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 |
x |
Design matrix with |
... |
Other named arguments. |
Imputation of y
by predictive mean matching, based on Rubin (1987, p. 168, formulas a and b).
The procedure is as follows:
Estimate beta and sigma by linear regression
Draw beta* and sigma* from the proper posterior
Compute predicted values for yobs
beta and ymis
beta*
For each ymis
, find the observation with closest predicted
value, and take its observed value in y
as the imputation.
If there is more than one candidate, make a random draw among them.
Note: The matching is done on predicted y
, NOT on observed y
.
imp |
Numeric vector of length |
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. (2011).
mice
: Multivariate Imputation by Chained Equations in R
.
Journal of Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/