AI 中文总结
研究利用lme4 R包拟合广义线性混合效应模型，通过惩罚迭代加权最小二乘法确定相关参数，借助积分近似计算最大似然估计，提供了基础R中GLMs的标准功能。
AI 中文摘要
lme4 R包可用于拟合广义线性混合模型（GLMMs），它扩展了线性混合模型（LMMs）。GLMMs的两个主要扩展是：允许给定随机效应时响应的条件分布为非高斯分布（如二项式、泊松分布）；通过逆链接函数使条件均值成为固定和随机效应系数线性组合的非线性函数。利用惩罚迭代加权最小二乘法确定给定观测数据时随机效应的条件模式、随机效应的方差协方差矩阵以及固定效应参数。通过对条件模式分布的积分近似来计算给定参数集的最大似然估计（默认使用拉普拉斯近似或计算成本更高的自适应高斯 - 埃尔米特求积法）。该包提供了基础R中GLMs的所有标准功能，包括标准的访问函数集以及用户指定分布（在指数族分布内）和链接函数的可能性。
英文摘要
The lme4 R package can be used to fit generalized linear mixed models (GLMMs), which extend the class of linear mixed models (LMMs). The two main extensions provided by GLMMs are (1) allowing for the conditional distribution of the response given the random effects to be non-Gaussian (e.g. binomial, Poisson) and (2) allowing the conditional mean to be a nonlinear function of a linear combination of the fixed and random effect coefficients, via an inverse link function. The conditional mode of the random effects given the observed data, the variance-covariance matrix of the random effects, and the fixed effect parameters are determined using penalized iteratively reweighted least squares. We compute an approximation of the integral over the distributions of the conditional modes to compute the maximum likelihood estimate for a given set of parameters (by default we use the Laplace approximation or, alternatively, the more computationally expensive adaptive Gauss-Hermite quadrature). The package provides all the standard features available for GLMs in base R, including the standard set of accessor functions as well as the possibility of user-specified distributions (within the exponential dispersion family) and link functions.
