Journal of Statistical Software最新文献

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Regularized Ordinal Regression and the ordinalNet R Package. 正则化正则回归和 ordinalNet R 软件包。

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-09-01 DOI: 10.18637/jss.v099.i06

Michael J Wurm, Paul J Rathouz, Bret M Hanlon

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal (ELMO) class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.

正则化技术，如 lasso（Tibshirani，1996 年）和 elastic net（Zou 和 Hastie，2005 年），可用于提高回归模型的系数估计和预测准确性，以及进行变量选择。正则回归模型在应用中被广泛使用，正则化的使用可能会带来益处；然而，许多流行的正则化回归软件包并不包含这些模型。我们提出了一种坐标下降算法，用于拟合一大类带有弹性网惩罚的序数回归模型。此外，我们还证明了该类模型中的每个模型都可以推广到一种更灵活的形式，既可以用于有序分类数据建模，也可以用于无序分类响应数据建模。我们将其称为元素链接多叉-序数（ELMO）类，它包括广泛使用的模型，如多叉逻辑回归（也有序数形式）和序数逻辑回归（也有无序多叉形式）。我们介绍了一种适用于任一模型形式的弹性净惩罚类，此外，这种惩罚还可用于将非顺序模型缩减为顺序模型。最后，我们介绍了 R 软件包 ordinalNet，它实现了该模型类的算法。

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引用次数: 0

Robust Analysis of Sample Selection Models through the R Package ssmrob 基于R Package的样本选择模型鲁棒性分析

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-08-21 DOI: 10.18637/jss.v099.i04

Mikhail Zhelonkin, E. Ronchetti

The aim of this paper is to describe the implementation and to provide a tutorial for the R package ssmrob, which is developed for robust estimation and inference in sample selection and endogenous treatment models. The sample selectivity issue occurs in practice in various fields, when a non-random sample of a population is observed, i.e., when observations are present according to some selection rule. It is well known that the classical estimators introduced by Heckman (1979) are very sensitive to small deviations from the distributional assumptions (typically the normality assumption on the error terms). Zhelonkin, Genton, and Ronchetti (2016) investigated the robustness properties of these estimators and proposed robust alternatives to the estimator and the corresponding test. We briefly discuss the robust approach and demonstrate its performance in practice by providing several empirical examples. The package can be used both to produce a complete robust statistical analysis of these models which complements the classical one and as a set of useful tools for exploratory data analysis. Specifically, robust estimators and standard errors of the coefficients of both the selection and the regression equations are provided together with a robust test of selectivity. The package therefore provides additional useful information to practitioners in different fields of applications by enhancing their statistical analysis of these models.

本文的目的是描述实现并为R包ssmrob提供教程，ssmrob是为样本选择和内生处理模型中的鲁棒估计和推理而开发的。样本选择性问题发生在各个领域的实践中，当观察到一个群体的非随机样本时，即当观察结果根据某些选择规则存在时。众所周知，Heckman(1979)引入的经典估计量对偏离分布假设(通常是误差项的正态性假设)的小偏差非常敏感。Zhelonkin, Genton和Ronchetti(2016)研究了这些估计器的鲁棒性，并提出了估计器和相应测试的鲁棒性替代方案。我们简要地讨论了鲁棒方法，并通过提供几个经验例子来证明其在实践中的性能。该软件包既可用于生成这些模型的完整稳健统计分析，补充了经典模型，也可作为探索性数据分析的一组有用工具。具体地说，给出了选择方程和回归方程系数的鲁棒估计和标准误差，以及对选择性的鲁棒检验。因此，该包通过加强对这些模型的统计分析，为不同应用领域的从业者提供了额外的有用信息。

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引用次数: 1

Optimal Design Generation and Power Evaluation in R: The skpr Package R中的最优设计生成与功率评估:skpr封装

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-08-18 DOI: 10.18637/jss.v099.i01

T. Morgan-Wall, George C. Khoury

The R package skpr provides a suite of functions to generate and evaluate experimental designs. Package skpr generates D, I, Alias, A, E, T, and G-optimal designs, and supports custom user-defined optimality criteria, N-level split-plot designs, mixture designs, and design augmentation. Also included are a collection of analytic and Monte Carlo power evaluation functions for normal, non-normal, random effects, and survival models, as well as tools to plot fraction of design space plots and correlation maps. Additionally, skpr includes a flexible framework for the user to perform custom power analyses with external libraries and user-defined functions, as well as a graphical user interface that wraps most of the functionality of the package in a point-and-click web application.

