Multiple imputations to fill the missing data. Non missing independent variables are used to approximate a missing observations for a dependent variable. Quantitative models were built under Rcpp packages and the C++ library Armadillo.
fill_NA_N(
x,
model,
posit_y,
posit_x,
w = NULL,
logreg = FALSE,
k = 10,
ridge = 1e-06
)
# S3 method for class 'data.frame'
fill_NA_N(
x,
model,
posit_y,
posit_x,
w = NULL,
logreg = FALSE,
k = 10,
ridge = 1e-06
)
# S3 method for class 'data.table'
fill_NA_N(
x,
model,
posit_y,
posit_x,
w = NULL,
logreg = FALSE,
k = 10,
ridge = 1e-06
)
# S3 method for class 'matrix'
fill_NA_N(
x,
model,
posit_y,
posit_x,
w = NULL,
logreg = FALSE,
k = 10,
ridge = 1e-06
)a numeric matrix or data.frame/data.table (factor/character/numeric/logical) - variables
a character - possible options ("lm_bayes","lm_noise","pmm")
an integer/character - a position/name of dependent variable
an integer/character vector - positions/names of independent variables
a numeric vector - a weighting variable - only positive values, Default: NULL
a boolean - if dependent variable has log-normal distribution (numeric). If TRUE log-regression is evaluated and then returned exponential of results., Default: FALSE
an integer - a number of multiple imputations or for pmm a number of closest points from which a one random value is taken, Default:10
a numeric - a value added to diagonal elements of the x'x matrix, Default: 1e-6
load imputations in a numeric/character/factor (similar to the input type) vector format
fill_NA_N(data.frame): s3 method for data.frame
fill_NA_N(data.table): S3 method for data.table
fill_NA_N(matrix): S3 method for matrix
It is assumed that users add the intercept column themselves.
The miceFast module provides the most efficient environment; the second recommended option is data.table with a numeric matrix.
Only "lm_bayes", "lm_noise", and "pmm" models are supported.
The model is fitted only when the number of complete observations exceeds the number of independent variables.
library(miceFast)
library(dplyr)
library(data.table)
data(air_miss)
# dplyr: PMM with 20 draws
air_miss %>%
mutate(Ozone_pmm = fill_NA_N(
x = ., model = "pmm",
posit_y = "Ozone", posit_x = c("Solar.R", "Wind", "Temp"),
k = 20
))
#> Ozone Solar.R Wind Temp Day Intercept index weights groups x_character
