Strange big number of variables in multinom() function in R

Question

When I run the multinom() function in R, the number of variables in the result is very big while I only have a few predictor variables in the formula. Can anyone explain to me why this is happening and how can I resolve it? (mv_daily only takes 0 and 1, icu_loc takes 0,1,2 in the data.)

I tried 3 predictor variables and the number of variables in the result increased to 1230! The program takes each distinct value of a predictor variable as a different variable in the results and gives it a different coefficient.

newdata2 <- read.csv("~/Desktop/input_multinom_reg_March9_csv.csv")
library(nnet)
test <- multinom(state_tomorrow ~ mv_daily + icu_loc, newdata2,maxit=400,MaxNWts=2000)

Results:

 Call:
 multinom(formula = state_tomorrow ~ mv_day2 + icu_loc, data = newdata2, 
 maxit = 400, MaxNWts = 2000)

Coefficients:
(Intercept)    mv_daily    icu_loc
F     3.6303751 -1.1223394 -0.3681095
H     1.2178084 -1.3153864  0.3721295
IND   0.4628305 -2.1366738 -1.2530020
PR    2.2952981 -1.3085620 -0.4032178
RRT   0.1000952 -0.6432881  0.7659957

# weights:  24 (15 variable)
initial  value 18682.675986 
iter  10 value 12929.391832
iter  20 value 12341.441938
final  value 12284.346914

Data look like this:

id  state_tomorrow  day  mv_daily  icu_loc
1     F              1      0         1
1     RRT            2      1         1
2     PR             4      1         0
2     PR             5      1         2

Answer 1

When estimating multinomial models, one should expect a separate parameter estimate for each factor level.