Impute missing values based on random-forest models via vimpute().
rangerImpute(
formula,
data,
imp_var = TRUE,
imp_suffix = "imp",
...,
verbose = FALSE,
median = FALSE,
edit_rules = NULL,
edit_control = 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
Additional ranger hyperparameters (e.g. num.trees, mtry,
min.node.size), forwarded to the ranger learner via vimpute().
Show the number of observations used for training and evaluating the RF-Model.
TRUE/FALSE. If TRUE, ranger regression predictions are
aggregated tree-wise using the median (via vimpute()).
Optional model-agnostic validator, applied jointly to the
assembled imputed LHS columns after all target fits; see vimpute().
Control object or named list; see vimpute_edit_control().
the imputed data set.
Other imputation methods:
hotdeck(),
impPCA(),
imputeCellEM(),
imputeCellIRMI(),
imputeCellM(),
imputeCellMCD(),
imputeCellwise(),
imputeRobust(),
imputeRobustChain(),
irmi(),
kNN(),
matchImpute(),
medianSamp(),
regressionImp(),
sampleCat(),
vimmi,
vimpute(),
xgboostImpute()
data(sleep)
# rangerImpute() runs on vimpute(), which VIM backs with the suggested
# mlr3 stack -- skip the example where that stack is not installed.
if (requireNamespace("mlr3", quietly = TRUE) &&
requireNamespace("mlr3learners", quietly = TRUE) &&
requireNamespace("mlr3pipelines", quietly = TRUE)) {
rangerImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)
}
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger Dream_imp
#> 1 6654.000 5712.00 3.1 1.4 3.3 38.6 645.0 3 5 3 TRUE
#> 2 1.000 6.60 6.3 2.0 8.3 4.5 42.0 3 1 3 FALSE
#> 3 3.385 44.50 9.3 2.6 12.5 14.0 60.0 1 1 1 TRUE
#> 4 0.920 5.70 8.4 2.0 16.5 NA 25.0 5 2 3 TRUE
#> 5 2547.000 4603.00 2.1 1.8 3.9 69.0 624.0 3 5 4 FALSE
#> 6 10.550 179.50 9.1 0.7 9.8 27.0 180.0 4 4 4 FALSE
#> 7 0.023 0.30 15.8 3.9 19.7 19.0 35.0 1 1 1 FALSE
#> 8 160.000 169.00 5.2 1.0 6.2 30.4 392.0 4 5 4 FALSE
#> 9 3.300 25.60 10.9 3.6 14.5 28.0 63.0 1 2 1 FALSE
#> 10 52.160 440.00 8.3 1.4 9.7 50.0 230.0 1 1 1 FALSE
#> 11 0.425 6.40 11.0 1.5 12.5 7.0 112.0 5 4 4 FALSE
#> 12 465.000 423.00 3.2 0.7 3.9 30.0 281.0 5 5 5 FALSE
#> 13 0.550 2.40 7.6 2.7 10.3 NA NA 2 1 2 FALSE
#> 14 187.100 419.00 5.1 1.3 3.1 40.0 365.0 5 5 5 TRUE
#> 15 0.075 1.20 6.3 2.1 8.4 3.5 42.0 1 1 1 FALSE
#> 16 3.000 25.00 8.6 0.0 8.6 50.0 28.0 2 2 2 FALSE
#> 17 0.785 3.50 6.6 4.1 10.7 6.0 42.0 2 2 2 FALSE
#> 18 0.200 5.00 9.5 1.2 10.7 10.4 120.0 2 2 2 FALSE
#> 19 1.410 17.50 4.8 1.3 6.1 34.0 NA 1 2 1 FALSE
#> 20 60.000 81.00 12.0 6.1 18.1 7.0 NA 1 1 1 FALSE
#> 21 529.000 680.00 3.4 0.3 NA 28.0 400.0 5 5 5 FALSE
#> 22 27.660 115.00 3.3 0.5 3.8 20.0 148.0 5 5 5 FALSE
#> 23 0.120 1.00 11.0 3.4 14.4 3.9 16.0 3 1 2 FALSE
#> 24 207.000 406.00 5.2 1.3 12.0 39.3 252.0 1 4 1 TRUE
