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mice (version 1.21)

mice.impute.pmm: Elementary Imputation Method: Linear Regression Analysis

Description

Imputes univariate missing data using predictive mean matching

Usage

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

Arguments

y
Incomplete data vector of length n
ry
Vector of missing data pattern (FALSE=missing, TRUE=observed)
x
Matrix (n x p) of complete covariates.

Value

  • impA vector of length nmis with imputations.

Details

Imputation of y by predictive mean matching, based on Rubin (p. 168, formulas a and b). The procedure is as follows:
  1. Draw $\beta$ and $\sigma$ from the proper posterior
  2. Compute predicted values for yobs and ymis
  3. For each $y_{mis}$, find the observation with closest predicted value, and take its observed $y$ as the imputation. The matching is on $y$, NOT on $y$, which deviates from formula b.

References

Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Leiden: TNO Quality of Life. http://www.stefvanbuuren.nl/publications/MICE V1.0 Manual TNO00038 2000.pdf Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.