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I apologize for the "how do I run this model in R" question. I will be the first to admit that i am a newbie when it comes to statistical models. Hopefully I have enough substantive questions surrounding it to be interesting, and the question will come out more like, "Does this command in R correspond to this statistical model?"

I am trying to estimate a model that can estimate the probability of a given Twitter user "following" a political user from a given political party. My dataframe is at the level of individual users, where each user can choose to follow or not follow a party on Twitter. As alternative-specific variables i have measures of ideological distance from the Twitter user and the political party and an interaction term that specifies whether the distance is positive or negative. Thus, the decision to follow a politician on twitter is a function of your ideological distance.

Initially i tried to estimate a conditional logit model, but i quickly got away from that idea since the choices are not mutually exclusive i.e. they can choose to follow more than one party. Now i am in doubt whether i should employ a multinomial probit or a multivariate probit, since i want my model to allow indviduals to choose more than one alternative. However, when i try to estimate a multinomial probit, my code doesn't work. My code is:

mprobit <- mlogit(Follow ~ F1_Distance+F2_Distance+F1_Distance*F1_interaction+F2_Distance*F2_interaction+strata(id), 
           long, probit = T, seed = 123)

And i get the following error message:

Error in dfidx::dfidx(data = data, dfa$idx, drop.index = dfa$drop.index,  : 
  the two indexes don't define unique observations

I've tried looking the error up, but i can't seem to find anything that relates to probit models. Can you tell me what i'm doing wrong? Once again, sorry for my ignorance. Thank you for your help.

Also, i've tried copying my dataframe in the code below. The data is for the first 6 observations for the first Twitter user, but i have a dataset of 5181 users, which corresponds to 51810 observations, since there's 10 parties in Denmark.

 id    Alternative  Follow F1_Distance  F2_Distance  F1_interaction    
1 1    alternativet  1     -0.9672566   -1.3101138              0
2 1 danskfolkeparti  0      0.6038972    1.3799961              1             
3 1    konservative  1      1.0759252    0.8665096              1              
4 1    enhedslisten  0     -1.0831657   -1.0815424              0              
5 1 liberalalliance  0      1.5389934    0.8470291              1              
6 1   nyeborgerlige  1      1.4139934    0.9898862              1              

 F2_interaction
1   0
2   1
3   1
4   0
5   1
6   1
>```
stathomie41
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  • Not sure how to do this short of writing your own log-likelihood function since you are looking at a multiple discrete case. The error just means that `id` and `Alternative` do not give unique cases. Like you say, multiple options can be chosen. As a start, you could run independent logit models for each party, if you believe that party matters or you can run a single logit model on all your data by defining the outcome as whether you follow a party at all. – edsandorf Jan 04 '21 at 11:51
  • I tried switching to a simple logit model at the individual level and it worked much better. Thanks for your answer! – stathomie41 Jan 06 '21 at 23:08

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