k-Nearest Neighbour Imputation based on a variation of the Gower Distance for numerical, categorical, ordered and semi-continous variables.
kNN(
data,
variable = colnames(data),
k = 5,
dist_var = colnames(data),
weights = NULL,
numFun = median,
catFun = maxCat,
makeNA = NULL,
NAcond = NULL,
impNA = TRUE,
donorcond = NULL,
mixed = vector(),
mixed.constant = NULL,
trace = FALSE,
imp_var = TRUE,
imp_suffix = "imp",
addRF = FALSE,
onlyRF = FALSE,
addRandom = FALSE,
useImputedDist = TRUE,
weightDist = FALSE,
methodStand = "range",
ordFun = medianSamp
)data.frame or matrix
variables where missing values should be imputed
number of Nearest Neighbours used
names or variables to be used for distance calculation
weights for the variables for distance calculation.
If weights = "auto" weights will be selected based on variable importance from random forest regression, using function ranger::ranger().
Weights are calculated for each variable seperately.
function for aggregating the k Nearest Neighbours in the case of a numerical variable
function for aggregating the k Nearest Neighbours in the case of a categorical variable
list of length equal to the number of variables, with values, that should be converted to NA for each variable
list of length equal to the number of variables, with a condition for imputing a NA
TRUE/FALSE whether NA should be imputed
list of length equal to the number of variables, with a donorcond condition as character string. e.g. a list element can be ">5" or c(">5","<10). If the list element for a variable is NULL no condition will be applied for this variable.
names of mixed variables
vector with length equal to the number of semi-continuous variables specifying the point of the semi-continuous distribution with non-zero probability
TRUE/FALSE if additional information about the imputation process should be printed
TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status
suffix for the TRUE/FALSE variables showing the imputation status
TRUE/FALSE each variable will be modelled using random forest regression (ranger::ranger()) and used as additional distance variable.
TRUE/FALSE if TRUE only additional distance variables created from random forest regression will be used as distance variables.
TRUE/FALSE if an additional random variable should be added for distance calculation
TRUE/FALSE if an imputed value should be used for distance calculation for imputing another variable. Be aware that this results in a dependency on the ordering of the variables.
TRUE/FALSE if the distances of the k nearest neighbours should be used as weights in the aggregation step
either "range" or "iqr" to be used in the standardization of numeric vaiables in the gower distance
function for aggregating the k Nearest Neighbours in the case of a ordered factor variable
the imputed data set.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16.
Other imputation methods:
hotdeck(),
impPCA(),
irmi(),
matchImpute(),
medianSamp(),
rangerImpute(),
regressionImp(),
sampleCat(),
xgboostImpute()
data(sleep)
kNN(sleep)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred Exp Danger BodyWgt_imp
#> 1 6654.000 5712.00 3.2 0.8 3.3 38.6 645.0 3 5 3 FALSE
#> 2 1.000 6.60 6.3 2.0 8.3 4.5 42.0 3 1 3 FALSE
#> 3 3.385 44.50 12.8 2.4 12.5 14.0 60.0 1 1 1 FALSE
#> 4 0.920 5.70 10.4 2.4 16.5 3.2 25.0 5 2 3 FALSE
#> 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 7.0 45.0 2 1 2 FALSE
#> 14 187.100 419.00 3.2 0.7 3.1 40.0 365.0 5 5 5 FALSE
#> 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 34.0 225.0 1 2 1 FALSE
#> 20 60.000 81.00 12.0 6.1 18.1 7.0 35.0 1 1 1 FALSE
