Best R Help, I like to estimate a Multinomial Logit Model with 10 Classes. The problem is that the number of observations differs a lot over the 10 classes:
Class | num. Observations A | 373 B | 631 C | 171 D | 700 E | 87 F | 249 G | 138 H | 133 I | 162 J | 407 Total: 3051 Where my data looks like: x1 x2 x3 x4 Class 1 1,02 2 1 A 2 7,2 1 5 B 3 4,2 1 4 H 1 4,1 1 8 F 2 2,4 3 7 D 1 1,2 0 4 J 2 0,9 1 2 G 4 4 3 0 C . . . . . My model looks like: estmodel <- multinom(choice ~ x1 + x2 + x3 + x4, data = trainset) When I estimate the model and use the resulting model for prediction of 'new' observations the model has a bias towards the Classes with a large number of observations (A,B,D,J), the other classes are never predicted by the model. I thougth that the option "weights" of the multinom function could be usefull but I am not sure how to use this in the above case. Is there someone with experience regarding such a weigthing approach in multinom? If someone could help me with suggestions it would be great! Nice day, Arne ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
