Dear list, My question is about the localization (or calibration) of nonlinear mixed models. I have the estimates of the parameters of some hierarchical nonlinear mixed models that we?d like to use to predict new responses. I also have measurements that can be used to calibrate the models and my question is how to compute the conditional expected values of the random parameter given the new measurements. One way could be to find the maximum of p1(u)*p2(y|u) using nlm procedure, where p1(u) is the density of random parameters u and p2(y|u) is the conditional density of the response y. But can this be done using nlme? In nlme the models can be formulated in a familiar and handy way, which would be a great advantage. I understand that if I give nlme the estimates of the fixed mean and variance parameters as initial values and prevent nlme from updating the initial values, nlme computes the conditional expectations (and their variances) I need. If this is true, how can I give the initial values and prevent updating? Or is there a better way to do the job? Best regards Jaakko Heinonen
Localization of nonlinear mixed models
1 message · Jaakko Heinonen