robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models
robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models
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DOI：
10.18637/jss.v075.i06
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发表时间：
2016-12-01
影响因子：
5.8
通讯作者：
Koller, Manuel
中科院分区：
文献类型：
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作者：
Koller, Manuel
与任何现实生活中的数据一样，线性混合效应模型建模的数据通常包含离群值或其他污染。即使是很小的污染也会使经典的估计远离没有污染的情况。与此同时，需要混合效应建模的数据集通常复杂而庞大。这使得很难发现污染。鲁棒估计方法旨在解决这两个问题：提供污染影响很小的估计，并检测和标记污染。我们引入了R包，robustlmm，以鲁棒地拟合线性混合效应模型。该软件包的功能和方法与lme4提供的功能和方法非常接近，lme4是一个R软件包，在R中实现了经典的线性混合效应模型估计。鲁棒最小均方法中的抗差估计方法是基于随机效应污染模型和中心污染模型的。污染可以在所有级别的数据中检测到。除了模型参数是可估计的之外，估计方法不对数据的分组结构做出任何假设。RobustLMM支持分层和非分层（例如，交叉的）分组结构。估计的鲁棒性及其渐近效率通过函数接口完全控制。单个部件（例如，在本教程中，我们将展示如何使用robustlmm拟合稳健的线性混合效应模型，如何评估模型拟合，如何检测离群值，以及如何比较不同的拟合。
As any real-life data, data modeled by linear mixed-effects models often contain out-liers or other contamination. Even little contamination can drive the classic estimates far away from what they would be without the contamination. At the same time, datasets that require mixed-effects modeling are often complex and large. This makes it difficult to spot contamination. Robust estimation methods aim to solve both problems: to provide estimates where contamination has only little influence and to detect and flag contamination.We introduce an R package, robustlmm, to robustly fit linear mixed-effects models. The package's functions and methods are designed to closely equal those offered by lme4, the R package that implements classic linear mixed-effects model estimation in R. The robust estimation method in robustlmm is based on the random effects contamination model and the central contamination model. Contamination can be detected at all levels of the data. The estimation method does not make any assumption on the data's grouping structure except that the model parameters are estimable. robustlmm supports hierarchical and non-hierarchical (e.g., crossed) grouping structures. The robustness of the estimates and their asymptotic efficiency is fully controlled through the function interface. Individual parts (e.g., fixed effects and variance components) can be tuned independently.In this tutorial, we show how to fit robust linear mixed-effects models using robustlmm, how to assess the model fit, how to detect outliers, and how to compare different fits.