APCI：用于可视化和分析年龄-时期-队列数据的 R 和 Stata 软件包。

IF 2.3 4区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

R Journal Pub Date : 2022-06-01 Epub Date: 2022-10-10 DOI:10.32614/rj-2022-026

Jiahui Xu, Liying Luo

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引用次数: 0

摘要

社会科学家经常试图评估年龄、时期和队列变量对结果总体趋势的相对贡献。我们开发了一个 R 软件包 APCI（和 Stata 命令 apci），用于实现年龄-时期-队列-互动（APC-I）模型，以估计和检验集合横截面数据和多队列面板数据中各类结果的年龄、时期和队列模式。软件包 APCI 还提供了一组用于可视化数据和建模结果的函数。我们用当前人口调查的经验数据演示了软件包 APCI 的用法。我们表明，软件包 APCI 为了解各类结果的年龄、时期和队列趋势提供了有用的可视化和分析工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

APCI: An R and Stata Package for Visualizing and Analyzing Age-Period-Cohort Data.

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APCI: An R and Stata Package for Visualizing and Analyzing Age-Period-Cohort Data.

Social scientists have frequently attempted to assess the relative contribution of age, period, and cohort variables to the overall trend in an outcome. We develop an R package APCI (and Stata command apci) to implement the age-period-cohort-interaction (APC-I) model for estimating and testing age, period, and cohort patterns in various types of outcomes for pooled cross-sectional data and multi-cohort panel data. Package APCI also provides a set of functions for visualizing the data and modeling results. We demonstrate the usage of package APCI with empirical data from the Current Population Survey. We show that package APCI provides useful visualization and analytical tools for understanding age, period, and cohort trends in various types of outcomes.

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来源期刊

R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.