Thank you for your response @Ksharp and @PGStats . I know that you mean, but I would like to fit nonlinear models that same form which Littel (2006) show in your book with linear models. For example, for to fit heterogenous variance in linear models, he use an variance function for the following linear model: y = a + bx; with followling procedure: proc nlmixed data=LR;
parms a=4.5 b=7.5 c=.01 sig2=5;
mean = a+b*x;
model y ~ normal(mean,sig2*exp(c*x));
predict mean out=mean df=16;
run; The variance function is Var = σ²exp{xγ}; where σ² = sig² and γ = c. I would like to know if I could use this same procedure for nonlinear models. And what function of variance could I use in place of σ²exp{xγ}. REFERENCE Littell, Ramon C., George A. Milliken, Walter W. Stroup, Russell D. Wolfinger, and Oliver Schabenberger. 2006. SAS® for Mixed Models, Second Edition. Cary, NC: SAS Institute Inc.
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