How to set specific contrasts in multinom() in nnet package?

Question

I have a 3-class problem that needs classification. I want to use the multinomial logistic regression in `nnet` package. The Class outcome has 3 factors, P, Q, R. I want to treat Q as the base factor. So I tried to write it the contrasts like this: ``` P <- c(1,0,0) R <- c(0,0,1) contrasts(trainingLR$Class) <- cbind(P,R) ``` checked it: ``` > contrasts(trainingLR$Class) P R P 1 0 Q 0 0 R 0 1 ``` Now `multinom()`: ``` library(nnet) multinom(Class ~., data=trainingLR) ``` Output: ``` > multinom(Class ~., data=trainingLR) # weights: 39 (24 variable) initial value 180.172415 iter 10 value 34.990665 iter 20 value 11.765136 iter 30 value 0.162491 iter 40 value 0.000192 iter 40 value 0.000096 iter 40 value 0.000096 final value 0.000096 converged Call: multinom(formula = Class ~ ., data = trainingLR) Coefficients: (Intercept) IL8 IL17A IL23A IL23R Q -116.2881 -16.562423 -34.80174 3.370051 6.422109 R 203.2414 6.918666 -34.40271 -10.233787 31.446915 EBI3 IL6ST IL12A IL12RB2 IL12B Q -8.316808 12…

Accepted Answer

I tried to avoid using contrasts and I discovered the `relevel` function for choosing a desired level as baseline. The following code

```
trainingLR$Class <- relevel(trainingLR$Class, ref = "P")
```

should set "P" level as your baseline. Therefore, try the same thing with "Q" or "R" levels.

The R Documentation (`?relevel`) mentions "This is useful for `contr.treatment` contrasts which take the first level as the reference."

Though might be too late to answer now, but since others might be interested, I thought is worthwhile sharing the above option.

How to set specific contrasts in multinom() in nnet package?

Solution

Problem