

The function iqr permits specifying regression models for cross-sectional data, allowing for censored and truncated outcomes. The function iqrL can be used to analyze longitudinal data in which the same individuals are observed repeatedly.

Two special functions, slp and plf, can be used for model building. Auxiliary functions for model summary, prediction, and plotting are provided. The generic function test.fit is used to assess the model fit.

The function diagnose.qc can be applied to iqr objects to diagnose quantile crossing, and the option remove.qc can be used to remove it, using the algorithm described in qc.control.

Paolo Frumento

Maintainer: Paolo Frumento <paolo.frumento@unipi.it>

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

Frumento, P., and Bottai, M. (2017). Parametric modeling of quantile regression coefficient functions with censored and truncated data. Biometrics, doi: 10.1111/biom.12675.

Frumento, P., Bottai, M., and Fernandez-Val, I. (2021). Parametric modeling of quantile regression coefficient functions with longitudinal data. Journal of the American Statistical Association [forthcoming].

Sottile, G., and Frumento, P. (2021). Parametric estimation of non-crossing quantile functions. Statistical Modelling [forthcoming].

 # iqr(y ~ x) # cross-sectional observations
 # iqr(Surv(time, event) ~ x) # censored data
 # iqr(Surv(start, stop, event) ~ x) # censored and truncated data
 # iqrL(y ~ x, id = id) # repeated measures

qrcm documentation built on Feb. 2, 2021, 9:07 a.m.