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