Estimation in Forest Yield Models Using Composite Link Functions with Random Effects
Estimation in Forest Yield Models Using Composite Link Functions with Random Effects
复制标题
使用具有随机效应的复合链接函数估计森林产量模型
DOI：
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
S. Candy
中科院分区：
文献类型：
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作者：
S. Candy
S型森林产量模型是通过测量时间和间隔不同的样地的纵向测量来估计的。屈服模型的投影形式被拟合为具有复合连接函数和偏移量的广义线性模型(GLM)。这使得年龄和额外的协变量可以很容易地合并到比单链GLMS提供的更灵活的非线性结构中。此外，现有的用于拟合广义线性混合模型(GLMM)的算法很容易适应于拟合投影模型，从而允许并入随机曲线效应。描述了复合链路GLMM的估计，以及GLMM的一种替代方案，其中随机效应被合并为与响应具有相同尺度的线性分量。该模型称为加性广义线性混合模型(AGLMM)。与GLMM不同，AGLMM中的固定效应参数估计是总体平均(PA)。AGLMM是使用使用边际期望的特定于对象(SS)算法的修改来拟合的。AGLMM中的随机效应可以有自然的解释，或者可以提供产生边际协方差结构的手段，就像线性混合模型的情况一样。给出了二项数据和Logit链接的AGLMM的一个简单例子。在这个和森林产量的例子中，构造了一个单一的人工随机效应，以给出受试者内部观察的可交换相关性，并使用条件准偏差来比较AGLMM和GLMM的适合性。如果拟合度相似，并且需要PA参数估计，则AGLMM是首选。
A sigmoidal forest yield model is estimated from longitudinal measurements of sample plots where measurement times and intervals vary. The projection form of the yield model is fitted as a generalized linear model (GLM) with composite link functions and an offset. This allows age and additional covariates to be easily incorporated in a more flexible nonlinear structure than that provided by single link GLMs. Also, existing algorithms for fitting generalized linear mixed models (GLMMs) are easily adapted to fit the projection model, thereby allowing incorporation of random plot effects. Estimation for the composite link GLMM is described, as well as an alternative to the GLMM in which random effects are incorporated as linear components on the same scale as the response. This model is referred to as an additive generalized linear mixed model (AGLMM). Unlike the GLMM, the fixed-effect parameter estimates in the AGLMM are population-average (PA). The AGLMM is fitted using a modification of a subject-specific (SS) algorithm using marginal expectation. The random effects in the AGLMM may have a natural interpretation or may provide a means of generating a marginal covariance structure, as is the case with linear mixed models. A simple example of an AGLMM for binomial data and logit link is given. In both this and the forest yield example, a single, artificial random effect is constructed to give exchangeable correlation for observations within-subjects and a comparison of fit of the AGLMM and GLMM is made using a conditional quasi-deviance. If the fit is similar and PA parameter estimates are required, the AGLMM is preferred.
影响因子：
1.9
作者：
LINDSTROM, MJ;BATES, DM
通讯作者：
BATES, DM
影响因子：
1.9
作者：
ZEGER, SL;LIANG, KY;ALBERT, PS
通讯作者：
ALBERT, PS