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)
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. |
type |
Vector of length ncol(x) identifying random and class variables.
Random variables are identified by a '2'. The class variable (only one
is allow) is code as '-2'. Random variables always include the fixed
effect. |
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 statistical properties see Van Buuren (2010).
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. (2009) MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. http://www.stefvanbuuren.nl/publications/MICE in R - Draft.pdf
Van Buuren, S. (2010) Multiple imputation of multilevel data. In Hox, J.J. and and Roberts, K. (Eds.), The Handbook of Advanced Multilevel Analysis, Milton Park, UK: Routledge.