Imputes univariate missing data using linear regression analysis (non Bayesian version)
mice.impute.norm.nob(y, ry, x, ...)
Incomplete data vector of length n
Vector of missing data pattern (FALSE
=missing,
TRUE
=observed)
Matrix (n
x p
) of complete covariates.
Other named arguments.
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 imputed data is therefore underestimated.
This creates imputation using the spread around the fitted linear regression
line of y
given x
, as fitted on the observed data.
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice
:
Multivariate Imputation by Chained Equations in R
. Journal of
Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/
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.