发表机构
Department of Mathematics and Statistics, University of Exeter（埃克塞特大学数学与统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍一个基于R Shiny的交互式Web应用，用于帮助非专家分析、比较和解释零膨胀计数数据模型，提供模型拟合、诊断和可视化功能，并缩小高级统计方法与实际应用之间的差距。
AI 中文摘要
在生态和环境研究中，计数数据通常包含大量零值，这些零值可能源于物种的真实缺失、不完全检测或某些事件的稀有性。对于非专家而言，由于模型的假设和诊断要求，选择和解释合适的统计模型（如泊松、负二项、零膨胀或障碍模型）可能很困难。为了解决这一问题，我们介绍了一个使用Shiny框架在R中构建的交互式Web应用程序，帮助用户分析、比较和解释可能表现出零膨胀的计数数据模型。该应用程序允许用户上传数据集，探索变量之间的分布和关系，并拟合多种模型，包括泊松、负二项、零膨胀和障碍方法。它整合了用于模型比较的信息准则工具、系数估计和置信区间的可视化、变量重要性的评估以及基于模拟残差的诊断检查。重要的是，该应用程序提供了计数生成过程和零生成过程的清晰可视化，使得传达通常难以解释的组成部分变得更加容易。为了提高可访问性，该工具提供了对输出和诊断的引导式解释，使其对研究应用和教学都有价值。一项使用环境计数数据的案例研究说明了该应用程序如何帮助用户识别零膨胀、选择合适的模型，并深入了解数据背后的生态过程。总体而言，该应用程序缩小了高级统计方法与实际分析之间的差距，支持可重复的工作流程，并增强了零膨胀计数数据模型的清晰度和可解释性。
英文摘要
Count data often contain a large number of zeros in ecological and environmental research, arising from factors such as true species absence, imperfect detection, or the rarity of certain events. Choosing and interpreting suitable statistical models, like Poisson, Negative Binomial, zero-inflated, or hurdle models, can be difficult for non-experts because of the models' assumptions and diagnostic requirements. To address this, we introduce an interactive web application built in R with the Shiny framework that helps users analyse, compare, and interpret count-data models that may exhibit zero inflation. The application enables users to upload datasets, explore distributions and relationships among variables, and fit a variety of models including Poisson, Negative Binomial, zero-inflated, and hurdle approaches. It incorporates tools for model comparison using information criteria, visualisation of coefficient estimates and confidence intervals, assessment of variable importance, and diagnostic checks based on simulated residuals. Importantly, the app provides clear visualisations of both the count-generating and zero-generating processes, making it easier to communicate components that are typically challenging to interpret. To improve accessibility, the tool offers guided explanations of outputs and diagnostics, making it valuable for both research applications and teaching. A case study using environmental count data illustrates how the application helps users identify zero inflation, choose appropriate models, and gain insight into the ecological processes underlying the data. Overall, the application narrows the gap between advanced statistical methods and practical analysis, supporting reproducible workflows and enhancing the clarity and interpretability of models for zero-inflated count data.
Comments29 pages (including references), 17 figures
