`lmer`

Imputes univariate systematically and sporadically missing data using a two-level normal model using `lme4::lmer()`

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

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'. Fixed effects are indicated by
a '1'.

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.

…

Arguments passed down to `lmer`

Vector with imputed data, same type as `y`

, and of length
`sum(wy)`

Data are missing systematically if they have not been measured, e.g., in the case where we combine data from different sources. Data are missing sporadically if they have been partially observed.

While the method is fully Bayesian, it may fix parameters of the variance-covariance matrix or the random effects to their estimated value in cases where creating draws from the posterior is not possible. The procedure throws a warning when this happens.

Jolani S. (2017) Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations. Forthcoming.

Jolani S., Debray T.P.A., Koffijberg H., van Buuren S., Moons K.G.M. (2015).
Imputation of systematically missing predictors in an individual
participant data meta-analysis: a generalized approach using MICE.
*Statistics in Medicine*, 34:1841-1863.

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.norm`

,
`mice.impute.2l.pan`