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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.

See also

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