Hi Koskinen For response variables with multiple categories, you may try polr() in MASS package, which implement a proportional odds model. And you may search the R archives, several threads discussed this problem before...
Wuming On 6/15/05, Ville Koskinen <[EMAIL PROTECTED]> wrote: > 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 > ______________________________________________ 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
