mice.impute.norm: Imputation by Bayesian Linear Regression
Description
Imputes univariate missing data using Bayesian linear regression analysis
Usage
mice.impute.norm(y, ry, x, ...)
Arguments
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.
...
Other named arguments.
Value
A vector of length nmis with imputations.
Details
Draws values of beta and sigma for Bayesian linear regression imputation
of y given x according to Rubin p. 167.
References
Van Buuren, S., Groothuis-Oudshoorn, K. (2011)
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.