mice.impute.2l.norm: 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, intercept = TRUE, ...)

Arguments

Incomplete data vector of length n

Vector of missing data pattern (FALSE=missing, TRUE=observed)

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 allowed) is coded as '-2'. Random variables also include the fixed effect.

intercept

Logical determining whether the intercept is automatically added.

...

Other named arguments.

Value

A vector of length nmis with imputations.

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 simulation work see Van Buuren (2011).

The random intercept is automatically added in mice.impute.2L.norm(). A model within a random intercept can be specified by mice(..., intercept = FALSE).

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. (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.