R包skpr提供了一套功能来生成和评估实验设计。skpr包生成D, I, Alias, A, E, T和g最优设计，并支持自定义用户定义的最优性标准，n级分割图设计，混合设计和设计增强。还包括用于正态、非正态、随机效应和生存模型的分析和蒙特卡罗功率评估函数的集合，以及用于绘制设计空间图和相关图的工具。此外，skpr还包括一个灵活的框架，供用户使用外部库和用户定义的函数执行自定义功率分析，以及一个图形用户界面，该界面将软件包的大部分功能封装在一个点击式web应用程序中。

引用次数: 5

cold: An R Package for the Analysis of Count Longitudinal Data 用于统计纵向数据分析的R包

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-08-18 DOI: 10.18637/jss.v099.i03

M. H. Gonçalves, M. S. Cabral

This paper describes the R package cold for the analysis of count longitudinal data. In this package marginal and random effects models are considered. In both cases estimation is via maximization of the exact likelihood and serial dependence among observations is assumed to be of Markovian type and referred as the integer-valued autoregressive of order one process. For random effects models adaptive Gaussian quadrature and Monte Carlo methods are used to compute integrals whose dimension depends on the structure of random effects. cold is written partly in R language, partly in Fortran 77, interfaced through R and is built following the S4 formulation of R methods.

本文介绍了R包冷对计数纵向数据的分析。在这个包考虑了边际效应和随机效应模型。在这两种情况下，估计都是通过精确似然的最大化和观测之间的序列依赖被假设为马尔可夫类型，并被称为一阶整值自回归过程。对于随机效应模型，采用自适应高斯正交和蒙特卡罗方法计算其维数取决于随机效应结构的积分。cold部分用R语言编写，部分用Fortran 77编写，通过R进行接口，并按照R方法的S4公式构建。

引用次数: 1

Dimension Reduction for Time Series in a Blind Source Separation Context Using R 基于R的盲源分离环境下时间序列降维

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-07-12 DOI: 10.18637/jss.v098.i15

K. Nordhausen, M. Matilainen, J. Miettinen, Joni Virta, S. Taskinen

Multivariate time series observations are increasingly common in multiple fields of science but the complex dependencies of such data often translate into intractable models with large number of parameters. An alternative is given by first reducing the dimension of the series and then modelling the resulting uncorrelated signals univariately, avoiding the need for any covariance parameters. A popular and effective framework for this is blind source separation. In this paper we review the dimension reduction tools for time series available in the R package tsBSS. These include methods for estimating the signal dimension of second-order stationary time series, dimension reduction techniques for stochastic volatility models and supervised dimension reduction tools for time series regression. Several examples are provided to illustrate the functionality of the package.

多变量时间序列观测在多个科学领域越来越普遍，但这些数据的复杂依赖关系往往转化为具有大量参数的棘手模型。另一种方法是首先降低序列的维数，然后对产生的不相关信号进行单变量建模，避免需要任何协方差参数。一个流行且有效的框架是盲源分离。本文综述了R包tsBSS中可用的时间序列降维工具。这些方法包括估计二阶平稳时间序列的信号维数的方法，随机波动模型的降维技术和时间序列回归的监督降维工具。提供了几个示例来说明该包的功能。

引用次数: 5

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data. SeqNet：用于生成基因-基因网络和模拟 RNA-Seq 数据的 R 软件包。

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-07-01 Epub Date: 2021-07-10 DOI: 10.18637/jss.v098.i12

Tyler Grimes, Somnath Datta

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

基因表达数据为推断基因调控网络的连接提供了丰富的资源。虽然为这项任务开发的方法已经取得了成功，但在比较各种方法的性能方面仍然存在挑战。黄金标准数据集非常稀缺，而且使用有限。虽然有模拟表达数据的工具，但它们的设计并不类似于从 RNA-seq 实验中获得的数据。SeqNet 是一个 R 软件包，提供了生成各种丰富的基因网络结构并从中模拟 RNA-seq 数据的工具。它生成的硅 RNA-seq 数据可用于基准测试和评估基因网络推断方法。该软件包可在 CRAN 和 GitHub 上获取：https://github.com/tgrimes/SeqNet。

引用次数: 0

The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R 采用者包:R临床试验的适应性优化设计

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-06-28 DOI: 10.18637/jss.v098.i09

K. Kunzmann, Maximilian Pilz, Carolin Herrmann, G. Rauch, M. Kieser

Even though adaptive two-stage designs with unblinded interim analyses are becoming increasingly popular in clinical trial designs, there is a lack of statistical software to make their application more straightforward. The package adoptr fills this gap for the common case of two-stage one- or two-arm trials with (approximately) normally distributed outcomes. In contrast to previous approaches, adoptr optimizes the entire design upfront which allows maximal efficiency. To facilitate experimentation with different objective functions, adoptr supports a flexible way of specifying both (composite) objective scores and (conditional) constraints by the user. Special emphasis was put on providing measures to aid practitioners with the validation process of the package.