#> 21 529.000 680.00 3.2 0.3 3.8 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 8.3 1.5 12.0 39.3 252.0 1 4 1 FALSE
#> 25 85.000 325.00 4.7 1.5 6.2 41.0 310.0 1 3 1 FALSE
#> 26 36.330 119.50 12.8 2.4 13.0 16.2 63.0 1 1 1 FALSE
#> 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 10.9 2.3 10.8 22.4 100.0 1 1 1 FALSE
#> 31 35.000 56.00 11.0 0.9 9.8 16.3 33.0 3 5 4 FALSE
#> 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 9.8 30.0 2 1 1 FALSE
#> 36 1.350 8.10 8.4 2.8 11.2 3.9 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 3.2 1.0 3.1 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 11.0 1.8 12.5 13.7 63.0 2 2 2 FALSE
#> 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 3.2 0.7 2.6 17.0 150.0 5 5 5 FALSE
#> 54 55.500 175.00 3.2 0.6 3.8 20.0 151.0 5 5 5 FALSE
#> 55 1.400 12.50 11.0 1.8 11.0 12.7 90.0 2 2 2 FALSE
#> 56 0.060 1.00 8.1 2.2 10.3 3.5 42.0 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 13.8 3.9 17.4 13.0 38.0 3 1 1 FALSE
#> BrainWgt_imp NonD_imp Dream_imp Sleep_imp Span_imp Gest_imp Pred_imp Exp_imp
#> 1 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 3 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 4 FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 12 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 13 FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> 14 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 15 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 16 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 17 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 18 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 19 FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> 20 FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> 21 FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> 22 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 23 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 24 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 25 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 26 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 27 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 28 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 29 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 30 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 31 FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> 32 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 33 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 34 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 35 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> 36 FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> 37 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 38 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 39 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 40 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 41 FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> 42 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 43 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 44 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 45 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 46 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 47 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 48 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 49 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 50 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 51 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 52 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 53 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 