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

Abstract:

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.

Key words: nonlinear mixed effect model, first-order linearization algorithm, first-order conditional expectation linearization algorithm, Chinese Fir (Cunninghamia lanceolata), dominant height