Skip to main content

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

Buy Article:

$21.50 plus tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

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

Document Type: Research Article

Publication date: 2009-06-01

More about this publication?
  • 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.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
  • Submit a Paper
  • Membership Information
  • Author Guidelines
  • Podcasts
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more