mice (version 1.21)

mice.impute.norm: Elementary Imputation Method: Linear Regression Analysis

Description

Imputes univariate missing data using 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.

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, 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.