54 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 55 FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> 56 FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> 57 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 58 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 59 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 60 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 61 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 62 FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> Danger_imp
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 FALSE
#> 5 FALSE
#> 6 FALSE
#> 7 FALSE
#> 8 FALSE
#> 9 FALSE
#> 10 FALSE
#> 11 FALSE
#> 12 FALSE
#> 13 FALSE
#> 14 FALSE
#> 15 FALSE
#> 16 FALSE
#> 17 FALSE
#> 18 FALSE
#> 19 FALSE
#> 20 FALSE
#> 21 FALSE
#> 22 FALSE
#> 23 FALSE
#> 24 FALSE
#> 25 FALSE
#> 26 FALSE
#> 27 FALSE
#> 28 FALSE
#> 29 FALSE
#> 30 FALSE
#> 31 FALSE
#> 32 FALSE
#> 33 FALSE
#> 34 FALSE
#> 35 FALSE
#> 36 FALSE
#> 37 FALSE
#> 38 FALSE
#> 39 FALSE
#> 40 FALSE
#> 41 FALSE
#> 42 FALSE
#> 43 FALSE
#> 44 FALSE
#> 45 FALSE
#> 46 FALSE
#> 47 FALSE
#> 48 FALSE
#> 49 FALSE
#> 50 FALSE
#> 51 FALSE
#> 52 FALSE
#> 53 FALSE
#> 54 FALSE
#> 55 FALSE
#> 56 FALSE
#> 57 FALSE
#> 58 FALSE
#> 59 FALSE
#> 60 FALSE
#> 61 FALSE
#> 62 FALSE
library(laeken)
kNN(sleep, numFun = weightedMean, weightDist=TRUE)
#> BodyWgt BrainWgt NonD Dream Sleep Span Gest Pred
#> 1 6654.000 5712.00 3.621115 1.0244651 3.300000 38.600000 645.00000 3
#> 2 1.000 6.60 6.300000 2.0000000 8.300000 4.500000 42.00000 3
#> 3 3.385 44.50 12.130785 2.7984870 12.500000 14.000000 60.00000 1
#> 4 0.920 5.70 10.011620 2.4135093 16.500000 4.337617 25.00000 5
#> 5 2547.000 4603.00 2.100000 1.8000000 3.900000 69.000000 624.00000 3
#> 6 10.550 179.50 9.100000 0.7000000 9.800000 27.000000 180.00000 4
#> 7 0.023 0.30 15.800000 3.9000000 19.700000 19.000000 35.00000 1
#> 8 160.000 169.00 5.200000 1.0000000 6.200000 30.400000 392.00000 4
#> 9 3.300 25.60 10.900000 3.6000000 14.500000 28.000000 63.00000 1
#> 10 52.160 440.00 8.300000 1.4000000 9.700000 50.000000 230.00000 1
#> 11 0.425 6.40 11.000000 1.5000000 12.500000 7.000000 112.00000 5
#> 12 465.000 423.00 3.200000 0.7000000 3.900000 30.000000 281.00000 5
#> 13 0.550 2.40 7.600000 2.7000000 10.300000 12.348960 47.75702 2
#> 14 187.100 419.00 3.370754 0.7184306 3.100000 40.000000 365.00000 5
#> 15 0.075 1.20 6.300000 2.1000000 8.400000 3.500000 42.00000 1
#> 16 3.000 25.00 8.600000 0.0000000 8.600000 50.000000 28.00000 2
#> 17 0.785 3.50 6.600000 4.1000000 10.700000 6.000000 42.00000 2
#> 18 0.200 5.00 9.500000 1.2000000 10.700000 10.400000 120.00000 2
#> 19 1.410 17.50 4.800000 1.3000000 6.100000 34.000000 173.78512 1
#> 20 60.000 81.00 12.000000 6.1000000 18.100000 7.000000 48.10889 1
#> 21 529.000 680.00 3.389039 0.3000000 3.496601 28.000000 400.00000 5
#> 22 27.660 115.00 3.300000 0.5000000 3.800000 20.000000 148.00000 5
#> 23 0.120 1.00 11.000000 3.4000000 14.400000 3.900000 16.00000 3
#> 24 207.000 406.00 7.930240 2.0051906 12.000000 39.300000 252.00000 1
#> 25 85.000 325.00 4.700000 1.5000000 6.200000 41.000000 310.00000 1
#> 26 36.330 119.50 12.140941 2.8012782 13.000000 16.200000 63.00000 1
#> 27 0.101 4.00 10.400000 3.4000000 13.800000 9.000000 28.00000 5
#> 28 1.040 5.50 7.400000 0.8000000 8.200000 7.600000 68.00000 5
#> 29 521.000 655.00 2.100000 0.8000000 2.900000 46.000000 336.00000 5
#> 30 100.000 157.00 10.628498 2.3640501 10.800000 22.400000 100.00000 1
#> 31 35.000 56.00 10.683888 0.8764794 10.005650 16.300000 33.00000 3
#> 32 0.005 0.14 7.700000 1.4000000 9.100000 2.600000 21.50000 5
