search for: pseudor

...deviance for the null model (with the intercept only). library(nnet) full.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ x1 + x2 + x3, weights=total, data=mydata) null.model<- multinom(cbind(factor1, factor2 ,., factor5) ~ +1, weights=total, data=mydata) Then I calculated pseudoR^2 = 1 - full.model$deviance / null.model$deviance I'm obtaining very low values for pseudoR^2 (there is not much difference between the deviances of the two models). full.model fits (graphically) very well to the data , so I think that the problem is in the null.model (maybe it is not well...