基于线性混合模型的杉木人工林枝条大小预测模型  

Analysis of the branch size for Chinese fir plantation using the linear mixed effects model

作　　者：许昊[1] 孙玉军[1] 王新杰[1] 高志雄[1] 董云飞[1] 

机构地区：[1]北京林业大学林学院,北京100083

出　　处：《南京林业大学学报（自然科学版）》2015年第2期97-103,共7页Journal of Nanjing Forestry University：Natural Sciences Edition

基　　金：国家林业局重点项目(2012-07);国家林业公益性行业科研专项项目(200904003-1);林业科技成果国家级推广项目([2014]26)

摘　　要：基于福建省将乐国有林场杉木人工林40株解析木的2 598组枝条解析数据,利用R语言的lme功能,采用线性混合效应(LME)模型方法,以单株树木作为随机效应,建立杉木人工林枝条大小(基径和长度)的预测模型,并利用独立样本数据对模型进行检验。结果表明:考虑随机效应的枝条大小LME预测模型比传统多元线性回归模型的拟合精度高;不同随机效应参数的组合,其LME模型的精度不同,3个参数作为随机效应参数时模型精度最高,但超过3个参数时模型不收敛;考虑异方差结构的LME模型能够消除数据间的异方差性,其精度更高,其中,以幂函数作为异方差结构时的模型精度最高。模型检验结果表明:对于杉木人工林枝条大小的预测,线性混合模型的检验精度比传统多元线性回归模型的精度有明显提高。Based on the branch analysis data of 2 598 branches from 40 sample trees of Chinese fir plantation in Jiangle National Forest Farm in Fujian Province. The linear mixed effects（LME） models was used build primary branch size（diameter and length） LME models with the trees effect as the random effect by R language. The results showed that the LME models provide better performance than that of the traditional multiple linear regression models for the branch size prediction of Chinese fir plantation. The LME models with different combinations of the random effects parameters had different fitting precisions,among which the LME model with three random effects parameters provides the best performance. However,the LME models with more than three random effects parameters showed no convergence. The LME models including variance structures could effectively remove the heteroscedasticity in the data,among which the LME model with the power function as the variance structure has better fitting precisions. Model validation confirms that the LME models with the random effect and heteroscedasticity structure could significantly improve the precision of prediction,compared with the traditional regression models.

关 键 词：杉木 枝条基径 枝条长度 线性混合模型 异方差 

分 类 号：S758[农业科学—森林经理学]