mice.impute.norm {mice}R Documentation

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.

Details

Draws values of beta and sigma for Bayesian linear regression imputation of y given x according to Rubin p. 167.

Value

A vector of length nmis with imputations.

Note

Using mice.impute.norm for all columns gives results similar to Schafer's norm method (Schafer, 1997), though much slower.

Author(s)

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

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.


[Package mice version 1.21 Index]