We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021). The method begins by fitting a flexible density regression model using monotonic splines whose weights are modeled as data-dependent functions using artificial neural networks. Subsequently, estimates of conditional density and quantile process can all be obtained. Unlike many approaches to quantile regression that assume a linear model, SPQR allows for virtually any relationship between the covariates and the response distribution including non-linear effects and different effects on different quantile levels. To increase the interpretability and transparency of SPQR, model-agnostic statistics developed by Apley and Zhu (2020) are used to estimate and visualize the covariate effects and their relative importance on the quantile function. In this article, we detail how this framework is implemented in SPQR and illustrate how this package should be used in practice through simulated and real data examples.
翻译:我们开发了一套R包 SPQR, 在 Xu 和 Reich (2021年) 中应用半参数四分回归法(SPQR) 。 这种方法首先使用一个灵活的密度回归模型, 安装一个灵活的密度回归模型, 其重量以单调样板为模型, 使用人工神经网络作为数据依赖功能的模型。 随后, 可以全部获得有条件密度和四分位进程的估计。 与许多假设线性模型的四分位回归方法不同, SPQR 允许几乎在共变和响应分布之间建立任何关系, 包括非线性效应和对不同量级的不同影响 。 为了提高 SPQR 的可解释性和透明度, Apley 和 Zhu (2020年) 开发的模型认知性统计数据被用于估计和直观共变效应及其对量函数的相对重要性。 在本文中, 我们详细介绍了这个框架是如何在 SPQR 中执行的, 并演示如何通过模拟和真实的数据实例在实践中使用这一组合。