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 usingI()
. SeejomoImpute
.- 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
# }