Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Introduction to R01:11

Introduction to R

597
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
597
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

789
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
789
Interpreting R Charts01:22

Interpreting R Charts

116
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
116
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.4K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.4K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

536
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
536
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

398
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
398

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Diabetes Mortality in the Post-Pandemic Era: What Recent Global Burden of Disease Data Reveals About COVID-19's Lasting Impact.

Epidemiologia (Basel, Switzerland)·2026
Same author

Counting everyone onboard is not enough: modelling lessons from the MV Hondius Andes virus outbreak.

Journal of travel medicine·2026
Same author

A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl.

Bulletin of mathematical biology·2026
Same author

A comparative study of simulation-based inference methods for epidemic models with identifiability considerations.

PLoS computational biology·2026
Same author

Patterns of Sexual Behaviors and Sexual Partner Characteristics as Predictors of Perceived HIV Risk and HIV Status Among Adolescent Girls and Young Women in Kenya.

AIDS and behavior·2026
Same author

Comparing Bayesian and frequentist inference in biological models: A comparative analysis of accuracy, uncertainty, and identifiability.

Mathematical biosciences and engineering : MBE·2026
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
查看所有相关文章

相关实验视频

Updated: Sep 12, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

673

统计模型预测 (StatModPredict):一个用户友好的R-Shiny界面,用于与统计模型相配并进行预测.

Amanda Bleichrodt1, Amelia Phan1, Ruiyan Luo1

  • 1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, United States of America.

PloS one
|August 7, 2025
PubMed
概括
此摘要是机器生成的。

StatModPredict是一个R-Shiny仪表板,可以在没有编程的情况下实现高级统计时间序列预测. 它使学生和专业人士能够预测流行病轨迹,并有效地分析时间序列数据.

更多相关视频

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

377
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Sep 12, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

673
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

377
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 流行病学 流行病学
  • 公共卫生 公共卫生
  • 生物统计学 生物统计学

背景情况:

  • 统计时间序列模型对于公共卫生预测至关重要,但需要广泛的编程知识,限制了可访问性.
  • 学生,专业人士和政策制定者往往缺乏使用这些强大的预测工具的编程技能.

研究的目的:

  • 为了介绍StatModPredict,一个R-Shiny仪表板,旨在提供可访问和直观的预测分析.
  • 为了使具有有限编程经验的用户能够使用各种统计模型进行可靠的预测.

主要方法:

  • StatModPredict集成了自动回归集成移动平均线 (ARIMA),通用线性模型 (GLM),通用添加模型 (GAM) 和Meta的先知模型.
  • 仪表板支持实时预测,回顾分析,模型拟合,评估,可视化和结果比较.
  • 用户可以自定义参数,上传外部预测进行比较,并通过可编辑图形分析时间序列数据.

主要成果:

  • R-Shiny仪表板,StatModPredict,成功地降低了时间序列预测的编程障碍.
  • 使用美国年度艾滋病毒病例数据的演示展示了仪表板对现实世界预测应用的实用性.
  • 该工具促进了各种用户群体的探索和使用,包括学生和公共卫生专业人员.

结论:

  • StatModPredict使对复杂的预测工具的访问民主化,促进实践学习和跨学科的更广泛应用.
  • 开源界面支持任何使用时间序列数据的领域,增强流行病轨迹预测和数据分析.
  • 通过消除技术障碍,StatModPredict促进了对预测方法的更广泛采用和潜在用户贡献.