#> 1 41 190 7.4 67 1 1 1 1.0186350 5 (140,210]
#> 2 36 118 8.0 72 2 1 2 1.0107583 5 (70,140]
#> 3 12 149 12.6 74 3 1 3 0.9891023 5 (140,210]
#> 4 18 313 11.5 62 4 1 4 0.9913450 5 (280,350]
#> 5 NA NA 14.3 56 5 1 5 0.9945367 5 <NA>
#> 6 28 NA 14.9 66 6 1 6 1.0088464 5 <NA>
#> 7 23 299 8.6 65 7 1 7 0.9933102 5 (280,350]
#> 8 19 99 13.8 59 8 1 8 0.9964602 5 (70,140]
#> 9 8 19 20.1 61 9 1 9 1.0180674 5 (0,70]
#> 10 NA 194 8.6 69 10 1 10 0.9950548 5 (140,210]
#> 11 7 NA 6.9 74 11 1 11 1.0137543 5 <NA>
#> 12 16 256 9.7 69 12 1 12 0.9862474 5 (210,280]
#> 13 11 290 9.2 66 13 1 13 0.9995110 5 (280,350]
#> 14 14 274 10.9 68 14 1 14 1.0037145 5 (210,280]
#> 15 18 65 13.2 58 15 1 15 0.9964222 5 (0,70]
#> 16 14 334 11.5 64 16 1 16 0.9898265 5 (280,350]
#> 17 34 307 12.0 66 17 1 17 1.0022897 5 (280,350]
#> 18 6 78 18.4 57 18 1 18 1.0012367 5 (70,140]
#> 19 30 322 11.5 68 19 1 19 1.0084019 5 (280,350]
#> 20 11 44 9.7 62 20 1 20 0.9877371 5 (0,70]
#> 21 1 8 9.7 59 21 1 21 0.9975105 5 (0,70]
#> 22 11 320 16.6 73 22 1 22 0.9993213 5 (280,350]
#> 23 4 25 9.7 61 23 1 23 0.9949510 5 (0,70]
#> 24 32 92 12.0 61 24 1 24 1.0121339 5 (70,140]
#> 25 NA 66 16.6 57 25 1 25 1.0124095 5 (0,70]
#> 26 NA 266 14.9 58 26 1 26 1.0047912 5 (210,280]
#> 27 NA NA 8.0 57 27 1 27 0.9998296 5 <NA>
#> 28 23 13 12.0 67 28 1 28 0.9901172 5 (0,70]
#> 29 45 252 14.9 81 29 1 29 1.0197179 5 (210,280]
#> 30 115 223 5.7 79 30 1 30 1.0060722 5 (210,280]
#> 31 37 279 7.4 76 31 1 31 1.0080169 5 (210,280]
#> 32 NA 286 8.6 78 1 1 32 1.0121752 6 (280,350]
#> 33 NA 287 9.7 74 2 1 33 0.9958874 6 (280,350]
#> 34 NA 242 16.1 67 3 1 34 0.9978362 6 (210,280]
#> 35 NA 186 9.2 84 4 1 35 0.9940910 6 (140,210]
#> 36 NA 220 8.6 85 5 1 36 1.0046951 6 (210,280]
#> 37 NA 264 14.3 79 6 1 37 1.0066960 6 (210,280]
#> 38 29 127 9.7 82 7 1 38 0.9993627 6 (70,140]
#> 39 NA 273 6.9 87 8 1 39 1.0002943 6 (210,280]
#> 40 71 291 13.8 90 9 1 40 0.9968199 6 (280,350]
#> 41 39 323 11.5 87 10 1 41 1.0056739 6 (280,350]
#> 42 NA 259 10.9 93 11 1 42 0.9976106 6 (210,280]
#> 43 NA 250 9.2 92 12 1 43 1.0102772 6 (210,280]
#> 44 23 148 8.0 82 13 1 44 1.0037450 6 (140,210]
#> 45 NA 332 13.8 80 14 1 45 1.0085720 6 (280,350]
#> 46 NA 322 11.5 79 15 1 46 1.0039686 6 (280,350]
#> 47 21 191 14.9 77 16 1 47 1.0039100 6 (140,210]
#> 48 37 284 20.7 72 17 1 48 0.9826485 6 (280,350]
#> 49 20 37 9.2 65 18 1 49 1.0094902 6 (0,70]
