rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models
rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models
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DOI：
10.1111/2041-210x.12797
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发表时间：
2017-11-01
影响因子：
6.6
通讯作者：
Schielzeth, Holger
中科院分区：
文献类型：
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作者：
Stoffel, Martin A.;Nakagawa, Shinichi;Schielzeth, Holger
1。类内相关性（ICC）和重复性（R）是量化测量值和理解生物变异结构的基本统计数据。线性混合效应模型为估计ICC和R提供了一种多功能框架。但是，虽然通过似然比测试进行的点估计和显着性测试很简单，但对不确定性的量化并不那么容易实现。2。当对具有非高斯分布的数据进行分析时，会出现进一步的并发症，因为对于非高斯模型而言，平均值和方差的分离不如非高斯模型。尽管如此，对于最广泛使用的广义线性混合模型（GLMMS）的家族的近似可重复性的解决方案。通过参数引导量来量化估计量的不确定性，并通过似然比测试和残留物的排列来实现显着性测试。该软件包允许控制固定效果，从而估算了调整后的重复性（从估计值中消除固定效应差异）和增强的一致性重复性（这为分母增加了固定效应方差）。此外，可以从随机斜率模型中估算可重复性。该软件包具有方便的摘要和绘图功能。4。Besides重复性，该软件包还允许量化确定系数R-2以及原始方差组件。我们提供了一个示例分析，以证明核心特征并讨论RPTR的一些局限性。
1. Intra-class correlations (ICC) and repeatabilities (R) are fundamental statistics for quantifying the reproducibility of measurements and for understanding the structure of biological variation. Linear mixed effects models offer a versatile framework for estimating ICC and R. However, while point estimation and significance testing by likelihood ratio tests is straightforward, the quantification of uncertainty is not as easily achieved.2. A further complication arises when the analysis is conducted on data with non-Gaussian distributions because the separation of the mean and the variance is less clear-cut for non-Gaussian than for Gaussian models. Nonetheless, there are solutions to approximate repeatability for the most widely used families of generalized linear mixed models (GLMMs).3.Here, we introduce the R package rptR for the estimation of ICC and R for Gaussian, binomial and Poisson-distributed data. Uncertainty in estimators is quantified by parametric bootstrapping and significance testing is implemented by likelihood ratio tests and through permutation of residuals. The package allows control for fixed effects and thus the estimation of adjusted repeatabilities (that remove fixed effect variance from the estimate) and enhanced agreement repeatabilities (that add fixed effect variance to the denominator). Furthermore, repeatability can be estimated from random-slope models. The package features convenient summary and plotting functions.4.Besides repeatabilities, the package also allows the quantification of coefficients of determination R-2 as well as of raw variance components. We present an example analysis to demonstrate the core features and discuss some of the limitations of rptR.