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Abstract
The multinomial logit model is the most widely used model for nominal multi-category responses. One problem with the model is that many parameters are involved, and another that interpretation of parameters is much harder than for linear models because the model is nonlinear. Both problems can profit from graphical representations. We propose to visualize the effect strengths by star plots, where one star collects all the parameters connected to one term in the linear predictor. In simple models, one star refers to one explanatory variable. In contrast to conventional star plots, which are used to represent data, the plots represent parameters and are considered as parameter glyphs. The set of stars for a fitted model makes the main features of the effects of explanatory variables on the response variable easily accessible. The method is extended to ordinal models and illustrated by several datasets. Supplementary materials are available online.
ACKNOWLEDGMENT
The presentation of the method and the form of the stars has strongly benefitted from suggestions by an associate editor and an unknown reviewer.
Additional information
Notes on contributors
Gerhard Tutz
Gerhard Tutz is at Ludwig-Maximilians-Universität München, Akademiestraße 1, 80799 München, Germany (E-mail: [email protected]). Gunther Schauberger is at Ludwig-Maximilians-Universität München, Akademiestraße 1, 80799 München, Germany.
Gunther Schauberger
Gerhard Tutz is at Ludwig-Maximilians-Universität München, Akademiestraße 1, 80799 München, Germany (E-mail: [email protected]). Gunther Schauberger is at Ludwig-Maximilians-Universität München, Akademiestraße 1, 80799 München, Germany.