impute.norm: Elementary Imputation Method: Linear Regression Analysis
Description
Imputes univariate missing data using linear regression analysis
Usage
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.
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. & Oudshoorn, C.G.M. (2000). Multivariate Imputation by Chained Equations:
MICE V1.0 User's manual. Report PG/VGZ/00.038, TNO Prevention and Health, Leiden.
Brand, J.P.L. (1999). Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets. Ph.D. Thesis, TNO Prevention and Health/Erasmus University Rotterdam. ISBN 90-74479-08-1.
Schafer, J.L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.