mice.impute.pmm {mice}R Documentation

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.

Details

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

  • Draw beta and sigma from the proper posterior
  • Compute predicted values for yobs and ymis
  • For each ymis, find the observation with closest predicted value, and take its observed y as the imputation. end{enumerate} The matching is on yhat, NOT on y, which deviates from formula b.

    Value

    imp A vector of length nmis with imputations.

    Author(s)

    Stef van Buuren, Karin Oudshoorn, 2000

    References

    Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.

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


    [Package mice version 1.15 Index]