[amelia] Imputation-improving Transformations

Dear list members, My name is Michael and I am a Ph.D. student in political science. I am relatively new to Amelia, so I apologize if my question seems naïve to more experienced users. I am working with a large dataset based on survey responses. The data includes a number of indices, which were created by taking the mean of multiple survey items. For example, the index "group membership" takes the average of six different types of groups a respondent can be a member of. The values of this variable range from 1 to 3, with 21 unique values (1; 1.2; 1.25; 1.33; ...; 3). My question is: How can I improve the imputation process so that Amelia only uses these 21 values? As of right now, when I impute variables like "group membership", Amelia generates a wide range of values that do not make sense (negative values, values above three, etc.) given the initial values and coding of these indices. I know how to do this with ordinal variables, a.out <- amelia(data, m = 5, ords = "var name"), but unfortunately I cannot figure out how to do a similar imputation-improving transformation for indices such as "group membership". I am using Amelia in R, so if anyone could give me the code to solve my problem, I would much appreciate it. Thank you in advance for your help. Best, Michael