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 n |
ry |
Vector of missing data pattern (FALSE =missing, TRUE =observed) |
x |
Matrix (n x p ) of complete covariates. |
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. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf
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.