GitHub

Imputation (mice)

The main function of the package is mice, which takes a Tables.jl-compatible table as its input. It returns a multiply imputed dataset (Mids) object with the imputed values.

Mice.Mids — Type
Mids

A multiply imputed dataset object.

The data originally supplied are stored as data.

The imputed data are stored as imputations (one column per imputation).

The locations at which data have been imputed are stored as imputeWhere.

The number of imputations is stored as m.

The imputation method for each variable is stored as methods.

The predictor matrix is stored as predictorMatrix.

The order in which the variables are imputed is stored as visitSequence.

The number of iterations is stored as iter.

The mean of each variable across the imputations is stored as meanTraces.

The variance of each variable across the imputations is stored as varTraces.

source
Mice.mice — Function
mice(
    data;
    m::Int = 5,
    imputeWhere::AxisVector{Vector{Bool}} = findMissings(data),
    visitSequence::Vector{String} = makeMonotoneSequence(imputeWhere),
    methods::AxisVector{String} = makeMethods(data),
    predictorMatrix::AxisMatrix{Int} = makePredictorMatrix(data),
    iter::Int = 10,
    progressReports::Bool = true,
    kwargs...
    )

Imputes missing values in a dataset using the MICE algorithm. The output is a Mids object.

The data containing missing values (data) must be supplied as a Tables.jl table.

The number of imputations created is specified by m.

imputeWhere is an AxisVector of boolean vectors specifying where data are to be imputed. The default is to impute all missing data.

The variables will be imputed in the order specified by visitSequence. The default is sorted by proportion of missing data in ascending order; the order can be customised using a vector of variable names in the desired order. Any column not to be imputed at all can be left out of the visit sequence.

The imputation method for each variable is specified by the AxisVector methods. The default is to use predictive mean matching (pmm) for all variables. Any variable not to be imputed can be marked as such using an empty string ("").

The predictor matrix is specified by the AxisMatrix predictorMatrix. The default is to use all other variables as predictors for each variable. Any variable not predicting another variable can be marked as such in the matrix using a 0.

The number of iterations is specified by iter.

If progressReports is true, a progress indicator will be displayed in the console.

source
mice(
    mids::Mids;
    iter::Int = 10,
    progressReports::Bool = true,
    kwargs...
    )

Adds additional iterations to an existing Mids object.

The number of additional iterations is specified by iter.

progressReports can also be specified: all other arguments will be ignored or passed to inner functions.

source

Funded by Wellcome     Wellcome logo