Dear r-helpers, This might be an elementary question, but I have a hard time getting my head around it, so all help is much appreciated.
I am working on a nonlinear regression model of the form if z > 0 y = f1(x,y), else y = f2(x,t) . In other words, the functional form of f(.) changes according to some criteria z. Natural approach would be to fit two models, i.e. model1 <- nlm(y ~ ..., data=data[data$z > 0,]), model2 <- nlm(y ~ ..., data=data[data$z <= 0,]). But this is unfeasible since both models contain the same variable x and I would like to estimate the coefficient on x using both subsets of the data. My first shot would be to do something like this: formula <- ifelse(z > 0, "y ~ f(x,y).", "y ~ f(x,t)") nlm(formula, data=data) .. but preliminary testing indicates that this might not work. The coefficient on t does not get estimated. Thanks in advance for all helpful suggestions! With kind regards, Otto ---- Otto Kassi University of Helsinki Dept. of Economics ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