#> 50 12 120 11.5 73 19 1 50 0.9970971 6 (70,140]
#> 51 13 137 10.3 76 20 1 51 0.9952965 6 (70,140]
#> 52 NA 150 6.3 77 21 1 52 0.9990534 6 (140,210]
#> 53 NA 59 1.7 76 22 1 53 0.9897811 6 (0,70]
#> 54 NA 91 4.6 76 23 1 54 0.9877399 6 (70,140]
#> 55 NA 250 6.3 76 24 1 55 1.0068612 6 (210,280]
#> 56 NA 135 8.0 75 25 1 56 0.9949438 6 (70,140]
#> 57 NA 127 8.0 78 26 1 57 1.0101760 6 (70,140]
#> 58 NA 47 10.3 73 27 1 58 1.0028999 6 (0,70]
#> 59 NA 98 11.5 80 28 1 59 1.0022141 6 (70,140]
#> 60 NA 31 14.9 77 29 1 60 0.9993376 6 (0,70]
#> 61 NA 138 8.0 83 30 1 61 1.0075570 6 (70,140]
#> 62 135 269 4.1 84 1 1 62 1.0020979 7 (210,280]
#> 63 49 248 9.2 85 2 1 63 0.9998938 7 (210,280]
#> 64 32 236 9.2 81 3 1 64 1.0004625 7 (210,280]
#> 65 NA 101 10.9 84 4 1 65 0.9827890 7 (70,140]
#> 66 64 175 4.6 83 5 1 66 0.9935117 7 (140,210]
#> 67 40 314 10.9 83 6 1 67 0.9978889 7 (280,350]
#> 68 77 276 5.1 88 7 1 68 1.0117213 7 (210,280]
#> 69 97 267 6.3 92 8 1 69 1.0030677 7 (210,280]
#> 70 97 272 5.7 92 9 1 70 0.9887215 7 (210,280]
#> 71 85 175 7.4 89 10 1 71 0.9944119 7 (140,210]
#> 72 NA 139 8.6 82 11 1 72 1.0045765 7 (70,140]
#> 73 10 264 14.3 73 12 1 73 0.9998116 7 (210,280]
#> 74 27 175 14.9 81 13 1 74 1.0046056 7 (140,210]
#> 75 NA 291 14.9 91 14 1 75 1.0153114 7 (280,350]
#> 76 7 48 14.3 80 15 1 76 1.0051227 7 (0,70]
#> 77 48 260 6.9 81 16 1 77 1.0076395 7 (210,280]
#> 78 35 274 10.3 82 17 1 78 1.0144368 7 (210,280]
#> 79 61 285 6.3 84 18 1 79 1.0047472 7 (280,350]
#> 80 79 187 5.1 87 19 1 80 0.9952804 7 (140,210]
#> 81 63 220 11.5 85 20 1 81 1.0015820 7 (210,280]
#> 82 16 7 6.9 74 21 1 82 0.9938580 7 (0,70]
#> 83 NA 258 9.7 81 22 1 83 0.9992910 7 (210,280]
#> 84 NA 295 11.5 82 23 1 84 0.9902536 7 (280,350]
#> 85 80 294 8.6 86 24 1 85 0.9919899 7 (280,350]
#> 86 108 223 8.0 85 25 1 86 1.0068377 7 (210,280]
#> 87 20 81 8.6 82 26 1 87 0.9953272 7 (70,140]
#> 88 52 82 12.0 86 27 1 88 1.0210192 7 (70,140]
#> 89 82 213 7.4 88 28 1 89 1.0027222 7 (210,280]
#> 90 50 275 7.4 86 29 1 90 1.0046704 7 (210,280]
#> 91 64 253 7.4 83 30 1 91 1.0008055 7 (210,280]
#> 92 59 254 9.2 81 31 1 92 1.0069694 7 (210,280]
#> 93 39 83 6.9 81 1 1 93 1.0025879 8 (70,140]
#> 94 9 24 13.8 81 2 1 94 0.9977274 8 (0,70]
#> 95 16 77 7.4 82 3 1 95 1.0052109 8 (70,140]
#> 96 78 NA 6.9 86 4 1 96 0.9780448 8 <NA>
#> 97 35 NA 7.4 85 5 1 97 0.9885361 8 <NA>
