Study of the Dominant Height for Chinese Fir Plantation Using Two-Level Nonlinear Mixed Effects Model

FU Li-yong; LI Yong-ci; LI Chun-ming; TANG Shou-zheng

Nonlinear mixed effects model (NLMEM) is built on the relationship of the fixed and random effects in the regression function. The NLMEM has an obvious comparative advantage in analyzing the longitudinal data, repeated measures data and multilevel data. Two-level NLMEM is used to analyze the dominant height for Chinese fir (Cunninghamia lanceolata). The authors outline the two-level NLMEM and introduce the parameters estimation method of the model. Based on five common Richard and Logistic models, the mixed model is built. The modeling data are used to calculate and compare with 19 models derived from each based model, and 5 optimal mixed models are built. Compared the 5 optimal mixed models with traditional regression models, it is showed that the two-level NLMEM has a better fitting effect than the regression models.

1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

2. College of Science, Beijing Forestry University, Beijing 100083, China

Received Date: 2011-03-18

Available Online: 2011-12-13

Abstract: Nonlinear mixed effects model (NLMEM) is built on the relationship of the fixed and random effects in the regression function. The NLMEM has an obvious comparative advantage in analyzing the longitudinal data, repeated measures data and multilevel data. Two-level NLMEM is used to analyze the dominant height for Chinese fir (Cunninghamia lanceolata). The authors outline the two-level NLMEM and introduce the parameters estimation method of the model. Based on five common Richard and Logistic models, the mixed model is built. The modeling data are used to calculate and compare with 19 models derived from each based model, and 5 optimal mixed models are built. Compared the 5 optimal mixed models with traditional regression models, it is showed that the two-level NLMEM has a better fitting effect than the regression models.

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Reference (18)

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Study of the Dominant Height for Chinese Fir Plantation Using Two-Level Nonlinear Mixed Effects Model

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