A small data set with non-monotone missing values.

## Format

A data frame with 25 observations on the following 4 variables.

age

Age group (1=20-39, 2=40-59, 3=60+)

bmi

Body mass index (kg/m**2)

hyp

Hypertensive (1=no,2=yes)

chl

Total serum cholesterol (mg/dL)

## Source

Schafer, J.L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall. Table 6.14.

## Details

A small data set with missing data and mixed numerical and discrete variables. The data set `nhanes` is the same data set, but with all data treated as numerical.

`nhanes`

## Examples

``````# create 5 imputed data sets
imp <- mice(nhanes2)
#>
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl
#>   3   1  bmi  hyp  chl
#>   3   2  bmi  hyp  chl
#>   3   3  bmi  hyp  chl
#>   3   4  bmi  hyp  chl
#>   3   5  bmi  hyp  chl
#>   4   1  bmi  hyp  chl
#>   4   2  bmi  hyp  chl
#>   4   3  bmi  hyp  chl
#>   4   4  bmi  hyp  chl
#>   4   5  bmi  hyp  chl
#>   5   1  bmi  hyp  chl
#>   5   2  bmi  hyp  chl
#>   5   3  bmi  hyp  chl
#>   5   4  bmi  hyp  chl
#>   5   5  bmi  hyp  chl

# print the first imputed data set
complete(imp)
#>      age  bmi hyp chl
#> 1  20-39 25.5  no 118
#> 2  40-59 22.7  no 187
#> 3  20-39 26.3  no 187
#> 4  60-99 22.7 yes 218
#> 5  20-39 20.4  no 113
#> 6  60-99 21.7 yes 184
#> 7  20-39 22.5  no 118
#> 8  20-39 30.1  no 187
#> 9  40-59 22.0  no 238
#> 10 40-59 24.9  no 204
#> 11 20-39 29.6  no 187
#> 12 40-59 22.0  no 229
#> 13 60-99 21.7  no 206
#> 14 40-59 28.7 yes 204
#> 15 20-39 29.6  no 238
#> 16 20-39 29.6  no 238
#> 17 60-99 27.2 yes 284
#> 18 40-59 26.3 yes 199
#> 19 20-39 35.3  no 218
#> 20 60-99 25.5 yes 206
#> 21 20-39 22.5  no 238
#> 22 20-39 33.2  no 229
#> 23 20-39 27.5  no 131
#> 24 60-99 24.9  no 206
#> 25 40-59 27.4  no 186
``````