Dear R community,
I am using the mlogit package to analyze discrete choice data. Apart
from a main effects model, I want to estimate interactions between the
attributes of the choice set (e.g. the existence of a certain attribute)
and some subject-specific data (like gender or income).
Studying the mlogit documentation, I found no hint on how to do it. In
the literature there is only the case discussed how alternative-specific
variables can be combined. In my case, the alternatives are of no
interest, meaning I am using a purely generic model.
So far, I tried to model these interactions by simple multiplying the
variables.
Example:
mlogit.model <- mlogit(CHOICE ~ ATR1+ATR2+ATR3 + ATR1*GENDER +
ATR1*GENDER + ATR1*GENDER| -1, data=data_ml)
Here, gender is subject-specific.
I get results like the following:
---
Coefficients :
Estimate
ATR1_yes 0.779116
ATR2_ yes 2.257905
ATR3_ yes 1.141625
GENDERfem -14.026649
ATR1_yes :GENDERfem 0.094709
ATR2_ yes:GENDERfem -0.076223
ATR3_ yes:GENDERfem 0.117373
---
I present only the coefficients here. However, when I change the
reference level to male, the coefficients of the interactions effects
just change sign.
I have two questions in this regard:
1.) Is the modeling of such interactions effect feasible in the mlogit
setting?
2.) I have some problem understanding the changing sign of the
coefficients when I change the reference level. This would imply that
females always prefer the opposite of males. Clearly, this cannot be. I
imagine that I am misinterpreting this issue and I would be grateful for
any help on this.
Best regards,
Marcel
--
Marcel Gerds, M.Sc.
University of California
Department of Agricultural and Resource Economics
233 Giannini Hall
Berkeley, CA 94720
Tel.: +1 510-643-2202
Mobil: +49 176 21302825
E-Mail: marcel.ge...@berkeley.edu
web: www.marcel-gerds.de
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