#> 98 66 NA 4.6 87 6 1 98 0.9786223 8 <NA>
#> 99 122 255 4.0 89 7 1 99 1.0007045 8 (210,280]
#> 100 89 229 10.3 90 8 1 100 0.9965138 8 (210,280]
#> 101 110 207 8.0 90 9 1 101 1.0000343 8 (140,210]
#> 102 NA 222 8.6 92 10 1 102 0.9949271 8 (210,280]
#> 103 NA 137 11.5 86 11 1 103 0.9995715 8 (70,140]
#> 104 44 192 11.5 86 12 1 104 1.0010441 8 (140,210]
#> 105 28 273 11.5 82 13 1 105 1.0180383 8 (210,280]
#> 106 65 157 9.7 80 14 1 106 1.0116379 8 (140,210]
#> 107 NA 64 11.5 79 15 1 107 0.9996890 8 (0,70]
#> 108 22 71 10.3 77 16 1 108 1.0103102 8 (70,140]
#> 109 59 51 6.3 79 17 1 109 1.0040485 8 (0,70]
#> 110 23 115 7.4 76 18 1 110 1.0125059 8 (70,140]
#> 111 31 244 10.9 78 19 1 111 0.9907597 8 (210,280]
#> 112 44 190 10.3 78 20 1 112 1.0016896 8 (140,210]
#> 113 21 259 15.5 77 21 1 113 1.0040223 8 (210,280]
#> 114 9 36 14.3 72 22 1 114 0.9997726 8 (0,70]
#> 115 NA 255 12.6 75 23 1 115 0.9946828 8 (210,280]
#> 116 45 212 9.7 79 24 1 116 0.9796297 8 (210,280]
#> 117 168 238 3.4 81 25 1 117 1.0134912 8 (210,280]
#> 118 73 215 8.0 86 26 1 118 0.9943290 8 (210,280]
#> 119 NA 153 5.7 88 27 1 119 0.9947362 8 (140,210]
#> 120 76 203 9.7 97 28 1 120 1.0202685 8 (140,210]
#> 121 118 225 2.3 94 29 1 121 0.9915690 8 (210,280]
#> 122 84 237 6.3 96 30 1 122 1.0045993 8 (210,280]
#> 123 85 188 6.3 94 31 1 123 0.9997329 8 (140,210]
#> 124 96 167 6.9 91 1 1 124 0.9912182 9 (140,210]
#> 125 78 197 5.1 92 2 1 125 0.9945931 9 (140,210]
#> 126 73 183 2.8 93 3 1 126 0.9908711 9 (140,210]
#> 127 91 189 4.6 93 4 1 127 1.0046064 9 (140,210]
#> 128 47 95 7.4 87 5 1 128 1.0122550 9 (70,140]
#> 129 32 92 15.5 84 6 1 129 0.9870982 9 (70,140]
#> 130 20 252 10.9 80 7 1 130 0.9920913 9 (210,280]
#> 131 23 220 10.3 78 8 1 131 1.0118155 9 (210,280]
#> 132 21 230 10.9 75 9 1 132 0.9948104 9 (210,280]
#> 133 24 259 9.7 73 10 1 133 0.9900645 9 (210,280]
#> 134 44 236 14.9 81 11 1 134 0.9902580 9 (210,280]
#> 135 21 259 15.5 76 12 1 135 1.0146258 9 (210,280]
#> 136 28 238 6.3 77 13 1 136 1.0108690 9 (210,280]
#> 137 9 24 10.9 71 14 1 137 0.9860623 9 (0,70]
#> 138 13 112 11.5 71 15 1 138 1.0082647 9 (70,140]
#> 139 46 237 6.9 78 16 1 139 1.0173902 9 (210,280]
#> 140 18 224 13.8 67 17 1 140 0.9848134 9 (210,280]
#> 141 13 27 10.3 76 18 1 141 0.9990203 9 (0,70]
#> 142 24 238 10.3 68 19 1 142 1.0047265 9 (210,280]
