I have problems in understanding maxit parameter of the mice package:
library(mice)
data(nhanes)
str(nhanes)
imp = mice(nhanes, m=5, printFlag=FALSE, maxit = 30, seed=2525)
As described here I want to check the plausibility of the imputation results by considering the density plot densityplot(imp) (expecting similarity) and the convergence plot(imp) .
I know plot(imp) shows as lines all m Imputations (giving different results based on Gibbs Sampling) over the iterations (defined by maxit). When the trace lines reach a value and fluctuate slightly around it, convergence has been achieved (from here). But how does the algorithm improve the predictive value in each Iteration?