#> 33 0.010 0.25 17.900000 2.0000000 19.900000 24.000000 50.00000 1
#> 34 62.000 1320.00 6.100000 1.9000000 8.000000 100.000000 267.00000 1
#> 35 0.122 3.00 8.200000 2.4000000 10.600000 10.833181 30.00000 2
#> 36 1.350 8.10 8.400000 2.8000000 11.200000 3.673929 45.00000 3
#> 37 0.023 0.40 11.900000 1.3000000 13.200000 3.200000 19.00000 4
#> 38 0.048 0.33 10.800000 2.0000000 12.800000 2.000000 30.00000 4
#> 39 1.700 6.30 13.800000 5.6000000 19.400000 5.000000 12.00000 2
#> 40 3.500 10.80 14.300000 3.1000000 17.400000 6.500000 120.00000 2
#> 41 250.000 490.00 4.234588 1.0000000 3.261678 23.600000 440.00000 5
#> 42 0.480 15.50 15.200000 1.8000000 17.000000 12.000000 140.00000 2
#> 43 10.000 115.00 10.000000 0.9000000 10.900000 20.200000 170.00000 4
#> 44 1.620 11.40 11.900000 1.8000000 13.700000 13.000000 17.00000 2
#> 45 192.000 180.00 6.500000 1.9000000 8.400000 27.000000 115.00000 4
#> 46 2.500 12.10 7.500000 0.9000000 8.400000 18.000000 31.00000 5
#> 47 4.288 39.20 10.951784 2.2373985 12.500000 13.700000 63.00000 2
#> 48 0.280 1.90 10.600000 2.6000000 13.200000 4.700000 21.00000 3
#> 49 4.235 50.40 7.400000 2.4000000 9.800000 9.800000 52.00000 1
#> 50 6.800 179.00 8.400000 1.2000000 9.600000 29.000000 164.00000 2
#> 51 0.750 12.30 5.700000 0.9000000 6.600000 7.000000 225.00000 2
#> 52 3.600 21.00 4.900000 0.5000000 5.400000 6.000000 225.00000 3
#> 53 14.830 98.20 3.859326 0.6952781 2.600000 17.000000 150.00000 5
#> 54 55.500 175.00 3.200000 0.6000000 3.800000 20.000000 151.00000 5
#> 55 1.400 12.50 10.928583 2.2379005 11.000000 12.700000 90.00000 2
#> 56 0.060 1.00 8.100000 2.2000000 10.300000 3.500000 38.62720 3
#> 57 0.900 2.60 11.000000 2.3000000 13.300000 4.500000 60.00000 2
#> 58 2.000 12.30 4.900000 0.5000000 5.400000 7.500000 200.00000 3
#> 59 0.104 2.50 13.200000 2.6000000 15.800000 2.300000 46.00000 3
#> 60 4.190 58.00 9.700000 0.6000000 10.300000 24.000000 210.00000 4
#> 61 3.500 3.90 12.800000 6.6000000 19.400000 3.000000 14.00000 2
#> 62 4.050 17.00 13.542726 4.5441878 18.060387 13.000000 38.00000 3
#> Exp Danger BodyWgt_imp BrainWgt_imp NonD_imp Dream_imp Sleep_imp Span_imp
#> 1 5 3 FALSE FALSE TRUE TRUE FALSE FALSE
#> 2 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 3 1 1 FALSE FALSE TRUE TRUE FALSE FALSE
#> 4 2 3 FALSE FALSE TRUE TRUE FALSE TRUE
#> 5 5 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 6 4 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 7 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 8 5 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 9 2 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 10 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 11 4 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 12 5 5 FALSE FALSE FALSE FALSE FALSE FALSE
#> 13 1 2 FALSE FALSE FALSE FALSE FALSE TRUE
#> 14 5 5 FALSE FALSE TRUE TRUE FALSE FALSE
#> 15 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 16 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 17 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 18 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 19 2 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 20 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 21 5 5 FALSE FALSE TRUE FALSE TRUE FALSE
#> 22 5 5 FALSE FALSE FALSE FALSE FALSE FALSE
#> 23 1 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 24 4 1 FALSE FALSE TRUE TRUE FALSE FALSE
#> 25 3 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 26 1 1 FALSE FALSE TRUE TRUE FALSE FALSE
#> 27 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 28 3 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 29 5 5 FALSE FALSE FALSE FALSE FALSE FALSE
