Impute missing values based on a random forest model using xgboost::xgboost()
xgboostImpute(
formula,
data,
imp_var = TRUE,
imp_suffix = "imp",
verbose = FALSE,
nrounds = 100,
objective = NULL,
...
)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
Show the number of observations used for training
and evaluating the RF-Model. This parameter is also passed down to
xgboost::xgboost() to show computation status.
max number of boosting iterations,
argument passed to xgboost::xgboost()
objective for xgboost,
argument passed to xgboost::xgboost()
Arguments passed to xgboost::xgboost()
the imputed data set.
Other imputation methods:
hotdeck(),
impPCA(),
irmi(),
kNN(),
matchImpute(),
medianSamp(),
rangerImpute(),
regressionImp(),
sampleCat()
data(sleep)
xgboostImpute(Dream~BodyWgt+BrainWgt,data=sleep)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger Dream_imp
#> 1 6654.000 5712.00 NA 1.7991470 3.3 38.6 645.0 3 5 3 TRUE
#> 2 1.000 6.60 6.3 2.0000000 8.3 4.5 42.0 3 1 3 FALSE
#> 3 3.385 44.50 NA 3.5981774 12.5 14.0 60.0 1 1 1 TRUE
#> 4 0.920 5.70 NA 0.9710111 16.5 NA 25.0 5 2 3 TRUE
#> 5 2547.000 4603.00 2.1 1.8000000 3.9 69.0 624.0 3 5 4 FALSE
#> 6 10.550 179.50 9.1 0.7000000 9.8 27.0 180.0 4 4 4 FALSE
#> 7 0.023 0.30 15.8 3.9000000 19.7 19.0 35.0 1 1 1 FALSE
#> 8 160.000 169.00 5.2 1.0000000 6.2 30.4 392.0 4 5 4 FALSE
#> 9 3.300 25.60 10.9 3.6000000 14.5 28.0 63.0 1 2 1 FALSE
#> 10 52.160 440.00 8.3 1.4000000 9.7 50.0 230.0 1 1 1 FALSE
#> 11 0.425 6.40 11.0 1.5000000 12.5 7.0 112.0 5 4 4 FALSE
#> 12 465.000 423.00 3.2 0.7000000 3.9 30.0 281.0 5 5 5 FALSE
#> 13 0.550 2.40 7.6 2.7000000 10.3 NA NA 2 1 2 FALSE
#> 14 187.100 419.00 NA 1.5919796 3.1 40.0 365.0 5 5 5 TRUE
#> 15 0.075 1.20 6.3 2.1000000 8.4 3.5 42.0 1 1 1 FALSE
#> 16 3.000 25.00 8.6 0.0000000 8.6 50.0 28.0 2 2 2 FALSE
#> 17 0.785 3.50 6.6 4.1000000 10.7 6.0 42.0 2 2 2 FALSE
#> 18 0.200 5.00 9.5 1.2000000 10.7 10.4 120.0 2 2 2 FALSE
#> 19 1.410 17.50 4.8 1.3000000 6.1 34.0 NA 1 2 1 FALSE
#> 20 60.000 81.00 12.0 6.1000000 18.1 7.0 NA 1 1 1 FALSE
#> 21 529.000 680.00 NA 0.3000000 NA 28.0 400.0 5 5 5 FALSE
#> 22 27.660 115.00 3.3 0.5000000 3.8 20.0 148.0 5 5 5 FALSE
#> 23 0.120 1.00 11.0 3.4000000 14.4 3.9 16.0 3 1 2 FALSE
