# Re: [R] Logistic regression with more than two choices

From: Wuming Gong <wuming.gong_at_gmail.com>
Date: Wed 15 Jun 2005 - 10:57:55 EST

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 <ville.koskinen@matrex.fi> 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
>
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