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Pooling coefficients (pool)

Once you have a Mira object containing the results of repeated analyses, you can use the pool function to pool the results. The pool function returns the pooled results wrapped in a Mipo object.

Mice.Mipo — Type
Mipo

A type for storing the pooled results of multiply imputed repeated analyses (Mira).

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Mice.pool — Function
pool(mira::Mira)

Pools the results of multiply imputed repeated analyses (Mira). The function will work on any Mira object containing model outputs which are receptive to the coef, stderror and nobs functions from StatsAPI.jl.

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The pool function should work on any Mira of model outputs that accept the StatsAPI functions coef, stderror and nobs. Otherwise, you will get an error and you will need to pool the results manually in accordance with Rubin's rules [3].

For example:

using CSV, DataFrames, GLM, Mice, Random

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

# Defining missing values
colsWithMissings = ["Drug", "Ascites", "Hepatomegaly", "Spiders", "Cholesterol", "Copper", "Alk_Phos", "SGOT", "Tryglicerides", "Platelets", "Prothrombin", "Stage"];
myData[!, colsWithMissings] = allowmissing(myData[!, colsWithMissings]);
for i in colsWithMissings
    replace!(myData[!, i], "NA" => missing)
end
for i in ["Cholesterol", "Copper", "Alk_Phos", "SGOT", "Tryglicerides", "Platelets", "Prothrombin"]
    myData[!, i] = passmissing(x -> parse(Float64, x)).(myData[!, i])
end

myMethods = makeMethods(myData);
myMethods[["ID", "N_Days"]] .= "";

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

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

imputedData = mice(myData, predictorMatrix = myPredictorMatrix, methods = myMethods);

analysesLMs = with(imputedData, data -> lm(@formula(N_Days ~ Drug + Age + Stage + Bilirubin), data));
# returns Mira of linear model outputs from each imputed dataset

resultsLMs = pool(analysesLMs);
# returns Mipo of pooled linear model results