mice (version 2.14)

mice.impute.pmm: Imputation by Predictive Mean Matching

Description

Imputes univariate missing data using predictive mean matching

Usage

mice.impute.pmm(y, ry, x, ...)
mice.impute.pmm2(y, ry, x, ...)

Arguments

y

Numeric vector with incomplete data

ry

Response pattern of y (TRUE=observed, FALSE=missing)

x

Design matrix with length(y) rows and p columns containing complete covariates.

...

Other named arguments.

Value

• imp
  Numeric vector of length sum(!ry) with imputations

Details

Imputation of y by predictive mean matching, based on Rubin (1987, p. 168, formulas a and b). The procedure is as follows:

1. Estimate beta and sigma by linear regression
2. Draw beta* and sigma* from the proper posterior
3. Compute predicted values foryobsbeta andymisbeta*
4. For eachymis, find the observation with closest predicted value, and take its observed value inyas the imputation.
5. If there is more than one candidate, make a random draw among them. Note: The matching is done on predictedy, NOT on observedy.

mice.impute.pmm2() is about five times faster than mice.impute.pmm(), and was added to mice 2.13. If pmm2() holds up after testing, expect it to replace the default function pmm() in a future version of mice.

References

Little, R.J.A. (1988), Missing data adjustments in large surveys (with discussion), Journal of Business Economics and Statistics, 6, 287--301.

Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., Rubin, D.B. (2006) Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76, 12, 1049--1064. http://www.stefvanbuuren.nl/publications/FCS in multivariate imputation - JSCS 2006.pdf

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