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

 mice.impute.bygroup R Documentation

## Groupwise Imputation Function

### Description

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

### Usage

```mice.impute.bygroup(y, ry, x, wy=NULL, 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. `wy` Vector of `length(y)` indicating which entries should be imputed. `group` Name of grouping variable `imputationFunction` Imputation method for mice `...` More arguments to be passed to imputation function

### Value

Vector of imputed values

### Examples

```## Not run:
#############################################################################
# EXAMPLE 1: Cluster-specific imputation for some variables
#############################################################################

library(mice)
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
method <- rep("norm", V)
names(method) <- colnames(dat)

#** groupwise imputation of variable books
method["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, method=method, predictorMatrix=predictorMatrix,
m=1, maxit=1, group=group, imputationFunction=imputationFunction )

#############################################################################
# EXAMPLE 2: Group-wise multilevel imputation '2l.pan'
#############################################################################

library(mice)
dat <- data.ma01

# select data
dat <- dat[, c("idschool","hisei","books","female") ]
V <- ncol(dat)
dat <- dat[ ! is.na( dat\$books), ]
# define factor variable

dat\$books <- as.factor(dat\$books)
# create initial predictor matrix and imputation methods
predictorMatrix <- matrix( 0, nrow=V, ncol=V)
rownames(predictorMatrix) <- colnames(predictorMatrix) <- colnames(dat)
predictorMatrix["idschool", ] <- 0
predictorMatrix[ "hisei", "idschool" ] <- -2
predictorMatrix[ "hisei", c("books","female") ] <- 1
method <- rep("", V)
names(method) <- colnames(dat)
method["hisei"] <- "bygroup"
group <- list( "hisei"="female" )
imputationFunction <- list("hisei"="2l.pan" )

#** conduct multiple imputation in mice
imp <- mice::mice( dat, method=method, predictorMatrix=predictorMatrix,
m=1, maxit=1, group=group, imputationFunction=imputationFunction )
str(imp)

## End(Not run)
```

miceadds documentation built on Jan. 7, 2023, 1:09 a.m.