I am running PROC MI for the first time to impute missing variables in a clinical data set. I am using fully conditional specification with the regpmm (predictive mean matching) option for some variables, because they should only take discrete values between 0 and 28 (variable in question is a count of tender joints, "tjc"). Despite this, some observations of tjc get the result 1.110223E-16. Looking at the distribution of the imputed data these values should probably have been zero. Does anyone know what might have caused this, and if there is a risk that the rest of my data is distorted, too? Can I ignore it or is there something really wrong? I have checked the original data, and all values are discrete and between 0 and 28. The data set has around 2000 subjects and 30 variables with varying degrees of missing data. Here is some example code: proc mi data=have out=mi.want_&sysdate. nimpute=25 seed=123;
var age female substance duration crp esr pain;
class substance;
fcs plots=trace nbiter=25 regpmm (tjc = age female substance duration crp esr pain) ;
run; In reality the model is much bigger though, with a macro looping through all variables as predictors in the model. I am running SAS Enterprise Guide version 7 (64-bit) on Windows. Thankful for any input!
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