Despite reading this other StatsExchange post, I am still struggling to understand what iterations do in multiple imputation, i.e. the parameter "maxit" in the mice() function.
My understanding of iterations was that in the case of multiple predictor variables, since predictor variables would be used to impute other predictor variables, we would need multiple iterations to account for the different possible ways this could occur. Hence, we would get different predictor variable imputations for different iterations.
But this doesn't seem to be the correct interpretation, as even in a simulation with one predictor/response variable (code below), each iteration gives me different values... Below, I am applying multiple imputation to predict bmi (has missingness) using ONLY age (NO missingness). I expected the mean across imputations to be constant (i.e. each plotted line is horizontal) for each iteration, as there is only one predictor variable and hence only one source of uncertainty. What am I missing here? Thanks.
require(mice) require(lattice) imp <- mice(nhanes, m = 3, print=F, seed = 123) pred <- imp$pred pred[ ,"hyp"] <- 0 pred[,"chl"] <- 0 pred imp <- mice(nhanes, pred=pred, print=F) plot(imp)