James C Thomas, Kyungsup Shin, Xian Jin Xie
{"title":"Principal Component Analysis in Dental Research.","authors":"James C Thomas, Kyungsup Shin, Xian Jin Xie","doi":"10.11607/jomi.10940","DOIUrl":null,"url":null,"abstract":"<p><p>Principal component analysis (PCA) is a statistical tool that condenses the information contained in a large group of independent variables to a more manageable number of variables. This is useful when performing an analysis on data sets with a large number of variables. PCA restructures the original independent variables into new variables called principal components that maximize the information present in the data. The principal components then act as a substitute for the independent variables in an analysis. The purpose of this article is to present PCA in an understandable way for researchers without advanced statistical and mathematical backgrounds. To solidify the comprehension of the process and provide a template for researchers, we present an extended step-by-step example of PCA in use on a fictitious peri-implantitis data set.</p>","PeriodicalId":94230,"journal":{"name":"The International journal of oral & maxillofacial implants","volume":"40 1","pages":"13-20"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The International journal of oral & maxillofacial implants","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11607/jomi.10940","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

主成分分析(PCA)是一种统计工具,可将一大组独立变量中包含的信息浓缩为更易于管理的变量数量。这在对包含大量变量的数据集进行分析时非常有用。PCA 将原始的独立变量重组为新的变量,这些新变量被称为主成分,能最大限度地反映数据中的信息。然后,主成分就可以在分析中替代自变量。本文旨在以易于理解的方式向没有高级统计和数学背景的研究人员介绍 PCA。为了加深对这一过程的理解,并为研究人员提供一个模板,我们以一个虚构的种植周炎数据集为例,详细介绍了 PCA 的使用步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principal Component Analysis in Dental Research.

Principal component analysis (PCA) is a statistical tool that condenses the information contained in a large group of independent variables to a more manageable number of variables. This is useful when performing an analysis on data sets with a large number of variables. PCA restructures the original independent variables into new variables called principal components that maximize the information present in the data. The principal components then act as a substitute for the independent variables in an analysis. The purpose of this article is to present PCA in an understandable way for researchers without advanced statistical and mathematical backgrounds. To solidify the comprehension of the process and provide a template for researchers, we present an extended step-by-step example of PCA in use on a fictitious peri-implantitis data set.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信