Impute missing values based on a random forest model using ranger::ranger()
rangerImpute(
formula,
data,
imp_var = TRUE,
imp_suffix = "imp",
...,
verbose = FALSE,
median = FALSE
)model formula for the imputation
A data.frame containing the data
TRUE/FALSE if a TRUE/FALSE variables for each imputed
variable should be created show the imputation status
suffix used for TF imputation variables
Arguments passed to ranger::ranger()
Show the number of observations used for training
and evaluating the RF-Model. This parameter is also passed down to
ranger::ranger() to show computation status.
Use the median (rather than the arithmetic mean) to average the values of individual trees for a more robust estimate.
the imputed data set.
Other imputation methods:
hotdeck(),
impPCA(),
irmi(),
kNN(),
matchImpute(),
medianSamp(),
regressionImp(),
sampleCat(),
xgboostImpute()
data(sleep)
rangerImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1 6654.000 5712.00 2.970833 1.4355100 3.3 38.6 645.0 3 5 3
#> 2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.0 3 1 3
#> 3 3.385 44.50 9.130740 2.6256033 12.5 14.0 60.0 1 1 1
#> 4 0.920 5.70 8.600863 1.8766000 16.5 NA 25.0 5 2 3
#> 5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.0 3 5 4
#> 6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.0 4 4 4
#> 7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.0 1 1 1
#> 8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.0 4 5 4
#> 9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.0 1 2 1
#> 10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.0 1 1 1
#> 11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.0 5 4 4
#> 12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.0 5 5 5
#> 13 0.550 2.40 7.600000 2.7000000 10.3 NA NA 2 1 2
#> 14 187.100 419.00 5.320417 1.3451600 3.1 40.0 365.0 5 5 5
#> 15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.0 1 1 1
#> 16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.0 2 2 2
#> 17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.0 2 2 2
#> 18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.0 2 2 2
#> 19 1.410 17.50 4.800000 1.3000000 6.1 34.0 NA 1 2 1
#> 20 60.000 81.00 12.000000 6.1000000 18.1 7.0 NA 1 1 1
#> 21 529.000 680.00 3.287653 0.3000000 NA 28.0 400.0 5 5 5
#> 22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.0 5 5 5
#> 23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.0 3 1 2
#> 24 207.000 406.00 5.354757 1.3599500 12.0 39.3 252.0 1 4 1
#> 25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.0 1 3 1
#> 26 36.330 119.50 6.040207 0.9702652 13.0 16.2 63.0 1 1 1
#> 27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.0 5 1 3
#> 28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.0 5 3 4
#> 29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.0 5 5 5
#> 30 100.000 157.00 5.285880 1.3557233 10.8 22.4 100.0 1 1 1
#> 31 35.000 56.00 7.885967 2.1222400 NA 16.3 33.0 3 5 4
#> 32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.5 5 2 4
#> 33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.0 1 1 1
#> 34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.0 1 1 1
#> 35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.0 2 1 1
#> 36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.0 3 1 3
#> 37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.0 4 1 3
#> 38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.0 4 1 3
#> 39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.0 2 1 1
#> 40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.0 2 1 1
#> 41 250.000 490.00 5.498953 1.0000000 NA 23.6 440.0 5 5 5
#> 42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.0 2 2 2
#> 43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.0 4 4 4
#> 44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.0 2 1 2
#> 45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.0 4 4 4
#> 46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.0 5 5 5
#> 47 4.288 39.20 8.492950 2.1238767 12.5 13.7 63.0 2 2 2
#> 48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.0 3 1 3
#> 49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.0 1 1 1
#> 50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.0 2 3 2
#> 51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.0 2 2 2
#> 52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.0 3 2 3
#> 53 14.830 98.20 8.677897 0.8781486 2.6 17.0 150.0 5 5 5
#> 54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.0 5 5 5
#> 55 1.400 12.50 6.399930 1.3485567 11.0 12.7 90.0 2 2 2
#> 56 0.060 1.00 8.100000 2.2000000 10.3 3.5 NA 3 1 2
#> 57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.0 2 1 2
#> 58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.0 3 1 3
#> 59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.0 3 2 2
#> 60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.0 4 3 4
#> 61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.0 2 1 1
#> 62 4.050 17.00 7.426097 1.4582267 NA 13.0 38.0 3 1 1
#> Dream_imp NonD_imp
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