R: Imputation by a two-level normal model

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, 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.

### 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)`.

### Value

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

### 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`.

### 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. (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.

### See Also

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

[Package mice version 3.8.0 Index]