impute.norm.improper: Elementary Imputation Method: Linear Regression Analysis (improper)
Description
Imputes univariate missing data using linear regression analysis (improper version)Usage
impute.norm.improper(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.
Warning
The function does not incorporate the variability of the regression
weights, so it is not 'proper' in the sense of Rubin. For small samples,
variability of the imputed data is therefore somewhat underestimated.Details
This creates imputation using the spread around the fitted
linear regression line of y given x, as fitted on the observed data.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.