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I have two questions about the mice package.

  • The first, is the mincor in the quickpred argument. When on the cran it says it is the absolute minimum correlation compared. Does this mean that if I set mincor to zero even very weak correlations will be accepted? If I understand correctly, for a good result I should put values close to 1. Sorry if I'm being too layman or ignorant on the subject, but I had to learn from scratch about multiple imputation.
  • Another question I have is about the size of the missing values. I think my data has a lot of missing values, but I'm not sure if I can imput even though.

An example of how I made the function for the multiple imputation

m.out <- mice(result.wide, m=10, 
pred=quickpred(result.wide, mincor=0, include = 
c("category", "region"), exclude=c( "NAME_AP")))

These are the amounts of missing values. enter image description here

Kledson Lemes
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  • Can you make your post [reproducible](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) and provide `dput(result.wide)`? – jrcalabrese Dec 08 '22 at 15:25

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