#> 30 1 1 FALSE FALSE TRUE TRUE FALSE FALSE
#> 31 5 4 FALSE FALSE TRUE TRUE TRUE FALSE
#> 32 2 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 33 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 34 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 35 1 1 FALSE FALSE FALSE FALSE FALSE TRUE
#> 36 1 3 FALSE FALSE FALSE FALSE FALSE TRUE
#> 37 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 38 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 39 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 40 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 41 5 5 FALSE FALSE TRUE FALSE TRUE FALSE
#> 42 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 43 4 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 44 1 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 45 4 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 46 5 5 FALSE FALSE FALSE FALSE FALSE FALSE
#> 47 2 2 FALSE FALSE TRUE TRUE FALSE FALSE
#> 48 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 49 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 50 3 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 51 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 52 2 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 53 5 5 FALSE FALSE TRUE TRUE FALSE FALSE
#> 54 5 5 FALSE FALSE FALSE FALSE FALSE FALSE
#> 55 2 2 FALSE FALSE TRUE TRUE FALSE FALSE
#> 56 1 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 57 1 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 58 1 3 FALSE FALSE FALSE FALSE FALSE FALSE
#> 59 2 2 FALSE FALSE FALSE FALSE FALSE FALSE
#> 60 3 4 FALSE FALSE FALSE FALSE FALSE FALSE
#> 61 1 1 FALSE FALSE FALSE FALSE FALSE FALSE
#> 62 1 1 FALSE FALSE TRUE TRUE TRUE FALSE
#> Gest_imp Pred_imp Exp_imp Danger_imp
#> 1 FALSE FALSE FALSE FALSE
#> 2 FALSE FALSE FALSE FALSE
#> 3 FALSE FALSE FALSE FALSE
#> 4 FALSE FALSE FALSE FALSE
#> 5 FALSE FALSE FALSE FALSE
#> 6 FALSE FALSE FALSE FALSE
#> 7 FALSE FALSE FALSE FALSE
#> 8 FALSE FALSE FALSE FALSE
#> 9 FALSE FALSE FALSE FALSE
#> 10 FALSE FALSE FALSE FALSE
#> 11 FALSE FALSE FALSE FALSE
#> 12 FALSE FALSE FALSE FALSE
#> 13 TRUE FALSE FALSE FALSE
#> 14 FALSE FALSE FALSE FALSE
#> 15 FALSE FALSE FALSE FALSE
#> 16 FALSE FALSE FALSE FALSE
#> 17 FALSE FALSE FALSE FALSE
#> 18 FALSE FALSE FALSE FALSE
#> 19 TRUE FALSE FALSE FALSE
#> 20 TRUE FALSE FALSE FALSE
#> 21 FALSE FALSE FALSE FALSE
#> 22 FALSE FALSE FALSE FALSE
#> 23 FALSE FALSE FALSE FALSE
#> 24 FALSE FALSE FALSE FALSE
#> 25 FALSE FALSE FALSE FALSE
#> 26 FALSE FALSE FALSE FALSE
#> 27 FALSE FALSE FALSE FALSE
#> 28 FALSE FALSE FALSE FALSE
#> 29 FALSE FALSE FALSE FALSE
#> 30 FALSE FALSE FALSE FALSE
#> 31 FALSE FALSE FALSE FALSE
#> 32 FALSE FALSE FALSE FALSE
#> 33 FALSE FALSE FALSE FALSE
#> 34 FALSE FALSE FALSE FALSE
#> 35 FALSE FALSE FALSE FALSE
#> 36 FALSE FALSE FALSE FALSE
#> 37 FALSE FALSE FALSE FALSE
#> 38 FALSE FALSE FALSE FALSE
#> 39 FALSE FALSE FALSE FALSE
#> 40 FALSE FALSE FALSE FALSE
#> 41 FALSE FALSE FALSE FALSE
#> 42 FALSE FALSE FALSE FALSE
#> 43 FALSE FALSE FALSE FALSE
#> 44 FALSE FALSE FALSE FALSE
#> 45 FALSE FALSE FALSE FALSE
#> 46 FALSE FALSE FALSE FALSE
#> 47 FALSE FALSE FALSE FALSE
#> 48 FALSE FALSE FALSE FALSE
#> 49 FALSE FALSE FALSE FALSE
#> 50 FALSE FALSE FALSE FALSE
#> 51 FALSE FALSE FALSE FALSE
#> 52 FALSE FALSE FALSE FALSE
#> 53 FALSE FALSE FALSE FALSE
#> 54 FALSE FALSE FALSE FALSE
#> 55 FALSE FALSE FALSE FALSE
#> 56 TRUE FALSE FALSE FALSE
#> 57 FALSE FALSE FALSE FALSE
#> 58 FALSE FALSE FALSE FALSE
#> 59 FALSE FALSE FALSE FALSE
#> 60 FALSE FALSE FALSE FALSE
#> 61 FALSE FALSE FALSE FALSE
#> 62 FALSE FALSE FALSE FALSE