#> 25 85.000 325.00 4.7 1.5 6.2 41.0 310.0 1 3 1 FALSE
#> 26 36.330 119.50 6.1 1.0 13.0 16.2 63.0 1 1 1 TRUE
#> 27 0.101 4.00 10.4 3.4 13.8 9.0 28.0 5 1 3 FALSE
#> 28 1.040 5.50 7.4 0.8 8.2 7.6 68.0 5 3 4 FALSE
#> 29 521.000 655.00 2.1 0.8 2.9 46.0 336.0 5 5 5 FALSE
#> 30 100.000 157.00 5.3 1.5 10.8 22.4 100.0 1 1 1 TRUE
#> 31 35.000 56.00 8.0 2.2 NA 16.3 33.0 3 5 4 TRUE
#> 32 0.005 0.14 7.7 1.4 9.1 2.6 21.5 5 2 4 FALSE
#> 33 0.010 0.25 17.9 2.0 19.9 24.0 50.0 1 1 1 FALSE
#> 34 62.000 1320.00 6.1 1.9 8.0 100.0 267.0 1 1 1 FALSE
#> 35 0.122 3.00 8.2 2.4 10.6 NA 30.0 2 1 1 FALSE
#> 36 1.350 8.10 8.4 2.8 11.2 NA 45.0 3 1 3 FALSE
#> 37 0.023 0.40 11.9 1.3 13.2 3.2 19.0 4 1 3 FALSE
#> 38 0.048 0.33 10.8 2.0 12.8 2.0 30.0 4 1 3 FALSE
#> 39 1.700 6.30 13.8 5.6 19.4 5.0 12.0 2 1 1 FALSE
#> 40 3.500 10.80 14.3 3.1 17.4 6.5 120.0 2 1 1 FALSE
#> 41 250.000 490.00 5.4 1.0 NA 23.6 440.0 5 5 5 FALSE
#> 42 0.480 15.50 15.2 1.8 17.0 12.0 140.0 2 2 2 FALSE
#> 43 10.000 115.00 10.0 0.9 10.9 20.2 170.0 4 4 4 FALSE
#> 44 1.620 11.40 11.9 1.8 13.7 13.0 17.0 2 1 2 FALSE
#> 45 192.000 180.00 6.5 1.9 8.4 27.0 115.0 4 4 4 FALSE
#> 46 2.500 12.10 7.5 0.9 8.4 18.0 31.0 5 5 5 FALSE
#> 47 4.288 39.20 8.6 2.0 12.5 13.7 63.0 2 2 2 TRUE
#> 48 0.280 1.90 10.6 2.6 13.2 4.7 21.0 3 1 3 FALSE
#> 49 4.235 50.40 7.4 2.4 9.8 9.8 52.0 1 1 1 FALSE
#> 50 6.800 179.00 8.4 1.2 9.6 29.0 164.0 2 3 2 FALSE
#> 51 0.750 12.30 5.7 0.9 6.6 7.0 225.0 2 2 2 FALSE
#> 52 3.600 21.00 4.9 0.5 5.4 6.0 225.0 3 2 3 FALSE
#> 53 14.830 98.20 8.7 0.9 2.6 17.0 150.0 5 5 5 TRUE
#> 54 55.500 175.00 3.2 0.6 3.8 20.0 151.0 5 5 5 FALSE
#> 55 1.400 12.50 6.4 1.3 11.0 12.7 90.0 2 2 2 TRUE
#> 56 0.060 1.00 8.1 2.2 10.3 3.5 NA 3 1 2 FALSE
#> 57 0.900 2.60 11.0 2.3 13.3 4.5 60.0 2 1 2 FALSE
#> 58 2.000 12.30 4.9 0.5 5.4 7.5 200.0 3 1 3 FALSE
#> 59 0.104 2.50 13.2 2.6 15.8 2.3 46.0 3 2 2 FALSE
#> 60 4.190 58.00 9.7 0.6 10.3 24.0 210.0 4 3 4 FALSE
#> 61 3.500 3.90 12.8 6.6 19.4 3.0 14.0 2 1 1 FALSE
#> 62 4.050 17.00 7.6 1.3 NA 13.0 38.0 3 1 1 TRUE
#> NonD_imp
#> 1 TRUE
#> 2 FALSE
#> 3 TRUE
#> 4 TRUE
#> 5 FALSE
#> 6 FALSE
#> 7 FALSE
#> 8 FALSE
#> 9 FALSE
#> 10 FALSE
#> 11 FALSE
#> 12 FALSE
#> 13 FALSE
#> 14 TRUE
#> 15 FALSE
#> 16 FALSE
#> 17 FALSE
#> 18 FALSE
#> 19 FALSE
#> 20 FALSE
#> 21 TRUE
#> 22 FALSE
#> 23 FALSE
#> 24 TRUE
#> 25 FALSE
#> 26 TRUE
#> 27 FALSE
#> 28 FALSE
#> 29 FALSE
#> 30 TRUE
#> 31 TRUE
#> 32 FALSE
#> 33 FALSE
#> 34 FALSE
#> 35 FALSE
#> 36 FALSE
#> 37 FALSE
#> 38 FALSE
#> 39 FALSE
#> 40 FALSE
#> 41 TRUE
#> 42 FALSE
#> 43 FALSE
#> 44 FALSE
#> 45 FALSE
#> 46 FALSE
#> 47 TRUE
#> 48 FALSE
#> 49 FALSE
#> 50 FALSE
#> 51 FALSE
#> 52 FALSE
#> 53 TRUE
#> 54 FALSE
#> 55 TRUE
#> 56 FALSE
#> 57 FALSE
#> 58 FALSE
#> 59 FALSE
#> 60 FALSE
#> 61 FALSE
#> 62 TRUE