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Abstract
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Article Information:
Algorithm for Tree Growth Modeling Based on Random Parameters and ARMA
Lichun Jiang, Fengri Li and Yaoxiang Li
Corresponding Author: Yaoxiang Li
Submitted: December 20, 2012
Accepted: January 25, 2013
Published: August 05, 2013 |
Abstract:
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Chapman-Richards function is used to model growth data of dahurian larch (Larix gmelinii Rupr.) from longitudinal measurements using nonlinear mixed-effects modeling approach. The parameter variation in the model was divided into random effects, fixed effects and variance-covariance structure. The values for fixed effects parameters and the variance-covariance matrix of random effects were estimated using NLME function in S-plus software. Autocorrelation structure was considered for explaining the dependency among multiple measurements within the individuals. Information criterion statistics (AIC, BIC and Likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software. Results showed that the Chapman-Richards model with three random parameters could typically depict the dahurian larch tree growth in northeastern China. The mixed-effects model provided better performance and more precise estimations than the fixed-effects model.
Key words: Fixed effects, modeling algorithm, nonlinear mixed effects, random effects, tree growth, ,
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Cite this Reference:
Lichun Jiang, Fengri Li and Yaoxiang Li, . Algorithm for Tree Growth Modeling Based on Random Parameters and ARMA. Research Journal of Applied Sciences, Engineering and Technology, (13): 2443-2450.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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