I want to perform multiple imputation with chained equations, using the package ‘mice’ in R. One of the variables that has missing values is categorical with three levels (say A, B, and C). The values that are missing from this variable can in reality only be A or B, but not C. How can I restrict the imputation such that level C is never imputed for this variable?

I want to perform multiple imputation with chained equations, using the package ‘mice’ in R. One of the variables that has missing values is categorical with three levels (say A, B, and C). The values that are missing from this variable can in reality only be A or B, but not C. How can I restrict the imputation such that level C is never imputed for this variable?

I want to perform multiple imputation with chained equations, using the package ‘mice’ in R. One of the variables that has missing values is categorical with three levels (say A, B, and C). The values that are missing from this variable can in reality only be A or B, but not C. How can I restrict the imputation such that level C is never imputed for this variable?

So I have a dataset with initially 9 variables (2 categorical, 4 dummy, 3 continious). Since I will be conducting multiple linear regression, I need the categorical variables to be dummy as well. So I created a new dataset in which I turned the categorical variables into dummy. So now both of my categorical variables have 3 dummy variables. As you probably know, only one of these dummy variables can be 1, and the other two needs to be 0.

I have a dataset with time-ordered variables where I distinguish between a continuous series of missing values including the final value (monotone missing) and missing values where at least one non-missing value separates them from the final value (intermittent missing).