mice.impute.norm.improper {mice} | R Documentation |
Imputes univariate missing data using linear regression analysis (improper version)
mice.impute.norm.improper(y, ry, x)
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. |
This creates imputation using the spread around the fitted linear regression line of y given x, as fitted on the observed data.
A vector of length nmis with imputations.
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 mice.imputed data is therefore somewhat underestimated.
This function is provided mainly to allow comparison between proper and improper norm methods.
Stef van Buuren, Karin Oudshoorn, 2000
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.