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mice (version 2.11)

mice.impute.pmm: Imputation by Predictive Mean Matching

Description

Imputes univariate missing data using predictive mean matching

Usage

mice.impute.pmm(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

  • impNumeric 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.

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/