# mice.impute.bygroup: Groupwise Imputation Function In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## Description

The function `mice.impute.bygroup` performs groupwise imputation for arbitrary imputation methods defined in mice.

## Usage

 `1` ```mice.impute.bygroup(y, ry, x, group, imputationFunction, ...) ```

## Arguments

 `y` Incomplete data vector of length `n` `ry` Vector of missing data pattern (`FALSE` – missing, `TRUE` – observed) `x` Matrix (`n` x `p`) of complete covariates. `group` Name of grouping variable `imputationFunction` Imputation method for mice `...` More arguments to be passed to imputation function

## Value

Vector of imputed values

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29``` ```## Not run: ############################################################################# # EXAMPLE 1: Cluster-specific imputation for some variables ############################################################################# data( data.ma01 ) dat <- data.ma01 # use sub-dataset dat <- dat[ dat\$idschool <= 1006 , ] V <- ncol(dat) # create initial predictor matrix and imputation methods predictorMatrix <- matrix( 1 , nrow=V , ncol=V) diag(predictorMatrix) <- 0 rownames(predictorMatrix) <- colnames(predictorMatrix) <- colnames(dat) predictorMatrix[ , c("idstud", "studwgt","urban" ) ] <- 0 imputationMethod <- rep("norm" , V) names(imputationMethod) <- colnames(dat) #** groupwise imputation of variable books imputationMethod["books"] <- "bygroup" # specify name of the grouping variable ('idschool') and imputation method ('norm') group <- list( "books" = "idschool" ) imputationFunction <- list("books" = "norm" ) #** conduct multiple imputation in mice imp <- mice::mice( dat, imputationMethod=imputationMethod, predictorMatrix=predictorMatrix, m=1 , maxit=1 , group = group , imputationFunction = imputationFunction ) ## End(Not run) ```

### Example output

```Loading required package: mice