CooccurrenceAffinity:一个R包,用于计算共发生数据中的新型亲和度量,以纠正传统指数中普遍存在的错误
Kumar P Mainali1,2, Eric Slud3,4
1Conservation Innovation Center, Chesapeake Conservancy, Earl Conservation Center, Annapolis, Maryland, United States of America.
PloS one
|January 17, 2025
概括
本研究介绍了CooccurrenceAffinity R包,用于分析共发生数据. 它提供了一个新的,可靠的指标 (alpha MLE) 和置信区间,改进了传统的有缺陷的指数.
科学领域:
- 生态生态学 生态生态学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 传统的同时发生指数有缺陷,显示出对流行率的敏感性和关联强度的模糊性.
- 之前的研究发现了共同指数的基本问题,并引入了一个新的关联参数alpha,其最大概率估计 (MLE).
研究的目的:
- 介绍 CooccurrenceAffinity R 包,用于计算α MLE.
- 提供工具来分析来自应急表和存在-缺席矩阵的同时发生的数据.
- 介绍新功能来计算和评估阿尔法指标的中位数和置信区间.
主要方法:
- 开发了执行alpha MLE计算的CooccurrenceAffinity R套件. 开发了应用alpha MLE计算的CooccurrenceAffinity R套件. 开发了应用alpha MLE计算的CooccurrenceAffinity R套件.
- 包括分析2x2应急表和m x n存在-缺席矩阵的功能.
- 实现用于计算和评估中位数和置信区间的函数,包括真实覆盖概率.
主要成果:
- 协同发生的Affinity包为协同发生的数据提供了用户友好的端到端分析.
- 它计算了alpha MLE,中位数间隔和相关联的置信间隔.
- 该包还提供传统指数 (Jaccard,Sørensen-Dice,Simpson) 进行比较.
结论:
- CooccurrenceAffinity提供了一个强大的和高效的解决方案,用于共同发生的数据分析.
- 该包通过提供可靠的新型指标来增强生态和生物关联研究.
- 它通过先进的统计方法来促进对物种或实体相互作用的更好理解.
相关概念视频
01:41Affinity and Avidity
35.8K
Overview
35.8K
01:20Kendall's Coefficient of Concordance
236
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
236
01:12Coefficient of Correlation
6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.0K
01:21Friedman Two-way Analysis of Variance by Ranks
144
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
144
01:09Wilcoxon Signed-Ranks Test for Matched Pairs
84
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
84
01:19Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K