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

Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

113
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
113
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

293
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:
293
Interpreting R Charts01:22

Interpreting R Charts

49
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...
49

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

Causal effect heterogeneity estimation using summary statistics.

Research square·2026
Same author

Challenges to case-only analysis for interaction detection using polygenic risk scores: model assumptions and biases in large biobanks.

Genetics·2026
Same author

Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024.

bioRxiv : the preprint server for biology·2025
Same author

Polygenic prediction of treatment efficacy with causal transfer learning.

medRxiv : the preprint server for health sciences·2025
Same author

What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research.

Statistics in medicine·2025
Same author

PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.

Nature human behaviour·2025

相关实验视频

Updated: May 29, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

ipd:用于对预测数据进行推断的R包.

Stephen Salerno1, Jiacheng Miao2, Awan Afiaz1,3

  • 1Public Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.

Bioinformatics (Oxford, England)
|February 3, 2025
PubMed
概括

介绍 ipd,一个用于下游建模的 R 包,使用归算数据. 它简化了使用AI/ML对预测数据的推断,为模型检查和分析提供了用户友好的功能.

更多相关视频

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

97
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

3.3K

相关实验视频

Last Updated: May 29, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

97
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

3.3K

科学领域:

  • 统计软件 统计软件
  • 机器学习应用 机器学习应用
  • 数据科学数据科学数据科学

背景情况:

  • ipd包是一个开源的R软件,用于下游建模.
  • 它解决了人工智能/ML算法归因的结果数据处理方面的挑战.
  • 该包可在CRAN和GitHub上获得,并附有全面的文档.

研究的目的:

  • 引入用于统计建模的 ipd R 包.
  • 提供一个用户友好的工具来推断数据的AI/ML输入结果.
  • 为了展示 ipd 软件包的基本使用和功能.

主要方法:

  • ipd包实现了对预测数据的推理的最新方法.
  • 它提供了一个单一的,用户友好的封装功能,名为"ipd".
  • 定制方法 (打印,摘要,整洁,一眼,增强) 包含在模型检查中.

主要成果:

  • ipd 软件包可使用归算结果数据进行下游建模.
  • 它可以直接推断AI/ML预测的数据.
  • 该包通过自定义S3方法简化了模型检查.

结论:

  • ipd 是一个有价值的开源R包,用于研究人员和数据科学家.
  • 它增强了使用归算数据进行可靠统计建模的能力.
  • 该方案促进可重现的研究和高效的数据分析.