尽管采用非盲法中期分析的自适应两阶段设计在临床试验设计中越来越流行，但缺乏统计软件使其应用更直接。对于(近似)正态分布结果的两阶段单组或双组试验的常见情况，一揽子采用者填补了这一空白。与以前的方法相比，adoptr预先优化了整个设计，从而实现了最大的效率。为了方便不同目标函数的实验，adoptr支持一种灵活的方式来指定(复合)目标分数和(条件)约束。特别强调的是提供措施来帮助实践者进行包的验证过程。

引用次数: 5

ergm 4: New Features for Analyzing Exponential-Family Random Graph Models ergm 4:分析指数族随机图模型的新特征

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-06-09 DOI: 10.18637/jss.v105.i06

P. Krivitsky, David R. Hunter, M. Morris, Chad Klumb

The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the new functionality in the 2021 release of ergm version 4. These include more flexible handling of nodal covariates, term operators that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergm's functionality to other network data types and structural features and the robust set of online resources that support the statnet development process and applications.

ergm包支持网络数据的统计分析和仿真。它锚定了用于网络分析的statnet套件，该套件在2008年统计软件杂志的特刊中介绍过。本文概述了2021年发布的ergm version 4中的新功能。其中包括更灵活地处理节点协变量、扩展和简化模型规范的术语算子、具有值边的网络新模型、改进的网络样本空间约束处理以及缺失边数据的估计。我们还确定了statnet套件中的新包，这些包将ergm的功能扩展到其他网络数据类型和结构特征，以及支持statnet开发过程和应用程序的强大在线资源集。

引用次数: 13

mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect 具有随机效应的总体死亡率和超额死亡率的灵活的基于风险的回归模型拟合

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-06-01 DOI: 10.18637/jss.v098.i14

H. Charvat, A. Belot

We present mexhaz, an R package for fitting flexible hazard-based regression models with the possibility to add time-dependent effects of covariates and to account for a two level hierarchical structure in the data through the inclusion of a normally distributed random intercept (i.e., a log-normally distributed shared frailty). Moreover, mexhaz based models can be fitted within the excess hazard setting by allowing the specification of an expected hazard in the model. These models are of common use in the context of the analysis of population-based cancer registry data. Follow-up time can be entered in the right-censored or counting process input style, the latter allowing models with delayed entries. The logarithm of the baseline hazard can be flexibly modeled with B-splines or restricted cubic splines of time. Parameters estimation is based on likelihood maximization: in deriving the contribution of each observation to the cluster-specific conditional likelihood, Gauss-Legendre quadrature is used to calculate the cumulative hazard; the cluster-specific marginal likelihoods are then obtained by integrating over the random effects distribution, using adaptive Gauss-Hermite quadrature. Functions to compute and plot the predicted (excess) hazard and (net) survival (possibly with cluster-specific predictions in the case of random effect models) are provided. We illustrate the use of the different options of the mexhaz package and compare the results obtained with those of other available R packages.

我们提出了mexhaz，这是一个R包，用于拟合灵活的基于风险的回归模型，可以添加协变量的时间依赖效应，并通过包含正态分布的随机截距(即对数正态分布的共享脆弱性)来解释数据中的两级层次结构。此外，通过允许在模型中指定预期危险，基于mexhaz的模型可以在过量危险设置内进行拟合。这些模型在分析基于人群的癌症登记数据的背景下是常用的。后续时间可以以右审查或计数过程输入样式输入，后者允许具有延迟输入的模型。基线危害的对数可以灵活地用b样条或时间的受限三次样条进行建模。参数估计基于似然最大化:在推导每个观测值对集群特定条件似然的贡献时，使用高斯-勒让德正交来计算累积风险;然后利用自适应高斯-埃尔米特正交法对随机效应分布进行积分，得到集群特有的边际似然。提供了计算和绘制预测(超额)风险和(净)生存(在随机效应模型的情况下可能具有特定于集群的预测)的函数。我们举例说明了mexhaz包的不同选项的使用，并将获得的结果与其他可用的R包的结果进行了比较。

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引用次数: 10

Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG 基于R包BiDAG的贝叶斯网络结构学习与采样

IF 5.8 2区计算机科学 Q1 Mathematics

Journal of Statistical Software

Pub Date : 2021-05-02 DOI: 10.18637/jss.v105.i09

Polina Suter, Jack Kuipers, G. Moffa, N. Beerenwinkel

The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize within the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks.

R包BiDAG实现了用于贝叶斯网络结构学习和采样的马尔可夫链蒙特卡罗(MCMC)方法。该软件包包括搜索最大后验(MAP)图和从给定数据的后验分布中采样图的工具。一种新的混合结构学习方法可以在大型图中进行推理。在第一步中，我们使用PC算法或基于先验知识定义一个约简搜索空间。第二步，迭代阶MCMC方案在有限的搜索空间内进行优化，并估计MAP图。从后验分布中抽样是使用顺序或分区MCMC实现的。该模型和算法既可以处理离散数据，也可以处理连续数据。BiDAG包还提供了用于动态贝叶斯网络结构学习和采样的MCMC方案的实现。

引用次数: 25

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