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Height-diameter Models of Chinese Fir (Cunninghamia lanceolata) Based on Nonlinear Mixed Effects Models in Southeast China

机译:基于非线性混合效应模型的杉木杉木高径模型

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Tree height and diameter at breast height are two important forest factors. The best model from 23 height-diameter equations was selected as the basic model to fit the height-diameter relationships of Chinese fir with one level (sites or plots effects) and nested two levels (nested effects of sites and plots) Nonlinear Mixed Effects (NLME) models. The best model was chosen by smaller Bias, RMSE and larger Radj2. Then the best random-effects combinations for the NLME models were determined by AIC, BIC and -2LL. The results showed that the basic model with three random effects parameters &Phi &Phi &Phi1 &Phi2 and &Phi3 was considered the best mixed model. The nested two levels NLME model considering heteroscedasticity structure (power function) possessed with higher predictable accuracy and significantly improved model performance (LRT = 469.43, p<0.0001). The NLME model would be allowed for estimating accuracy the height-diameter relationships of Chinese fir and provided better height predictions than the models using only fixed-effects parameters.
机译:树高和胸高直径是两个重要的森林因素。从23个高度-直径方程中选择最佳模型作为基本模型,以将杉木的高度-直径关系拟合为一个级别(站点或情节效应)和嵌套两个级别(站点和情节的嵌套效应)非线性混合效应( NLME)模型。较小的Bias,RMSE和较大的 Radj 2 选择了最佳模型。然后通过AIC,BIC和-2LL确定NLME模型的最佳随机效应组合。结果表明,具有三个随机效应参数&Phi&Phi&Phi 1 &Phi 2 和&Phi 3 的基本模型被认为是最佳的混合模型。考虑异方差结构(幂函数)的嵌套两级NLME模型具有更高的可预测精度和显着改善的模型性能(LRT = 469.43,p <0.0001)。与仅使用固定效果参数的模型相比,可以使用NLME模型估计杉木的高度-直径关系的准确性,并提供更好的高度预测。

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