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I am currently working on the imputation of 10 large datasets (by first creating a prediction matrix with correlation of 0.3, dfpred03) with mice in R and I am having a lot of issues like the following:

imptest <- mice(df, m=1, maxit = 1, method='cart',predictorMatrix=dfpred03)



 iter imp variable

  1   1  VAR1 VAR2  VAR3  VAR4Error: cannot allocate vector of size 446 Kb

Error during wrapup: cannot allocate vector of size 3.6 Mb

I understand I will have to make some concessions, but because I am not sure what the bottleneck is I do not really know which concessions to make.

Is there any documentation about how mice in R uses data?

My most important questions:

1. EDITED: How can I adapt the predictor matrix in order to make the imputation process less memory consuming? I am thinking of setting variables which have both low row and column sums to zero, but then I get left with NA's. Another thing I could do is to remove those variables and other variables with low observations.




2. If I succeed in doing one iteration of a dataset, would that mean that I can increase maxit and m because the maximum memory usage is in the iteration?




3. Is most memory used because of the amount of predictors or the amount of missingness in observations (and therefore the amount of observations)?




4. More generally, where does the spike in memory usage lie?