mice.impute.norm {mice} | R Documentation |
Imputes univariate missing data using Bayesian linear regression analysis
mice.impute.norm(y, ry, x, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
... |
Other named arguments. |
Draws values of beta
and sigma
for Bayesian linear regression
imputation of y
given x
according to Rubin p. 167.
A vector of length nmis
with imputations.
Using mice.impute.norm
for all columns is similar to Schafer's
NORM method (Schafer, 1997).
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
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/
Brand, J.P.L. (1999) Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. Dissertation. Rotterdam: Erasmus University.
Schafer, J.L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.