mice.impute.2l.norm {mice}R Documentation

Imputation by a Two-Level Normal Model

Description

Imputes univariate missing data using a two-level normal model

Usage

mice.impute.2l.norm(y, ry, x, type)

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.
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.

Details

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).

Value

A vector of length nmis with imputations.

Author(s)

Roel de Jong, 2008

References

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.


[Package mice version 2.2 Index]