mice (version 2.1)

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, 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.