Hedeker D, Gibbons R D
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago 60612-7260, USA.
Comput Methods Programs Biomed. 1996 May;49(3):229-52. doi: 10.1016/0169-2607(96)01723-3.
MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.
MIXREG是一个程序,用于为包含自相关误差的正态分布响应数据的混合效应回归模型(MRM)提供估计值。该模型可用于分析不平衡纵向数据,其中个体可能在不同数量的时间点进行测量,甚至在不同的时间点进行测量。允许一般形式的自相关误差或遵循AR(1)、MA(1)或ARMA(1,1)形式的自相关误差。该模型还可用于分析聚类数据,其中混合效应模型假定聚类内的数据是相关的。相关性程度与通常模型参数的估计值一起进行估计,从而对聚类进行调整。MIXREG使用最大边际似然估计,同时利用期望最大化(EM)算法和费舍尔评分解。对于评分解,随机效应的协方差矩阵以其高斯分解形式表示,对角矩阵使用指数变换重新参数化。个体随机效应的估计使用经验贝叶斯方法完成。文中提供了说明MIXREG用法和特征的示例。