#> 24 207.000 406.00 NA 1.5919796 12.0 39.3 252.0 1 4 1 TRUE
#> 25 85.000 325.00 4.7 1.5000000 6.2 41.0 310.0 1 3 1 FALSE
#> 26 36.330 119.50 NA 0.4998185 13.0 16.2 63.0 1 1 1 TRUE
#> 27 0.101 4.00 10.4 3.4000000 13.8 9.0 28.0 5 1 3 FALSE
#> 28 1.040 5.50 7.4 0.8000000 8.2 7.6 68.0 5 3 4 FALSE
#> 29 521.000 655.00 2.1 0.8000000 2.9 46.0 336.0 5 5 5 FALSE
#> 30 100.000 157.00 NA 0.9688541 10.8 22.4 100.0 1 1 1 TRUE
#> 31 35.000 56.00 NA 4.0075836 NA 16.3 33.0 3 5 4 TRUE
#> 32 0.005 0.14 7.7 1.4000000 9.1 2.6 21.5 5 2 4 FALSE
#> 33 0.010 0.25 17.9 2.0000000 19.9 24.0 50.0 1 1 1 FALSE
#> 34 62.000 1320.00 6.1 1.9000000 8.0 100.0 267.0 1 1 1 FALSE
#> 35 0.122 3.00 8.2 2.4000000 10.6 NA 30.0 2 1 1 FALSE
#> 36 1.350 8.10 8.4 2.8000000 11.2 NA 45.0 3 1 3 FALSE
#> 37 0.023 0.40 11.9 1.3000000 13.2 3.2 19.0 4 1 3 FALSE
#> 38 0.048 0.33 10.8 2.0000000 12.8 2.0 30.0 4 1 3 FALSE
#> 39 1.700 6.30 13.8 5.6000000 19.4 5.0 12.0 2 1 1 FALSE
#> 40 3.500 10.80 14.3 3.1000000 17.4 6.5 120.0 2 1 1 FALSE
#> 41 250.000 490.00 NA 1.0000000 NA 23.6 440.0 5 5 5 FALSE
#> 42 0.480 15.50 15.2 1.8000000 17.0 12.0 140.0 2 2 2 FALSE
#> 43 10.000 115.00 10.0 0.9000000 10.9 20.2 170.0 4 4 4 FALSE
#> 44 1.620 11.40 11.9 1.8000000 13.7 13.0 17.0 2 1 2 FALSE
#> 45 192.000 180.00 6.5 1.9000000 8.4 27.0 115.0 4 4 4 FALSE
#> 46 2.500 12.10 7.5 0.9000000 8.4 18.0 31.0 5 5 5 FALSE
#> 47 4.288 39.20 NA 2.4003341 12.5 13.7 63.0 2 2 2 TRUE
#> 48 0.280 1.90 10.6 2.6000000 13.2 4.7 21.0 3 1 3 FALSE
#> 49 4.235 50.40 7.4 2.4000000 9.8 9.8 52.0 1 1 1 FALSE
#> 50 6.800 179.00 8.4 1.2000000 9.6 29.0 164.0 2 3 2 FALSE
#> 51 0.750 12.30 5.7 0.9000000 6.6 7.0 225.0 2 2 2 FALSE
#> 52 3.600 21.00 4.9 0.5000000 5.4 6.0 225.0 3 2 3 FALSE
#> 53 14.830 98.20 NA 0.6781464 2.6 17.0 150.0 5 5 5 TRUE
#> 54 55.500 175.00 3.2 0.6000000 3.8 20.0 151.0 5 5 5 FALSE
#> 55 1.400 12.50 NA 1.0527972 11.0 12.7 90.0 2 2 2 TRUE
#> 56 0.060 1.00 8.1 2.2000000 10.3 3.5 NA 3 1 2 FALSE
#> 57 0.900 2.60 11.0 2.3000000 13.3 4.5 60.0 2 1 2 FALSE
#> 58 2.000 12.30 4.9 0.5000000 5.4 7.5 200.0 3 1 3 FALSE
#> 59 0.104 2.50 13.2 2.6000000 15.8 2.3 46.0 3 2 2 FALSE
#> 60 4.190 58.00 9.7 0.6000000 10.3 24.0 210.0 4 3 4 FALSE
