mice.impute.passive: Passive imputation

In mice: Multivariate Imputation by Chained Equations

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v045.i03")}

See Also

mice

mice documentation built on June 7, 2023, 5:38 p.m.