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To obtain scaled NLMM estimates in practice, it may be preferable to model the mediation by fitting separate equations, one for each pathway, using maximum likelihood rather than weighted least squares (the only estimation method currently available for probit link models in MPlus).
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We have fit separate equations for the number of new players, NP = ax−1.303; and the number of returning players, RP = (a/5x)−0.764.
These have generally been accommodated by fitting separate regional models rather than by incorporating environmental variables directly into the models.
If instead of fitting separate models we jointly estimate β j s using the generalized estimating equations (GEE) approach described by Sanchez et al. [ 6], it is possible to circumvent some of these concerns as well as test the differences in associations across visits (i.e., H0 : β1 = β2 = … = β n ).
This would simplify estimation by allowing the analyst to fit separate regression models for E(Z|A, Y, C) and Pr Y|A, C), say, standard logistic regression models if Z and Y are both binary, which are ensured not to conflict with a model for E(Z|A = 1, C) obtained using equation 11.
Results of the analysis were summarized by fitting Structural Equation Modelling (SEM).
Parameters of Equation 1 were estimated by fitting Equation 1 to the data of each individual animal, in each epoch, using the method of least squares.
Flow dependency of the pressure drop was determined by fitting Rohrer's equation (Δ P = K1*V' + K2*V').
Response curves parameters for all activators and repressors were calculated by fitting to their respective Hill equations (equations 1 and 2).
By fitting these data with equation (1) (Methods), we obtained the Ki of phlorizin for E457Q-hSGLT3.
Similar results were obtained by fitting a nonlinear regression equation (Sved 1971).
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