Impute multilevel missing data using pan — mice.impute.panImpute • mice

This function is a wrapper around the panImpute function from the mitml package so that it can be called to impute blocks of variables in mice. The mitml::panImpute function provides an interface to the pan package for multiple imputation of multilevel data (Schafer & Yucel, 2002). Imputations can be generated using type or formula, which offer different options for model specification.

mice.impute.panImpute(
  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 panImpute.
type: An integer vector specifying the role of each variable in the imputation model (see panImpute)
m: The number of imputed data sets to generate.
silent: (optional) Logical flag indicating if console output should be suppressed. Default is to 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.

Schafer JL (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.

Schafer JL, and Yucel RM (2002). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics, 11, 437-457.

Author

Stef van Buuren, 2018, building on work of Simon Grund, Alexander Robitzsch and Oliver Luedtke (authors of mitml package) and Joe Schafer (author of pan package).

Examples

blocks <- list(c("bmi", "chl", "hyp"), "age")
method <- c("panImpute", "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)
#> 
#>  iter imp variable
#>   1   1  bmi chl hyp
#>   1   2  bmi chl hyp
#>   1   3  bmi chl hyp
#>   1   4  bmi chl hyp
#>   1   5  bmi chl hyp