Calculate new variable during imputation

`mice.impute.passive(data, func)`

data

A data frame

func

A `formula`

specifying the transformations on data

The result of applying `formula`

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.

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/