I am running multinomial logit model using mlogit in R. The model includes 3 alternatives as the dependent variable and 4 individual specific predictors. Sample size is 50. The three of them are of class:factor while the other predictor is in class numeric. When I run model with ONLY 3 class factor predictors it works OK. But when I include numeric predictor along with the above three predictors it returns the following error in R. mlogit_model<-mlogit(y ~ 1| x1+x2+x3+x4, data=dat_long, reflevel = "3") Error in solve.default(H, g[!fixed]) : system is computationally singular: reciprocal condition number = 8.31637e-17 What could be the reason for this error? I searched for this and found several related posts online, but in this case multi-collinearity [eg][1] would not be the cause because the problem with numeric and factor variables. Any help would be much appreciated. [1]: https://stats.stackexchange.com/questions/44359/r-mlogit-error-on-data-system-is-exactly-singular