# Multinomial Logit with mlogit and Yogurt Data

I am using the mlogit package in R to estimate a choice model, I started with the famous Yogurt dataset.

library(mlogit)

data(Yogurt)
names(Yogurt)

Yogdata <- mlogit.data(Yogurt, choice = "choice", shape = "wide", varying = c(2:9),
sep=".")

Yog1 <- mlogit(choice ~ feat+price , data = Yogdata)

summary(Yog1)
phat      <- Yog1\$probabilities
Yname     <-colnames(phat)
Y1hat     <- max.col(phat)
Y1hatname <- Yname[Y1hat]

preds = predict(Yog1,newdata=Yogdata)
preds[100:120,]
Yogurt[100:120,c('choice')]


The output preds includes probabilities that sum up to 1 for each row.

         dannon     hiland    weight   yoplait
[1,] 0.3881972 0.03053851 0.2200040 0.3612603
[2,] 0.6834572 0.01580043 0.1138286 0.1869137
[3,] 0.4136804 0.03137184 0.2344462 0.3205015
[4,] 0.4136804 0.03137184 0.2344462 0.3205015
[5,] 0.4136804 0.03137184 0.2344462 0.3205015
[6,] 0.4136804 0.03137184 0.2344462 0.3205015
[7,] 0.4136804 0.03137184 0.2344462 0.3205015
[8,] 0.4136804 0.03137184 0.2344462 0.3205015

[1] dannon  dannon  yoplait yoplait dannon  dannon  dannon  yoplait dannon
[10] dannon  yoplait yoplait dannon  dannon  dannon  dannon  dannon  dannon
[19] dannon  dannon  dannon


The choice would be the highest probability in each row. Would this be correct? When I compare this choice against real choices coming from Yogurt, for the same rows, I see matches are not too great. Is there a way to improve this model? Is this the right way to judge the success of this model?