Fitting Linear Mixed-Effects Models Using lme4
Fitting Linear Mixed-Effects Models Using lme4
复制标题
DOI：
10.18637/jss.v067.i01
复制
发表时间：
2015-10-01
影响因子：
5.8
通讯作者：
Walker, Steven C.
中科院分区：
文献类型：
--
作者：
Bates, Douglas;Maechler, Martin;Walker, Steven C.
线性混合效应模型中参数的最大似然或限制最大似然（REML）估计值可以使用R的lme4包中的lmer函数确定。对于R中的大多数模型拟合函数，模型在lmer调用中由公式描述，在这种情况下包括固定和随机效应项。公式和数据一起确定模型的数值表示，根据该数值表示，可以根据一些模型参数来评估轮廓偏差或轮廓REML标准。使用R中的约束优化函数之一来优化适当的准则，以提供参数估计。我们描述了模型的结构，在评估的异形偏差或REML标准的步骤，和结构的类或类型，代表这样一个模型。包括足够的细节以允许希望编写函数以适合专门的线性混合模型的用户对这些结构进行专门化，例如包含谱系或平滑样条的模型，这些模型在lmer使用的公式语言中不容易表达。
Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.