mice.impute.norm {mice} | R Documentation |
Imputes univariate missing data using 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 gives results similar to Schafer's norm method (Schafer, 1997), though much slower.
Stef van Buuren, Karin Groothuis-Oudshoorn, 2000
Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) Multivariate Imputation by Chained Equations: MICE V1.0 User's manual. Leiden: TNO Quality of Life. http://www.stefvanbuuren.nl/publications/MICE V1.0 Manual TNO00038 2000.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.