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Analysis (with)

Once you have a Mids object containing imputed data, you can use it to perform repeated analyses.

Inspecting imputed data

If you just want to inspect the outcome of the imputation process, you can use the complete/listComplete function to fill in the missing values in the original data frame.

Mice.complete — Function
complete(
    mids::Mids,
    imputation::Int
    )

Produces a data table with missings replaced with imputed values from a multiply imputed dataset (Mids) object.

The Mids object must be supplied first.

The imputation argument is an integer identifying which specific imputation is to be used to fill in the missing values.

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Mice.listComplete — Function
listComplete(
    mids::Mids
    )

Summarises the outputs of all imputations in a multiply imputed dataset (Mids) as a list of completed datasets.

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Data analysis

To perform a data analysis procedure on each imputed dataset in turn, use the with function. The with function returns the results of the analyses wrapped in a Mira object.

Mice.Mira — Type
Mira

A multiply imputed repeated analyses object.

The analyses are stored as a vector of analyses of individual imputations.

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Mice.with — Function
with(
    mids::Mids,
    func::Function
    )

Conducts repeated analyses of a multiply imputed dataset (Mids).

The function takes two arguments: firstly the Mids object itself, then a function (func). The function should take the form data -> analysisFunction(arguments, data, moreArguments...), where data represents the position of the data argument in the function.

For example: with(mids, data -> lm(@formula(y ~ x1 + x2), data))

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The with function requires the use of a closure, which then permits the function to run the specified analysis procedure on each imputed dataset in turn. For example:

using CSV, DataFrames, GLM, Mice, Random, Statistics

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"]] .= false;

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

imputedData = mice(myData, predictorMatrix = myPredictorMatrix);

analysesMeans = with(imputedData, data -> mean(data.Cholesterol));
# returns Mira of the mean of Bilirubin in each imputed dataset

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

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