Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data:
Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ... I have been able to model a case with only two alternatives "A" and "not A" by using glm(). I do not know what functions are available to estimate such a model with more than two alternatives. Multinom() is one possibility, but it only allows the use of binary 0/1-data instead of observed probabilities. Did I understand this correctly? Additionally, I am willing to use different independent variables for the different alternatives in the model. Formally, I mean that: Pr(A)=exp(uA)/(exp(uA)+exp(uB)+exp(uC)+exp(uD) Pr(B)=exp(uB)/(exp(uA)+exp(uB)+exp(uC)+exp(uD) ... where uA, uB, uC and uD are linear functions with different independent variables, e.g. uA=alpha_A1*Age, uB=alpha_B1*Sex. Do you know how to estimate this type of models in R? Best regards, Ville Koskinen ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
