MICE (mice)

Module name

mice

Package

mice

Version

3.17.0

Language

R

Docs

here

Paper

van Buuren and Groothuis-Oudshoorn[1]

Graph type

MCMC

No

Edge constraints

No

Data type

C, D, M

Data missingness

Intervention type

Docker

bpimages/mice:3.17.0-ranger

Multivariate Imputation by Chained Equations

The mice package implements a method to deal with missing data. The package creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model. The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation.

Important

This is not a structure learning algorithm, just a workaround to use imputed data for the bips_tpc module.

Some fields described

  • action Parameter for the complete function

  • defaultMethod Parameter for the complete function: Default method to use for imputation

  • include Parameter for the complete function

  • m Parameter for the mice function: Number of imputations

  • maxit Parameter for the mice function: Maximum number of iterations

  • method Parameter for the complete function: Method to use for imputation

  • mild Parameter for the complete function

  • order Parameter for the complete function

Example JSON

[
  {
    "id": "mice",
    "m": 5,
    "maxit": 3,
    "method": "rf",
    "defaultMethod": "rf",
    "action": "all",
    "include": false,
    "mild": true,
    "order": "last",
    "timeout": null
  }
]