# Multiclass logistic regression with mlogit in R

I have a multi-class dataset like the following (a,b,c,d are features and e is the class (it can be 0,1 and 2)).

    a b c d e
1   1 1 2 2 1
2   1 2 4 2 0
3   1 2 4 2 0
4   2 2 2 2 0
5   2 1 2 2 2


I am trying to use mlogit package in order to see which column is more important but I am having a difficulty to understand how to use it.

What I am doing is:

dataset$e<-as.factor(dataset$e)
mldata<-mlogit.data(dataset, choice="e")

> mldata[1:5,]
a b c d     e chid alt
1.0 1 1 2 2 FALSE    1   0
1.1 1 1 2 2  TRUE    1   1
1.2 1 1 2 2 FALSE    1   2
2.0 1 2 4 2  TRUE    2   0
2.1 1 2 4 2 FALSE    2   1


Now, in order to see the coefficients, I am constructing the model like that:

mlogit.model<- mlogit(e~1|a+b+c+d, data = mldata, reflevel="1") mlogit.model

Call:
mlogit(formula = e ~ 1 | a + b + c + d, data = mldata, reflevel = "1",     method = "nr", print.level = 0)

Coefficients:
alt0       alt2     alt0:a     alt2:a     alt0:b     alt2:b     alt0:c
-211.0953   -89.7558    27.9911    33.1440    37.7503     7.3585     8.5072
alt2:c     alt0:d     alt2:d
3.0950    37.1340     5.5584


But now I don't understand what are alt0, alt2? What are the real coefficients of a,b,c and d?

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This might help: ats.ucla.edu/stat/r/dae/mlogit.htm. –  chl Apr 25 '11 at 19:05