A nonlinear mixed-effects model approach was used to model dominant height and site index for Eucalyptus globulus Labill. plantations in southeastern Australia. Mixed effects were considered initially for all three parameters of a modified Chapman–Richards model. Inclusion of random effects improved fitting and accounted for the within-plot heteroscedasticity. To correct for within-plot autocorrelation, a power autocorrelation model allowing for irregular intervals for remeasurements was found to be most appropriate. Additional fertilizer application at age 1 year and a number of environmental variables were related to the fixed-effects parameters, but these were not statistically significant, whereas mean annual rainfall and average daily maximum temperature in July (winter) greatly reduced the residual variability among plots. The resulting nonlinear mixed-effects model combines dominant height and site index prediction into a single model and predicts polymorphic height growth rates on different sites. The model can be used to predict population-mean dominant heights and site indices for different growing conditions of E. globulus plantations using existing information of annual rainfall and daily maximum temperature. When prior measures of dominant heights at several ages are available for a plot, specific random effects can be estimated and localized predictions of dominant height or site index can be obtained.
A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption
