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Improved Calibration of Nonlinear Mixed-Effects Models Demonstrated on a Height Growth Function

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The calibration of a nonlinear mixed-effects model is of critical importance in making local predictions. In previous applications, simplified equations were generally used to calibrate nonlinear mixed-effects models fitted using the first-order methods implemented through the NLINMIX macro in SAS. This simplification, however, could distort local predictions. In this study, using a nonlinear height growth model of lodgepole pine (Pinus contorta var. latifolia Engelm.), we demonstrated on two data sets the procedures to obtain an improved calibration of the height growth model. The differences between the improved and conventional calibrations were found to be significant. Calibration of the nonlinear mixed models using the improved method resulted in not only reduced bias but also reduced variance of the errors. It is recommended that the improved calibration method be used. A computing program detailing the procedures for obtaining the improved calibration was developed.

Keywords: height growth function; local prediction; model calibration; nonlinear mixed-effects model

Document Type: Research Article

Publication date: June 1, 2009

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
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