# Multivariate multilevel imputation using `jomo`

Source: `R/mice.impute.jomoImpute.R`

`mice.impute.jomoImpute.Rd`

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.

## 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.

## See also

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

## 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

```
# \donttest{
# 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)
#>
#> 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
# }
```