Dear Saeid, Don't take my message in the wrong spirit, but I'll need a little more information to fully resolve your problem. Here is a quick simulation of the details you specify (three variables, two fully observed, one of which is Nominal). You might cut and paste this code and see if it is giving you the same problems. It runs fine on my machine: library(Amelia) vcv<-matrix(0.1,nrow=3,ncol=3) vcv[row(vcv)==col(vcv)]<-1 a<-rmvnorm(n=100,mu=c(1,2,3),vcv=vcv) # Simulated Complete Dataset a[runif(n)>0.9,1]<-NA # MCAR missingness in 1st Column a[,2]<-round(a[,2]) # Nominal variable in 2nd Column a[a[,2]>3,2]<-3 # Censor bounds to the Nominal variable a[a[,2]<0,2]<-0 output<-amelia(a,noms=2) # Run amelia declaring Nominal variable This runs fine on my machine. Occasionally (1 in 20 imputations maybe) I'll get the error message: "The resulting variance matrix was not invertible. Please check your data for highly collinear variables." Which occurs when one of the bootstraps of the dataset contains only one instance of a particular category. If this is the error message you are getting, this suggests that one of the categories of your Nominal variable is quite rare, and in some bootstraps, a dummy variable identifying this category is not identified. Without knowing anything about your dataset, this might suggest either this rare category needs to be combined with another category, or an accidental miscoding of the data. Let me know if you are getting a different error message (in which case, tell me what error you are getting) or if this sample code does not run on your system (if your specific application requires a number of libraries, other than Amelia, load those too before running this simulation, on the rare chance libraries have shared function names). This all assumes you are using R directly. If you are using AmeliaView, either try to give me more specifics about the variables you are using and the error message you get, or, if you feel comfortable, send me your dataset and I'll run it from here. (My boilerplate assurance is that I will not use your dataset for anything other than resolving this problem, will not distribute or publish from your dataset, and will delete it as soon as your problem is resolved. However, I know that for some confidential or proprietary datasets, this is not an option.) regards, James Honaker. On Sun, 18 Feb 2007, saeid_shahraz wrote: - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia