NHANES example - all variables numerical

A small data set with 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).

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.

Source

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

Examples

# example one
data(nhanes)
bw <- 1
ilse(age~., data=nhanes,bw=bw)
#> $beta
#> (Intercept)         bmi         hyp         chl 
#>  2.06942664 -0.11656313  0.62109971  0.01056364 
#> 
#> $hX
#>       [,1]     [,2]     [,3]     [,4]
#>  [1,]    1 26.56250 1.235294 191.4000
#>  [2,]    1 22.70000 1.000000 187.0000
#>  [3,]    1 26.63178 1.000000 187.0000
#>  [4,]    1 26.56250 1.235294 191.4000
#>  [5,]    1 20.40000 1.000000 113.0000
#>  [6,]    1 26.66454 1.201671 184.0000
#>  [7,]    1 22.50000 1.000000 118.0000
#>  [8,]    1 30.10000 1.000000 187.0000
#>  [9,]    1 22.00000 1.000000 238.0000
#> [10,]    1 26.56250 1.235294 191.4000
#> [11,]    1 26.56250 1.235294 191.4000
#> [12,]    1 26.56250 1.235294 191.4000
#> [13,]    1 21.70000 1.000000 206.0000
#> [14,]    1 28.70000 2.000000 204.0000
#> [15,]    1 29.60000 1.000000 191.9111
#> [16,]    1 26.56250 1.235294 191.4000
#> [17,]    1 27.20000 2.000000 284.0000
#> [18,]    1 26.30000 2.000000 199.0000
#> [19,]    1 35.30000 1.000000 218.0000
#> [20,]    1 25.50000 2.000000 190.0530
#> [21,]    1 26.56250 1.235294 191.4000
#> [22,]    1 33.20000 1.000000 229.0000
#> [23,]    1 27.50000 1.000000 131.0000
#> [24,]    1 24.90000 1.000000 190.5630
#> [25,]    1 27.40000 1.000000 186.0000
#> 
#> $d.fn
#> [1] 6.654795e-08
#> 
#> $d.par
#> [1] 1.096466e-07
#> 
#> $iterations
#> [1] 5
#> 
#> $residuals
#>           1           2           3           4           5           6 
#> -0.76234098 -0.01994491 -0.56164448  1.23765902 -0.50633040  1.34860742 
#>           7           8           9          10          11          12 
#> -0.31436606 -0.15737777 -0.64028497  0.23765902 -0.76234098  0.23765902 
#>          13          14          15          16          17          18 
#>  0.66278272 -0.12124782 -0.26753897 -0.76234098 -0.14118407 -0.34818110 
#>          19          20          21          22          23          24 
#>  0.12127751  0.65308158 -0.76234098 -0.23970515  0.13112219  1.19885571 
#>          25 
#>  0.53846543 
#> 
#> $fitted.values
#>         1         2         3         4         5         6         7         8 
#> 1.7623410 2.0199449 1.5616445 1.7623410 1.5063304 1.6513926 1.3143661 1.1573778 
#>         9        10        11        12        13        14        15        16 
#> 2.6402850 1.7623410 1.7623410 1.7623410 2.3372173 2.1212478 1.2675390 1.7623410 
#>        17        18        19        20        21        22        23        24 
#> 3.1411841 2.3481811 0.8787225 2.3469184 1.7623410 1.2397051 0.8688778 1.8011443 
#>        25 
#> 1.4615346 
#> 
#> $inargs
#> $inargs$bw
#> [1] 1
#> 
#> $inargs$k.type
#> [1] "gaussian"
#> 
#> $inargs$method
#> [1] "Par.cond"
#> 
#> $inargs$max.iter
#> [1] 20
#> 
#> $inargs$peps
#> [1] 1e-05
#> 
#> $inargs$feps
#> [1] 1e-07
#> 
#> $inargs$infor_output
#> [1] FALSE
#> 
#> $inargs$arma
#> [1] TRUE
#> 
#> 
#> $call
#> clse(formula = age ~ ., data = nhanes, bw = bw)
#> 
#> $formula
#> age ~ .
#> <environment: 0x00000000163f4ca0>
#> 
#> $data
#>    age (Intercept)  bmi hyp chl
#> 1    1           1   NA  NA  NA
#> 2    2           1 22.7   1 187
#> 3    1           1   NA   1 187
#> 4    3           1   NA  NA  NA
#> 5    1           1 20.4   1 113
#> 6    3           1   NA  NA 184
#> 7    1           1 22.5   1 118
#> 8    1           1 30.1   1 187
#> 9    2           1 22.0   1 238
#> 10   2           1   NA  NA  NA
#> 11   1           1   NA  NA  NA
#> 12   2           1   NA  NA  NA
#> 13   3           1 21.7   1 206
#> 14   2           1 28.7   2 204
#> 15   1           1 29.6   1  NA
#> 16   1           1   NA  NA  NA
#> 17   3           1 27.2   2 284
#> 18   2           1 26.3   2 199
#> 19   1           1 35.3   1 218
#> 20   3           1 25.5   2  NA
#> 21   1           1   NA  NA  NA
#> 22   1           1 33.2   1 229
#> 23   1           1 27.5   1 131
#> 24   3           1 24.9   1  NA
#> 25   2           1 27.4   1 186
#> 
#> attr(,"class")
#> [1] "ilse"