#> 61 3.500 3.90 12.8 6.6000000 19.4 3.0 14.0 2 1 1 FALSE
#> 62 4.050 17.00 NA 0.5567985 NA 13.0 38.0 3 1 1 TRUE
xgboostImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1 6654.000 5712.00 2.100612 1.7991470 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 10.897229 3.5981774 12.5 14.0 60.0 1 1 1
#> 4 0.920 5.70 7.107610 0.9710111 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 5.839931 1.5919796 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 2.100612 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 5.839931 1.5919796 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 3.302149 0.4998185 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 4.058626 0.9688541 10.8 22.4 100.0 1 1 1
#> 31 35.000 56.00 6.824033 4.0075836 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 5.859960 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 7.402361 2.4003341 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 8.954710 0.6781464 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 3.977688 1.0527972 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 4.717938 0.5567985 NA 13.0 38.0 3 1 1
#> Dream_imp NonD_imp
#> 1 TRUE TRUE
#> 2 FALSE FALSE
#> 3 TRUE TRUE
#> 4 TRUE TRUE
#> 5 FALSE FALSE
#> 6 FALSE FALSE
#> 7 FALSE FALSE
#> 8 FALSE FALSE
#> 9 FALSE FALSE
#> 10 FALSE FALSE
#> 11 FALSE FALSE
#> 12 FALSE FALSE
#> 13 FALSE FALSE
#> 14 TRUE TRUE
#> 15 FALSE FALSE
#> 16 FALSE FALSE
#> 17 FALSE FALSE
#> 18 FALSE FALSE
#> 19 FALSE FALSE
#> 20 FALSE FALSE
#> 21 FALSE TRUE
#> 22 FALSE FALSE
#> 23 FALSE FALSE
#> 24 TRUE TRUE
#> 25 FALSE FALSE
#> 26 TRUE TRUE
#> 27 FALSE FALSE
#> 28 FALSE FALSE
#> 29 FALSE FALSE
#> 30 TRUE TRUE
#> 31 TRUE TRUE
#> 32 FALSE FALSE
#> 33 FALSE FALSE
#> 34 FALSE FALSE
#> 35 FALSE FALSE
#> 36 FALSE FALSE
#> 37 FALSE FALSE
#> 38 FALSE FALSE
#> 39 FALSE FALSE
#> 40 FALSE FALSE
#> 41 FALSE TRUE
#> 42 FALSE FALSE
#> 43 FALSE FALSE
#> 44 FALSE FALSE
#> 45 FALSE FALSE
#> 46 FALSE FALSE
#> 47 TRUE TRUE
#> 48 FALSE FALSE
#> 49 FALSE FALSE
#> 50 FALSE FALSE
#> 51 FALSE FALSE
#> 52 FALSE FALSE
#> 53 TRUE TRUE
#> 54 FALSE FALSE
#> 55 TRUE TRUE
#> 56 FALSE FALSE
#> 57 FALSE FALSE
#> 58 FALSE FALSE
#> 59 FALSE FALSE
#> 60 FALSE FALSE
#> 61 FALSE FALSE
#> 62 TRUE TRUE
