mice.impute.pmm {mice} | R Documentation |
Imputes univariate missing data using predictive mean matching
mice.impute.pmm(y, ry, x)
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 |
Imputation of y
by predictive mean matching, based on Rubin (1987, p. 168, formulas a and b).
The procedure is as follows:
yobs
and ymis
ymis
, find the observation with closest predicted
value, and take its observed value in y
as the imputation.
y
, NOT on observed y
.
imp |
Numeric vector of length sum(!ry) with imputations |
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
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. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf