Multivariate multilevel imputation using jomo — mice.impute.jomoImpute • mice

This function is a wrapper around the jomoImpute function from the mitml package so that it can be called to impute blocks of variables in mice. The mitml::jomoImpute function provides an interface to the jomo package for multiple imputation of multilevel data https://CRAN.R-project.org/package=jomo. Imputations can be generated using type or formula, which offer different options for model specification.

mice.impute.jomoImpute(
  data,
  formula,
  type,
  m = 1,
  silent = TRUE,
  format = "imputes",
  ...
)

Arguments

data: A data frame containing incomplete and auxiliary variables, the cluster indicator variable, and any other variables that should be present in the imputed datasets.
formula: A formula specifying the role of each variable in the imputation model. The basic model is constructed by model.matrix, thus allowing to include derived variables in the imputation model using I(). See jomoImpute.
type: An integer vector specifying the role of each variable in the imputation model (see jomoImpute)
m: The number of imputed data sets to generate. Default is 10.
silent: (optional) Logical flag indicating if console output should be suppressed. Default is FALSE.
format: A character vector specifying the type of object that should be returned. The default is format = "list". No other formats are currently supported.
...: Other named arguments: n.burn, n.iter, group, prior, silent and others.

Value

A list of imputations for all incomplete variables in the model, that can be stored in the the imp component of the mids object.

Note

The number of imputations m is set to 1, and the function is called m times so that it fits within the mice iteration scheme.

This is a multivariate imputation function using a joint model.

References

Grund S, Luedtke O, Robitzsch A (2016). Multiple Imputation of Multilevel Missing Data: An Introduction to the R Package pan. SAGE Open.

Quartagno M and Carpenter JR (2015). Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates. Statistics in Medicine, 35:2938-2954, 2015.

Author

Stef van Buuren, 2018, building on work of Simon Grund, Alexander Robitzsch and Oliver Luedtke (authors of mitml package) and Quartagno and Carpenter (authors of jomo package).

Examples

if (FALSE) { # \dontrun{
# Note: Requires mitml 0.3-5.7
blocks <- list(c("bmi", "chl", "hyp"), "age")
method <- c("jomoImpute", "pmm")
ini <- mice(nhanes, blocks = blocks, method = method, maxit = 0)
pred <- ini$pred
pred["B1", "hyp"] <- -2
imp <- mice(nhanes, blocks = blocks, method = method, pred = pred, maxit = 1)
} # }