xgboostImpute(Dream+NonD+Gest~BodyWgt+BrainWgt,data=sleep)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger
#> 1 6654.000 5712.00 2.100612 1.7991470 3.3 38.6 645.00000 3 5 3
#> 2 1.000 6.60 6.300000 2.0000000 8.3 4.5 42.00000 3 1 3
#> 3 3.385 44.50 10.897229 3.5981774 12.5 14.0 60.00000 1 1 1
#> 4 0.920 5.70 7.107610 0.9710111 16.5 NA 25.00000 5 2 3
#> 5 2547.000 4603.00 2.100000 1.8000000 3.9 69.0 624.00000 3 5 4
#> 6 10.550 179.50 9.100000 0.7000000 9.8 27.0 180.00000 4 4 4
#> 7 0.023 0.30 15.800000 3.9000000 19.7 19.0 35.00000 1 1 1
#> 8 160.000 169.00 5.200000 1.0000000 6.2 30.4 392.00000 4 5 4
#> 9 3.300 25.60 10.900000 3.6000000 14.5 28.0 63.00000 1 2 1
#> 10 52.160 440.00 8.300000 1.4000000 9.7 50.0 230.00000 1 1 1
#> 11 0.425 6.40 11.000000 1.5000000 12.5 7.0 112.00000 5 4 4
#> 12 465.000 423.00 3.200000 0.7000000 3.9 30.0 281.00000 5 5 5
#> 13 0.550 2.40 7.600000 2.7000000 10.3 NA 50.22270 2 1 2
#> 14 187.100 419.00 5.839931 1.5919796 3.1 40.0 365.00000 5 5 5
#> 15 0.075 1.20 6.300000 2.1000000 8.4 3.5 42.00000 1 1 1
#> 16 3.000 25.00 8.600000 0.0000000 8.6 50.0 28.00000 2 2 2
#> 17 0.785 3.50 6.600000 4.1000000 10.7 6.0 42.00000 2 2 2
#> 18 0.200 5.00 9.500000 1.2000000 10.7 10.4 120.00000 2 2 2
#> 19 1.410 17.50 4.800000 1.3000000 6.1 34.0 90.02483 1 2 1
#> 20 60.000 81.00 12.000000 6.1000000 18.1 7.0 118.77264 1 1 1
#> 21 529.000 680.00 2.100612 0.3000000 NA 28.0 400.00000 5 5 5
#> 22 27.660 115.00 3.300000 0.5000000 3.8 20.0 148.00000 5 5 5
#> 23 0.120 1.00 11.000000 3.4000000 14.4 3.9 16.00000 3 1 2
#> 24 207.000 406.00 5.839931 1.5919796 12.0 39.3 252.00000 1 4 1
#> 25 85.000 325.00 4.700000 1.5000000 6.2 41.0 310.00000 1 3 1
#> 26 36.330 119.50 3.302149 0.4998185 13.0 16.2 63.00000 1 1 1
#> 27 0.101 4.00 10.400000 3.4000000 13.8 9.0 28.00000 5 1 3
#> 28 1.040 5.50 7.400000 0.8000000 8.2 7.6 68.00000 5 3 4
#> 29 521.000 655.00 2.100000 0.8000000 2.9 46.0 336.00000 5 5 5
#> 30 100.000 157.00 4.058626 0.9688541 10.8 22.4 100.00000 1 1 1
#> 31 35.000 56.00 6.824033 4.0075836 NA 16.3 33.00000 3 5 4
#> 32 0.005 0.14 7.700000 1.4000000 9.1 2.6 21.50000 5 2 4
#> 33 0.010 0.25 17.900000 2.0000000 19.9 24.0 50.00000 1 1 1
