I am going through "Applied Logistic Regression" by Hosmer, Lemeshow and Sturdivant, concretely chapter 8, multinomial logistic regression. I've built a model:
>library(nnet)
>library(aplore3); data(aps)
>fit <- multinom(place3 ~ danger, data = aps)
I got coefficients that match those in the book. My problem is when I try to make predictions: it just stacks everything in the level that is the most frequent in the data, and zero in other levels. I used code:
>preds <- predict(fit, newdata = data.frame(danger = aps$danger),type = "class")
>table(preds)
OutDay Int Res
508 0 0
I've searched the web and got to the nice r-bloggers post and the function described there kind of sorted the thing out, with the same fit I got
predictions
Int OutDay Res
132 262 114
But I searched the web further because I wanted to read more about the use of the predict function with multinom so I ran into some examples like this one on github where the poster uses predict in the straightforward way. I replicated the example and got everything OK.
My final question is if someone can explain when to use the predict with multinom object in the complicated (r-bloggers' post) way and when to use it in the straightforward way. Is the difference that in my example I am trying to get out fitted values for the data that was used to fit the model, or there's more to it?
Thanks!
