mice.impute.pmm {mice} | R Documentation |
Imputes univariate missing data using predictive mean matching
mice.impute.pmm(y, ry, x, donors = 5, type = 1, ridge = 1e-05, version = "", ...)
y |
Numeric vector with incomplete data |
ry |
Response pattern of |
x |
Design matrix with |
donors |
The size of the donor pool among which a draw is made. The default is
|
type |
Type of matching distance. The default choice |
ridge |
The ridge penalty applied in |
version |
A character variable indicating the version to be used. Specifying |
... |
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 donors
observations with
closest predicted values, randomly sample one of these,
and take its observed value in y
as the imputation.
Ties are broken by making a random draw
among ties.
Note: The matching is done on predicted y
, NOT on
observed y
.
Numeric vector of length sum(!ry)
with imputations
Since mice 2.22
the standard mice.impute.pmm()
calls
the much faster matcher()
function instead of .pmm.match()
. Since
matcher()
uses its own random generator, results cannot be exactly
reproduced. In case where you want the old .pmm.match()
, specify
mice(..., version = "2.21")
.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000, 2012
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.
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/