How to display multinominal logit results graphically?

Adriana Lins de Albuquerque writes:

Do you have any suggestions for the best way to represent multinominal logit results graphically? I am using stata.

My reply: I don’t know from Stata, but here are my suggestions:

1. If the categories are unordered, break them up into a series of binary choices in a tree structure (for example, non-voter or voter, then voting for left or right, then voting for left party A or B, then voting for right party C or D). Each of these is a binary split and so can be displayed using the usual techniques for logit (as in chapters 3 and 4 of ARM).

2. If the categories are ordered, see Figure 6.4 of ARM for an example (from our analysis of storable votes).

6 thoughts on “How to display multinominal logit results graphically?

  1. Check out:

    Potârcă, Gina, Melinda Mills, and Laurent Lesnard. 2013. “Family Formation Trajectories in Romania, the Russian Federation, and France. Towards the Second Demographic Transition?” European Journal of Population 29(1):69-101. doi: 10.1007/s10680-012-9279-9

    The multinomial logit graph in that paper was also made in Stata, likely using:

    Long, J. Scott, and Jeremy Freese. 2006. Regression Models for Categorical Dependent Variables Using Stata, 2nd ed. Stata Press.

  2. I agree with Paul G the first thing to try is Stata’s own -marginsplot- command (you need v12+) which is really good and will save you lots of time. You get predicted probabilities marginalized over whatever combination of independent variables – and their interactions – you want! But if you want to pile up these curves for each of the categories on top of one another (like you sometimes see item-response people doing), Stata doesn’t do transparent graphics, so you’ll have to -serset- the lines, export them, and do it in R.

    But without having really spared enough time to think it through… I wonder if you could also do some kind of biplot, feeding a predictor vs outcome contingency table into the -mca- correspondence analysis command? However, my experience of biplots is that your ‘clients’ will really struggle to understand them.

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