#> 143 16 201 8.0 82 20 1 143 1.0091025 9 (140,210]
#> 144 13 238 12.6 64 21 1 144 0.9877918 9 (210,280]
#> 145 23 14 9.2 71 22 1 145 1.0007646 9 (0,70]
#> 146 36 139 10.3 81 23 1 146 0.9880016 9 (70,140]
#> 147 7 49 10.3 69 24 1 147 0.9926906 9 (0,70]
#> 148 14 20 16.6 63 25 1 148 1.0072197 9 (0,70]
#> 149 30 193 6.9 70 26 1 149 0.9985280 9 (140,210]
#> 150 NA 145 13.2 77 27 1 150 1.0001786 9 (140,210]
#> 151 14 191 14.3 75 28 1 151 1.0024673 9 (140,210]
#> 152 18 131 8.0 76 29 1 152 0.9968826 9 (70,140]
#> 153 20 223 11.5 68 30 1 153 1.0056592 9 (210,280]
#> Ozone_chac Ozone_f Ozone_high Ozone_pmm
#> 1 (40,60] (40,60] FALSE 41
#> 2 (20,40] (20,40] FALSE 36
#> 3 (0,20] (0,20] FALSE 12
#> 4 (0,20] (0,20] FALSE 18
#> 5 <NA> <NA> NA NA
#> 6 (20,40] (20,40] FALSE 28
#> 7 (20,40] (20,40] FALSE 23
#> 8 (0,20] (0,20] FALSE 19
#> 9 (0,20] (0,20] FALSE 8
#> 10 <NA> <NA> NA 110
#> 11 (0,20] (0,20] FALSE 7
#> 12 (0,20] (0,20] FALSE 16
#> 13 (0,20] (0,20] FALSE 11
#> 14 (0,20] (0,20] FALSE 14
#> 15 (0,20] (0,20] FALSE 18
#> 16 (0,20] (0,20] FALSE 14
#> 17 (20,40] (20,40] FALSE 34
#> 18 (0,20] (0,20] FALSE 6
#> 19 (20,40] (20,40] FALSE 30
#> 20 (0,20] (0,20] FALSE 11
#> 21 (0,20] (0,20] FALSE 1
#> 22 (0,20] (0,20] FALSE 11
#> 23 (0,20] (0,20] FALSE 4
#> 24 (20,40] (20,40] FALSE 32
#> 25 <NA> <NA> NA 21
#> 26 <NA> <NA> NA 1
#> 27 <NA> <NA> NA NA
#> 28 (20,40] (20,40] FALSE 23
#> 29 (40,60] (40,60] TRUE 45
#> 30 (100,120] (100,120] TRUE 115
#> 31 (20,40] (20,40] FALSE 37
#> 32 <NA> <NA> NA 23
#> 33 <NA> <NA> NA 40
#> 34 <NA> <NA> NA 21
#> 35 <NA> <NA> NA 18
#> 36 <NA> <NA> NA 28
#> 37 <NA> <NA> NA 16
#> 38 (20,40] (20,40] FALSE 29
#> 39 <NA> <NA> NA 96
#> 40 (60,80] (60,80] TRUE 71
#> 41 (20,40] (20,40] FALSE 39
#> 42 <NA> <NA> NA 16
#> 43 <NA> <NA> NA 52
#> 44 (20,40] (20,40] FALSE 23
#> 45 <NA> <NA> NA 32
#> 46 <NA> <NA> NA 39
#> 47 (20,40] (20,40] FALSE 21
#> 48 (20,40] (20,40] FALSE 37
#> 49 (0,20] (0,20] FALSE 20
#> 50 (0,20] (0,20] FALSE 12
#> 51 (0,20] (0,20] FALSE 13
#> 52 <NA> <NA> NA 108
#> 53 <NA> <NA> NA 118
#> 54 <NA> <NA> NA 29
#> 55 <NA> <NA> NA 79
#> 56 <NA> <NA> NA 23
#> 57 <NA> <NA> NA 21
#> 58 <NA> <NA> NA 11
#> 59 <NA> <NA> NA 18
#> 60 <NA> <NA> NA 21
#> 61 <NA> <NA> NA 59
#> 62 (120,140] (120,140] TRUE 135
#> 63 (40,60] (40,60] TRUE 49
#> 64 (20,40] (20,40] FALSE 32
#> 65 <NA> <NA> NA 24
#> 66 (60,80] (60,80] TRUE 64
