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相关概念视频

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
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Correlation of Experimental Data01:23

Correlation of Experimental Data

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Scatter Plot01:15

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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相关实验视频

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多变量模式分析:一种方法和软件,用于揭示,量化和可视化多对线数据中的预测关联模式.

Tim U H Baumeister1, Eivind Aadland2, Roger G Linington1

  • 1Department of Chemistry, Simon Fraser University, Burnaby, Canada.

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概括

多变量模式分析 (MVPA) 提供了一个解决方案,用于分析复杂的代谢学数据与多变量共变量. 这个新的R包和闪亮的应用程序提高了模型的解释性和分辨率,而不会丢失数据.

关键词:
一个共变的投影.潜变量投影的潜变量投影.多对线共变量是多对线共变量.多变量模式分析多变量模式分析净关联模式 净关联模式目标预测的目标预测.

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科学领域:

  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.
  • 代谢学 代谢学 代谢学

背景情况:

  • 代谢学中的多线性共变量对传统的多变量回归提出了挑战.
  • 处理多对线性现有的处理方法往往会降低数据分辨率和模型可解释性.
  • 存在对能够有效分析复杂,高维数据而不损害分析质量的方法的需求.

研究的目的:

  • 实施和演示多变量模式分析 (MVPA) 以处理回归中的多对线共变量.
  • 为 MVPA 开发一个开源的 R 包和一个用户友好的 Shiny 应用程序.
  • 提高处理复杂多变量数据结构的模型的解释潜力.

主要方法:

  • MVPA采用了一种通用投影算法,旨在管理多线性和线性依赖的共变量.
  • 该方法将数据方差分为直角元件,区分共变量关系和净预测关联.
  • 一个三步过程涉及部分最小平方回归,蒙特卡罗重新抽样和目标投影用于协变量调整.

主要成果:

  • MVPA方法已经在一个开源的R包 (mvpa) 和一个集成的R Shiny应用程序 (mvpaShiny) 中实现.
  • MVPA有效地处理多对线共变量,保持数据分辨率,并通过方差图改善模型的解释性.
  • 该方法通过分析代谢学描述器对胰岛素抵抗和生活方式因素的调解来说明.

结论:

  • 开发的MVPA方法及其R包实现为复杂的多变量数据提供了先进的分析和可视化功能.
  • R软件包的开源可用性有助于在科学研究中更广泛地采用和应用.
  • 这项工作为研究人员提供了有价值的工具,他们使用高维和多线数据集,特别是在代谢学.