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Modeling Branch Length of Larch Tree Using Linear Mixed-Effects Models

机译:使用线性混合效应模型建模分支长度落叶松树

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In this study, the sample data was based on 2190 branch length samples of 30 trees from dahurian larch (Larix gmelinii Rupr.) plantations located in Wuying forest bureau in Heilongjiang Province. A second order polynomial equation with linear mixed-effects was used for modeling branch length of larch tree. The LME procedure in S-Plus is used to fit the mixed-effects models for the branch length data. The results showed that the polynomial model with three random parameters could significantly improve the model performance. The fitted mixed effects model was also evaluated using mean error, mean absolute error, mean percent error, and mean absolute percent error. The mixed model was found to predict branch length better than the original model fitted using ordinary least squares based on all errors. The application of mixed branch length model not only showed the mean trends of branch length, but also showed the individual difference based on variance-covariance structure.
机译:在这项研究中,样本数据基于来自Dahurian Larch(Larix Gmelinii Rupr)的30棵树的2190分支长度样本。位于黑龙江省的武州林局的种植园。具有线性混合效应的二阶多项式方程用于落叶松树的建模分支长度。 S-Plus中的LME程序用于适合分支长度数据的混合效果模型。结果表明,具有三个随机参数的多项式模型可以显着提高模型性能。使用平均误差,平均绝对误差,平均误差和平均百分比误差也评估拟合的混合效果模型。发现混合模型以基于所有误差的使用普通最小二乘拟合的原始模型更好地预测分支长度。混合分支长度模型的应用不仅显示了分支长度的平均趋势,而且还显示了基于方差协方差结构的个体差异。

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