R: Passive imputation

mice.impute.passive {mice} | R Documentation

## Passive imputation

### Description

Calculate new variable during imputation

### Usage

    mice.impute.passive(data, func)

### Arguments

`data` | A data frame
`func` | A `formula` specifying the transformations on data

### Details

Passive imputation is a special internal imputation function. Using this facility, the user can specify, at any point in the `mice` Gibbs sampling algorithm, a function on the imputed data. This is useful, for example, to compute a cubic version of a variable, a transformation like `Q = W/H^2` based on two variables, or a mean variable like `(x_1+x_2+x_3)/3`. The so derived variables might be used in other places in the imputation model. The function allows to dynamically derive virtually any function of the imputed data at virtually any time.

### Value

The result of applying `formula`

### Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

### References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

### See Also

`mice`

[Package mice version 3.8.0 Index]