mice.impute.2L.norm {mice} | R Documentation |
Imputes univariate missing data using a two-level normal model
mice.impute.2L.norm(y, ry, x, type, intercept=TRUE, ...) mice.impute.2l.norm(y, ry, x, type, intercept=TRUE, ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
type |
Vector of length |
intercept |
Logical determining whether the intercept is automatically added. |
... |
Other named arguments. |
Implements the Gibbs sampler for the linear multilevel model with heterogeneous with-class variance (Kasim and Raudenbush, 1998). Imputations are drawn as an extra step to the algorithm. For simulation work see Van Buuren (2011).
The random intercept is automatically added in
mice.impute.2L.norm()
.
A vector of length nmis
with imputations.
Roel de Jong, 2008
Kasim RM, Raudenbush SW. (1998). Application of Gibbs sampling to nested variance components models with heterogeneous within-group variance. Journal of Educational and Behavioral Statistics, 23(2), 93–116.
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/
Van Buuren, S. (2011) Multiple imputation of multilevel data. In Hox, J.J. and and Roberts, J.K. (Eds.), The Handbook of Advanced Multilevel Analysis, Chapter 10, pp. 173–196. Milton Park, UK: Routledge.