#> 34 62.000 1320.00 6.100000 1.9000000 8.0 100.0 267.00000 1 1 1
#> 35 0.122 3.00 8.200000 2.4000000 10.6 NA 30.00000 2 1 1
#> 36 1.350 8.10 8.400000 2.8000000 11.2 NA 45.00000 3 1 3
#> 37 0.023 0.40 11.900000 1.3000000 13.2 3.2 19.00000 4 1 3
#> 38 0.048 0.33 10.800000 2.0000000 12.8 2.0 30.00000 4 1 3
#> 39 1.700 6.30 13.800000 5.6000000 19.4 5.0 12.00000 2 1 1
#> 40 3.500 10.80 14.300000 3.1000000 17.4 6.5 120.00000 2 1 1
#> 41 250.000 490.00 5.859960 1.0000000 NA 23.6 440.00000 5 5 5
#> 42 0.480 15.50 15.200000 1.8000000 17.0 12.0 140.00000 2 2 2
#> 43 10.000 115.00 10.000000 0.9000000 10.9 20.2 170.00000 4 4 4
#> 44 1.620 11.40 11.900000 1.8000000 13.7 13.0 17.00000 2 1 2
#> 45 192.000 180.00 6.500000 1.9000000 8.4 27.0 115.00000 4 4 4
#> 46 2.500 12.10 7.500000 0.9000000 8.4 18.0 31.00000 5 5 5
#> 47 4.288 39.20 7.402361 2.4003341 12.5 13.7 63.00000 2 2 2
#> 48 0.280 1.90 10.600000 2.6000000 13.2 4.7 21.00000 3 1 3
#> 49 4.235 50.40 7.400000 2.4000000 9.8 9.8 52.00000 1 1 1
#> 50 6.800 179.00 8.400000 1.2000000 9.6 29.0 164.00000 2 3 2
#> 51 0.750 12.30 5.700000 0.9000000 6.6 7.0 225.00000 2 2 2
#> 52 3.600 21.00 4.900000 0.5000000 5.4 6.0 225.00000 3 2 3
#> 53 14.830 98.20 8.954710 0.6781464 2.6 17.0 150.00000 5 5 5
#> 54 55.500 175.00 3.200000 0.6000000 3.8 20.0 151.00000 5 5 5
#> 55 1.400 12.50 3.977688 1.0527972 11.0 12.7 90.00000 2 2 2
#> 56 0.060 1.00 8.100000 2.2000000 10.3 3.5 19.44205 3 1 2
#> 57 0.900 2.60 11.000000 2.3000000 13.3 4.5 60.00000 2 1 2
#> 58 2.000 12.30 4.900000 0.5000000 5.4 7.5 200.00000 3 1 3
#> 59 0.104 2.50 13.200000 2.6000000 15.8 2.3 46.00000 3 2 2
#> 60 4.190 58.00 9.700000 0.6000000 10.3 24.0 210.00000 4 3 4
#> 61 3.500 3.90 12.800000 6.6000000 19.4 3.0 14.00000 2 1 1
#> 62 4.050 17.00 4.717938 0.5567985 NA 13.0 38.00000 3 1 1
#> Dream_imp NonD_imp Gest_imp
#> 1 TRUE TRUE FALSE
#> 2 FALSE FALSE FALSE
#> 3 TRUE TRUE FALSE
#> 4 TRUE TRUE FALSE
#> 5 FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE
#> 11 FALSE FALSE FALSE
#> 12 FALSE FALSE FALSE
#> 13 FALSE FALSE TRUE
#> 14 TRUE TRUE FALSE
#> 15 FALSE FALSE FALSE
#> 16 FALSE FALSE FALSE
#> 17 FALSE FALSE FALSE
#> 18 FALSE FALSE FALSE
#> 19 FALSE FALSE TRUE
#> 20 FALSE FALSE TRUE
#> 21 FALSE TRUE FALSE