#> 67 (20,40] (20,40] FALSE 40
#> 68 (60,80] (60,80] TRUE 77
#> 69 (80,100] (80,100] TRUE 97
#> 70 (80,100] (80,100] TRUE 97
#> 71 (80,100] (80,100] TRUE 85
#> 72 <NA> <NA> NA 23
#> 73 (0,20] (0,20] FALSE 10
#> 74 (20,40] (20,40] FALSE 27
#> 75 <NA> <NA> NA 14
#> 76 (0,20] (0,20] FALSE 7
#> 77 (40,60] (40,60] TRUE 48
#> 78 (20,40] (20,40] FALSE 35
#> 79 (60,80] (60,80] TRUE 61
#> 80 (60,80] (60,80] TRUE 79
#> 81 (60,80] (60,80] TRUE 63
#> 82 (0,20] (0,20] FALSE 16
#> 83 <NA> <NA> NA 12
#> 84 <NA> <NA> NA 23
#> 85 (60,80] (60,80] TRUE 80
#> 86 (100,120] (100,120] TRUE 108
#> 87 (0,20] (0,20] FALSE 20
#> 88 (40,60] (40,60] TRUE 52
#> 89 (80,100] (80,100] TRUE 82
#> 90 (40,60] (40,60] TRUE 50
#> 91 (60,80] (60,80] TRUE 64
#> 92 (40,60] (40,60] TRUE 59
#> 93 (20,40] (20,40] FALSE 39
#> 94 (0,20] (0,20] FALSE 9
#> 95 (0,20] (0,20] FALSE 16
#> 96 (60,80] (60,80] TRUE 78
#> 97 (20,40] (20,40] FALSE 35
#> 98 (60,80] (60,80] TRUE 66
#> 99 (120,140] (120,140] TRUE 122
#> 100 (80,100] (80,100] TRUE 89
#> 101 (100,120] (100,120] TRUE 110
#> 102 <NA> <NA> NA 50
#> 103 <NA> <NA> NA 91
#> 104 (40,60] (40,60] TRUE 44
#> 105 (20,40] (20,40] FALSE 28
#> 106 (60,80] (60,80] TRUE 65
#> 107 <NA> <NA> NA 13
#> 108 (20,40] (20,40] FALSE 22
#> 109 (40,60] (40,60] TRUE 59
#> 110 (20,40] (20,40] FALSE 23
#> 111 (20,40] (20,40] FALSE 31
#> 112 (40,60] (40,60] TRUE 44
#> 113 (20,40] (20,40] FALSE 21
#> 114 (0,20] (0,20] FALSE 9
#> 115 <NA> <NA> NA 21
#> 116 (40,60] (40,60] TRUE 45
#> 117 <NA> <NA> TRUE 168
#> 118 (60,80] (60,80] TRUE 73
#> 119 <NA> <NA> NA 73
#> 120 (60,80] (60,80] TRUE 76
#> 121 (100,120] (100,120] TRUE 118
#> 122 (80,100] (80,100] TRUE 84
#> 123 (80,100] (80,100] TRUE 85
#> 124 (80,100] (80,100] TRUE 96
#> 125 (60,80] (60,80] TRUE 78
#> 126 (60,80] (60,80] TRUE 73
#> 127 (80,100] (80,100] TRUE 91
#> 128 (40,60] (40,60] TRUE 47
#> 129 (20,40] (20,40] FALSE 32
#> 130 (0,20] (0,20] FALSE 20
#> 131 (20,40] (20,40] FALSE 23
#> 132 (20,40] (20,40] FALSE 21
#> 133 (20,40] (20,40] FALSE 24
#> 134 (40,60] (40,60] TRUE 44
#> 135 (20,40] (20,40] FALSE 21
#> 136 (20,40] (20,40] FALSE 28
#> 137 (0,20] (0,20] FALSE 9
#> 138 (0,20] (0,20] FALSE 13
#> 139 (40,60] (40,60] TRUE 46
#> 140 (0,20] (0,20] FALSE 18
#> 141 (0,20] (0,20] FALSE 13
#> 142 (20,40] (20,40] FALSE 24
#> 143 (0,20] (0,20] FALSE 16
#> 144 (0,20] (0,20] FALSE 13
