How to have a model with single covariate? I am working on a data with 14 columns and I must have ccarduse as the response and prodebt as the single covariate. How am I suppose to do this correctly?
This is what it came to my mind:
mod1<-multinom(ccarduse~prodebt, debt)
# weights:  9 (4 variable)
initial  value 429.557405 
final  value 386.378125 
converged

also I was planning on creating a graph to predicted probabilities of  each credit card against prodebt scores.
I used the following code but unfortunately it doesn't give me a graph nor an error could you please guide?
    preds=predict(mod1,data.frame(debt=prodebt),type="probs")
    plot(debt, preds[,1],ylim=c(.15,.6),xlab="credit card",ylab="Probabilities of Party identification")
Error in plot.default(...) : 
  formal argument "xlab" matched by multiple actual arguments


The predictions from your model give predictions of multiple different (but related) quantities. In particular, you get a predicted probability for each category in ccarduse, since the model is multinomial.
If you want to plot these predicted probabilities, you have to plot your prodebt against each column of your predictions. You can display this in multiple plots or with the values on top of either; it's your choice.
