Calculate new variable during imputation
mice.impute.passive(data, func)The result of applying formula
A data frame
A formula specifying the transformations on data
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
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.
tools:::Rd_expr_doi("10.18637/jss.v045.i03")
mice