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 Oudshoorn, 2000

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.


[Package mice version 1.15 Index]