Imputes univariate missing data using a two-level normal model
mice.impute.2l.norm(y, ry, x, type, wy = NULL, intercept = TRUE, ...)Vector to be imputed
Logical vector of length length(y) indicating the
the subset y[ry] of elements in y to which the imputation
model is fitted. The ry generally distinguishes the observed
(TRUE) and missing values (FALSE) in y.
Numeric design matrix with length(y) rows with predictors for
y. Matrix x may have no missing values.
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.
Logical vector of length length(y). A TRUE value
indicates locations in y for which imputations are created.
Logical determining whether the intercept is automatically added.
Other named arguments.
Vector with imputed data, same type as y, and of length
sum(wy)
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).
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. https://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.
Other univariate 2l functions: mice.impute.2l.bin,
mice.impute.2l.lmer,
mice.impute.2l.pan