文献类型：期刊文献

中文题名：基于非线性混合模型的杉木优势木平均高

英文题名：Dominant Height for Chinese Fir Plantation Using Nonlinear Mixed Effects Model Based on Linearization Algorithm

作者：符利勇[1] 张会儒[1] 唐守正[1]

第一作者：符利勇

机构：[1]中国林业科学研究院资源信息研究所

年份：2012

卷号：48

期号：7

起止页码：66-71

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;Scopus;北大核心:【北大核心2011】；CSCD:【CSCD2011_2012】；

基金：林业公益性行业重点项目"我国典型森林类型健康经营关键技术研究"(201004002)

语种：中文

中文关键词：非线性混合模型;一阶线性化算法;一阶条件期望线性化算法;杉木;优势木平均高

外文关键词：nonlinear mixed effect model; first-order linearization algorithm; first-order conditional expectation linearization algorithm; Chinese Fir （Cunninghamia lanceolata） ; dominant height

分类号：S758.1

摘要：从理论上介绍一阶线性化算法和一阶条件期望线性化算法求解非线性混合效应模型参数,并利用这2种算法分别对杉木优势木平均高进行拟合(选用常用的Logistic模型作为基础模型,把区组作为随机效应因子)。结果表明:2种算法对杉木优势木平均高进行拟合时精度都很高。通过对2种线性化算法进一步比较可得,在分析单木水平非线性混合效应优势木平均高模型时,2种算法拟合效果非常接近,因此在实际应用中可以选择其中任意一种算法对杉木优势木平均高进行拟合。
First-order linearization algorithm and first-order conditional expectation linearization algorithm were introduced theoretically to estimate the parameters in nonlinear mixed effects model. Through using the two algorithms to analysis the dominant height for Cunninghamia lancelata （In this paper the common Logistic model is chosen as a basic model） , it shows that both of the algorithms have high accuracy to fit the dominant height of Chinese Fir. Compared with the two linearization algorithms, we can see that the two algorithms have a close fitting effects for analyzing the single level nonlinear mixed effects dominate height model. Therefore, we can chose orbitrarily each of these algorithm to analyze the dominant height model for Cunninghamia lanceolata in practice.