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The MLEs of the model parameters are evaluated using the NLMixed procedure in SAS.
In most cases, a nonlinear mixed modelling approach was used with models fitted using the NLMIXED procedure in Version 9.3 of the SAS software (SAS Institute Inc. 2011).
The MLEs of the model parameters and the asymptotic standard errors of these estimates calculated using the NLMixed procedure in SAS are listed in Table 5.
The maximized log-likelihood can be either directly by using the NLMIXED procedure in SAS or the sub-routine MaxBFGS in the Ox program (see Doornik 2009) or by solving the nonlinear likelihood equations obtained by differentiating (31).
Table 3 lists the MLEs of the parameters for the fitted LGEW, LGW, LW, LBW and KwL regression models to the current data using the NLMixed procedure in SAS.
We constructed the non-linear mixed effects model by selecting the local and generalized equations that yielded the best fits for the species groups defined using the NLMIXED procedure in SAS/ETS (SAS Institute Inc 2008).
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We use the NLMixed procedure in SAS to obtain (widehat {{boldsymbol {theta }}}).
The computations were performed using the NLMixed subroutine in SAS.
We fitted the bivariate random effects models using the NLMIXED (non-linear mixed effect) procedure of SAS version 9.2 (SAS Institute, Cary, NC, USA).
Hierarchical logistic mixed-regression models were estimated using the PROC NLMIXED procedure.
The random-intercepts models, which we fitted using the 'nlmixed' procedure in SAS® version 9.1, accounted for the repeated measurement structure of the data.
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