#> 22 FALSE FALSE FALSE
#> 23 FALSE FALSE FALSE
#> 24 TRUE TRUE FALSE
#> 25 FALSE FALSE FALSE
#> 26 TRUE TRUE FALSE
#> 27 FALSE FALSE FALSE
#> 28 FALSE FALSE FALSE
#> 29 FALSE FALSE FALSE
#> 30 TRUE TRUE FALSE
#> 31 TRUE TRUE FALSE
#> 32 FALSE FALSE FALSE
#> 33 FALSE FALSE FALSE
#> 34 FALSE FALSE FALSE
#> 35 FALSE FALSE FALSE
#> 36 FALSE FALSE FALSE
#> 37 FALSE FALSE FALSE
#> 38 FALSE FALSE FALSE
#> 39 FALSE FALSE FALSE
#> 40 FALSE FALSE FALSE
#> 41 FALSE TRUE FALSE
#> 42 FALSE FALSE FALSE
#> 43 FALSE FALSE FALSE
#> 44 FALSE FALSE FALSE
#> 45 FALSE FALSE FALSE
#> 46 FALSE FALSE FALSE
#> 47 TRUE TRUE FALSE
#> 48 FALSE FALSE FALSE
#> 49 FALSE FALSE FALSE
#> 50 FALSE FALSE FALSE
#> 51 FALSE FALSE FALSE
#> 52 FALSE FALSE FALSE
#> 53 TRUE TRUE FALSE
#> 54 FALSE FALSE FALSE
#> 55 TRUE TRUE FALSE
#> 56 FALSE FALSE TRUE
#> 57 FALSE FALSE FALSE
#> 58 FALSE FALSE FALSE
#> 59 FALSE FALSE FALSE
#> 60 FALSE FALSE FALSE
#> 61 FALSE FALSE FALSE
#> 62 TRUE TRUE FALSE
sleepx <- sleep
sleepx$Pred <- as.factor(LETTERS[sleepx$Pred])
sleepx$Pred[1] <- NA
xgboostImpute(Pred~BodyWgt+BrainWgt,data=sleepx)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger Pred_imp
#> 1 6654.000 5712.00 NA NA 3.3 38.6 645.0 E 5 3 TRUE
#> 2 1.000 6.60 6.3 2.0 8.3 4.5 42.0 C 1 3 FALSE
#> 3 3.385 44.50 NA NA 12.5 14.0 60.0 A 1 1 FALSE
#> 4 0.920 5.70 NA NA 16.5 NA 25.0 E 2 3 FALSE
#> 5 2547.000 4603.00 2.1 1.8 3.9 69.0 624.0 C 5 4 FALSE
#> 6 10.550 179.50 9.1 0.7 9.8 27.0 180.0 D 4 4 FALSE
#> 7 0.023 0.30 15.8 3.9 19.7 19.0 35.0 A 1 1 FALSE
#> 8 160.000 169.00 5.2 1.0 6.2 30.4 392.0 D 5 4 FALSE
#> 9 3.300 25.60 10.9 3.6 14.5 28.0 63.0 A 2 1 FALSE
#> 10 52.160 440.00 8.3 1.4 9.7 50.0 230.0 A 1 1 FALSE
#> 11 0.425 6.40 11.0 1.5 12.5 7.0 112.0 E 4 4 FALSE
#> 12 465.000 423.00 3.2 0.7 3.9 30.0 281.0 E 5 5 FALSE
#> 13 0.550 2.40 7.6 2.7 10.3 NA NA B 1 2 FALSE
#> 14 187.100 419.00 NA NA 3.1 40.0 365.0 E 5 5 FALSE
#> 15 0.075 1.20 6.3 2.1 8.4 3.5 42.0 A 1 1 FALSE
#> 16 3.000 25.00 8.6 0.0 8.6 50.0 28.0 B 2 2 FALSE
#> 17 0.785 3.50 6.6 4.1 10.7 6.0 42.0 B 2 2 FALSE
#> 18 0.200 5.00 9.5 1.2 10.7 10.4 120.0 B 2 2 FALSE
#> 19 1.410 17.50 4.8 1.3 6.1 34.0 NA A 2 1 FALSE
#> 20 60.000 81.00 12.0 6.1 18.1 7.0 NA A 1 1 FALSE
