# Imputation by a two-level logistic model using `glmer`

Source: `R/mice.impute.2l.bin.R`

`mice.impute.2l.bin.Rd`

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

## 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'. 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

`glmer`

## Details

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.

## References

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.

## See also

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

,
`mice.impute.2l.norm()`

,
`mice.impute.2l.pan()`

## Examples

```
library(tidyr)
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
data("toenail2")
data <- tidyr::complete(toenail2, patientID, visit) %>%
tidyr::fill(treatment) %>%
dplyr::select(-time) %>%
dplyr::mutate(patientID = as.integer(patientID))
if (FALSE) {
pred <- mice(data, print = FALSE, maxit = 0, seed = 1)$pred
pred["outcome", "patientID"] <- -2
imp <- mice(data, method = "2l.bin", pred = pred, maxit = 1, m = 1, seed = 1)
}
```