[amelia] Overimputation - Setting observation-level priors on nominal variables

Hi there, I have basically 2 questions related to setting observation-level priors on nominal variables. I am trying to do an overimputation on a dichotomous variable, say y1. My 1st question: I am aware that using the argument “priors” and “overimp”, I could specify observation-level priors by 4-column matrix (row, column, prior.mean, prior.sd) or 5-column matrix (row, column, lower confidence range, upper confidence range, confidence level). I am attempting the 4-column matrix but I am not sure how do I specify prior.mean and prior.sd when my prior is the dichotomous variable itself. I read somewhere prior.mean can be set to y1 itself? Is prior.sd similar to the proportion of variance attributable to measurement error? Would need advice on how do I specify prior.sd in this case. My 2nd question: I am also aware of generating prior using the command “moPrep” from the Amelia package. The argument “error.proportion” from “moPrep” command is rather easy to understand (proportion of variance attributable to measurement error). But what is the difference setting priors using “moPrep" and “priors”? Should the output be the same? Please kindly advice. Many many thanks ! Huiying