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Imputation by a two-level normal model

Source: R/mice.impute.2l.norm.R
mice.impute.2l.norm.Rd

Imputes univariate missing data using a two-level normal model

Usage

mice.impute.2l.norm(y, ry, x, type, wy = NULL, intercept = TRUE, ...)

Arguments

y

Vector to be imputed

ry

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.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

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.

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

intercept

Logical determining whether the intercept is automatically added.

...

Other named arguments.

Value

Vector with imputed data, same type as y, and of length sum(wy)

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).

Note

Added June 25, 2012: The currently implemented algorithm does not handle predictors that are specified as fixed effects (type=1). When using mice.impute.2l.norm(), the current advice is to specify all predictors as random effects (type=2).

Warning: The assumption of heterogeneous variances requires that in every class at least one observation has a response in y.

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. doi:10.18637/jss.v045.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.

See also

Other univariate-2l: mice.impute.2l.bin(), mice.impute.2l.lmer(), mice.impute.2l.pan()

Author

Roel de Jong, 2008