#> 21 529.000 680.00 NA 0.3 NA 28.0 400.0 E 5 5 FALSE
#> 22 27.660 115.00 3.3 0.5 3.8 20.0 148.0 E 5 5 FALSE
#> 23 0.120 1.00 11.0 3.4 14.4 3.9 16.0 C 1 2 FALSE
#> 24 207.000 406.00 NA NA 12.0 39.3 252.0 A 4 1 FALSE
#> 25 85.000 325.00 4.7 1.5 6.2 41.0 310.0 A 3 1 FALSE
#> 26 36.330 119.50 NA NA 13.0 16.2 63.0 A 1 1 FALSE
#> 27 0.101 4.00 10.4 3.4 13.8 9.0 28.0 E 1 3 FALSE
#> 28 1.040 5.50 7.4 0.8 8.2 7.6 68.0 E 3 4 FALSE
#> 29 521.000 655.00 2.1 0.8 2.9 46.0 336.0 E 5 5 FALSE
#> 30 100.000 157.00 NA NA 10.8 22.4 100.0 A 1 1 FALSE
#> 31 35.000 56.00 NA NA NA 16.3 33.0 C 5 4 FALSE
#> 32 0.005 0.14 7.7 1.4 9.1 2.6 21.5 E 2 4 FALSE
#> 33 0.010 0.25 17.9 2.0 19.9 24.0 50.0 A 1 1 FALSE
#> 34 62.000 1320.00 6.1 1.9 8.0 100.0 267.0 A 1 1 FALSE
#> 35 0.122 3.00 8.2 2.4 10.6 NA 30.0 B 1 1 FALSE
#> 36 1.350 8.10 8.4 2.8 11.2 NA 45.0 C 1 3 FALSE
#> 37 0.023 0.40 11.9 1.3 13.2 3.2 19.0 D 1 3 FALSE
#> 38 0.048 0.33 10.8 2.0 12.8 2.0 30.0 D 1 3 FALSE
#> 39 1.700 6.30 13.8 5.6 19.4 5.0 12.0 B 1 1 FALSE
#> 40 3.500 10.80 14.3 3.1 17.4 6.5 120.0 B 1 1 FALSE
#> 41 250.000 490.00 NA 1.0 NA 23.6 440.0 E 5 5 FALSE
#> 42 0.480 15.50 15.2 1.8 17.0 12.0 140.0 B 2 2 FALSE
#> 43 10.000 115.00 10.0 0.9 10.9 20.2 170.0 D 4 4 FALSE
#> 44 1.620 11.40 11.9 1.8 13.7 13.0 17.0 B 1 2 FALSE
#> 45 192.000 180.00 6.5 1.9 8.4 27.0 115.0 D 4 4 FALSE
#> 46 2.500 12.10 7.5 0.9 8.4 18.0 31.0 E 5 5 FALSE
#> 47 4.288 39.20 NA NA 12.5 13.7 63.0 B 2 2 FALSE
#> 48 0.280 1.90 10.6 2.6 13.2 4.7 21.0 C 1 3 FALSE
#> 49 4.235 50.40 7.4 2.4 9.8 9.8 52.0 A 1 1 FALSE
#> 50 6.800 179.00 8.4 1.2 9.6 29.0 164.0 B 3 2 FALSE
#> 51 0.750 12.30 5.7 0.9 6.6 7.0 225.0 B 2 2 FALSE
#> 52 3.600 21.00 4.9 0.5 5.4 6.0 225.0 C 2 3 FALSE
#> 53 14.830 98.20 NA NA 2.6 17.0 150.0 E 5 5 FALSE
#> 54 55.500 175.00 3.2 0.6 3.8 20.0 151.0 E 5 5 FALSE
#> 55 1.400 12.50 NA NA 11.0 12.7 90.0 B 2 2 FALSE
#> 56 0.060 1.00 8.1 2.2 10.3 3.5 NA C 1 2 FALSE
#> 57 0.900 2.60 11.0 2.3 13.3 4.5 60.0 B 1 2 FALSE
#> 58 2.000 12.30 4.9 0.5 5.4 7.5 200.0 C 1 3 FALSE
#> 59 0.104 2.50 13.2 2.6 15.8 2.3 46.0 C 2 2 FALSE
#> 60 4.190 58.00 9.7 0.6 10.3 24.0 210.0 D 3 4 FALSE
#> 61 3.500 3.90 12.8 6.6 19.4 3.0 14.0 B 1 1 FALSE
#> 62 4.050 17.00 NA NA NA 13.0 38.0 C 1 1 FALSE