Multithreading

To start with, you need to make sure that your Julia session is started with multiple threads. See here for information on how to do this.

As of v0.3.0, you need to run the entire mice() function in parallel to get the full benefit of multithreading. It's advisable to set progressReports = false. For example, you could do something like this:

using CategoricalArrays, CSV, DataFrames, Mice, Random

myData = CSV.read("test/data/cirrhosis.csv", DataFrame, missingstring = "NA");

myData.Stage = categorical(myData.Stage); # Making the Stage variable categorical

myPredictorMatrix = makePredictorMatrix(myData);
myPredictorMatrix[:, ["ID", "N_Days"]] .= 0;

Random.seed!(1234); # Set random seed for reproducibility

imputedData = Vector{Mids}(undef, 10); # Initialise vector of Mids outputs

Threads.@threads for i in 1:10 # Number of parallel runs
    # Produces 5 x 10 = 50 imputed datasets in 10 separate Mids objects
    imputedData[i] = mice(myData, m = 5, predictorMatrix = myPredictorMatrix, progressReports = false)
end

imputedData = bindImputations(imputedData); # Binds the separate Mids objects into a single output

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