R: Impute multilevel missing data using 'pan'

mice.impute.panImpute {mice} | R Documentation

## Impute multilevel missing data using `pan`

### Description

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.

### Usage

    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.

### Author(s)

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).

### 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.

### See Also

`panImpute`

Other multivariate-2l: `mice.impute.jomoImpute()`

### 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)

[Package mice version 3.13.0 Index]