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.
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