As easy as sounds.  Seems like a proc sql, but I would also be interested in see data step option.   data meta; input A;   datalines; 12 8 14 25 8 16 ;     Results A   B 12    83 8      83 14    83 25    83 8      83 16    83   ... View more

Hello, I have run Proc MI, in particular the FCS Discrim for arbitrary missing categorical data. I had one categorical variable with missingness, we will call it X1.  I imputed it and got the  category percentages for X1. I also used the imputes in the BY statement of a Proc Freq and got a chi-sq test statistic, pooled them for the X1 imputes. In the chi-sq test, I compared X1 to another categorical variable (without missingness), we  will call this X2. My question, is it possible to get the imputed values (percentages) of X1for the X2 variable levels? The program just kicks out the percentages for X1, though I was hoping to see the percentages for X1 at the unique groups of X2. You can see the unique values in the impute, but these are the ungrouped or unpooled versions. Let me know if I need to clarify anything! Thanks, H L ... View more

I have a categorical variable, I will call it City. Each City has a number between 1-1500, though only 75 actual numbers are used between this range, with no pattern.  I want to create a dummy variable City_1 - City_75 for the 75 cities actually in the dataset. Example data and dummy coded variable: Current:                              Desired: City = 1                              City_1 = 1 City = 1                              City_1 = 1 City = 12                            City_2 = 1 City = 44                            City_3 = 1 City = 163                          City_4 = 1 City = 163                          City_4 = 1 City = 199                          City_5 = 1 So implicitly if City_1 = 1, then City_2,...,City_5 would all = 0 for that observation (person). The dummy code should be a numeric value. Thanks in advance, and please ask questions if I did not provide enough information. ... View more