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 all numerical variables. The data set nhanes2 is the same data set, but with age and hyp treated as factors.

nhanes2

## Examples

# create 5 imputed data sets
imp <- mice(nhanes)
#>
#>  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    1 25.5   1 187
#> 2    2 22.7   1 187
#> 3    1 27.2   1 187
#> 4    3 24.9   2 218
#> 5    1 20.4   1 113
#> 6    3 20.4   1 184
#> 7    1 22.5   1 118
#> 8    1 30.1   1 187
#> 9    2 22.0   1 238
#> 10   2 30.1   2 218
#> 11   1 27.2   1 187
#> 12   2 27.2   2 206
#> 13   3 21.7   1 206
#> 14   2 28.7   2 204
#> 15   1 29.6   1 238
#> 16   1 26.3   1 187
#> 17   3 27.2   2 284
#> 18   2 26.3   2 199
#> 19   1 35.3   1 218
#> 20   3 25.5   2 206
#> 21   1 35.3   1 204
#> 22   1 33.2   1 229
#> 23   1 27.5   1 131
#> 24   3 24.9   1 206
#> 25   2 27.4   1 186