mice.impute.norm.nob: Imputation by linear regression (non Bayesian)
Description
Imputes univariate missing data using linear regression
analysis (non Bayesian version)Usage
mice.impute.norm.nob(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.
...
Other named arguments.
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 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., 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.