qrcm-package {qrcm}

Quantile Regression Coefficients Modeling

Description

This package implements quantile regression coefficient modeling (qrcm), in which the coefficients of a quantile regression model are described by (flexible) parametric functions. The method is described in Frumento and Bottai (2016, 2017); Frumento and Salvati (2021); Frumento, Bottai, and Fernandez-Val (2021); and Hsu, Wen, and Chen (2021). Special functions can be used to diagnose and eliminate quantile crossing (Sottile and Frumento, 2023).

Details

Package:	qrcm
Type:	Package
Version:	3.1
Date:	2024-02-13
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.

Author(s)

Paolo Frumento

Maintainer: Paolo Frumento <[e-mail]>

References

Frumento, P., and Bottai, M. (2016). Parametric modeling of quantile regression coefficient functions. Biometrics, 72 (1), 74-84.

Frumento, P., and Bottai, M. (2017). Parametric modeling of quantile regression coefficient functions with censored and truncated data. Biometrics, 73 (4), 1179-1188.

Frumento, P., and Salvati, N. (2021). Parametric modeling of quantile regression coefficient functions with count data. Statistical Methods and Applications, 30, 1237-1258.

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, 116 (534), 783-797.

Hsu, C.Y., Wen, C.C., and Chen, Y.H. (2021). Quantile function regression analysis for interval censored data, with application to salary survey data. Japanese Journal of Statistics and Data Science, 4, 999-1018.

Sottile, G., and Frumento, P. (2023). Parametric estimation of non-crossing quantile functions. Statistical Modelling, 23 (2), 173-195.

Frumento, P., and Corsini, L. (2024). Using parametric quantile regression to investigate determinants of unemployment duration. Unpublished manuscript.

Examples

 # iqr(y ~ x) # cross-sectional observations
 # iqr(Surv(time, event) ~ x) # right-censored data
 # iqr(Surv(start, stop, event) ~ x) # right-censored and left-truncated data
 # iqr(Surv(time1, time2, type = "interval") ~ x) # interval-censored data
 # iqrL(y ~ x, id = id) # repeated measures
 
 # diagnose.qc(model) # diagnose quantile crossing
 # Use iqr(..., remove.qc = TRUE) to remove crossing