#> 145 (20,40] (20,40] FALSE 23
#> 146 (20,40] (20,40] FALSE 36
#> 147 (0,20] (0,20] FALSE 7
#> 148 (0,20] (0,20] FALSE 14
#> 149 (20,40] (20,40] FALSE 30
#> 150 <NA> <NA> NA 21
#> 151 (0,20] (0,20] FALSE 14
#> 152 (0,20] (0,20] FALSE 18
#> 153 (0,20] (0,20] FALSE 20
# dplyr: lm_noise with weights
air_miss %>%
mutate(Ozone_imp = fill_NA_N(
x = ., model = "lm_noise",
posit_y = "Ozone",
posit_x = c("Solar.R", "Wind", "Temp"),
w = .[["weights"]],
logreg = TRUE, k = 30
))
#> Ozone Solar.R Wind Temp Day Intercept index weights groups x_character
#> 1 41 190 7.4 67 1 1 1 1.0186350 5 (140,210]
#> 2 36 118 8.0 72 2 1 2 1.0107583 5 (70,140]
#> 3 12 149 12.6 74 3 1 3 0.9891023 5 (140,210]
#> 4 18 313 11.5 62 4 1 4 0.9913450 5 (280,350]
#> 5 NA NA 14.3 56 5 1 5 0.9945367 5 <NA>
#> 6 28 NA 14.9 66 6 1 6 1.0088464 5 <NA>
#> 7 23 299 8.6 65 7 1 7 0.9933102 5 (280,350]
#> 8 19 99 13.8 59 8 1 8 0.9964602 5 (70,140]
#> 9 8 19 20.1 61 9 1 9 1.0180674 5 (0,70]
#> 10 NA 194 8.6 69 10 1 10 0.9950548 5 (140,210]
#> 11 7 NA 6.9 74 11 1 11 1.0137543 5 <NA>
#> 12 16 256 9.7 69 12 1 12 0.9862474 5 (210,280]
#> 13 11 290 9.2 66 13 1 13 0.9995110 5 (280,350]
#> 14 14 274 10.9 68 14 1 14 1.0037145 5 (210,280]
#> 15 18 65 13.2 58 15 1 15 0.9964222 5 (0,70]
#> 16 14 334 11.5 64 16 1 16 0.9898265 5 (280,350]
#> 17 34 307 12.0 66 17 1 17 1.0022897 5 (280,350]
#> 18 6 78 18.4 57 18 1 18 1.0012367 5 (70,140]
#> 19 30 322 11.5 68 19 1 19 1.0084019 5 (280,350]
#> 20 11 44 9.7 62 20 1 20 0.9877371 5 (0,70]
#> 21 1 8 9.7 59 21 1 21 0.9975105 5 (0,70]
#> 22 11 320 16.6 73 22 1 22 0.9993213 5 (280,350]
#> 23 4 25 9.7 61 23 1 23 0.9949510 5 (0,70]
#> 24 32 92 12.0 61 24 1 24 1.0121339 5 (70,140]
#> 25 NA 66 16.6 57 25 1 25 1.0124095 5 (0,70]
#> 26 NA 266 14.9 58 26 1 26 1.0047912 5 (210,280]
#> 27 NA NA 8.0 57 27 1 27 0.9998296 5 <NA>
#> 28 23 13 12.0 67 28 1 28 0.9901172 5 (0,70]
#> 29 45 252 14.9 81 29 1 29 1.0197179 5 (210,280]
#> 30 115 223 5.7 79 30 1 30 1.0060722 5 (210,280]
#> 31 37 279 7.4 76 31 1 31 1.0080169 5 (210,280]
#> 32 NA 286 8.6 78 1 1 32 1.0121752 6 (280,350]
#> 33 NA 287 9.7 74 2 1 33 0.9958874 6 (280,350]
#> 34 NA 242 16.1 67 3 1 34 0.9978362 6 (210,280]
#> 35 NA 186 9.2 84 4 1 35 0.9940910 6 (140,210]
#> 36 NA 220 8.6 85 5 1 36 1.0046951 6 (210,280]
#> 37 NA 264 14.3 79 6 1 37 1.0066960 6 (210,280]
#> 38 29 127 9.7 82 7 1 38 0.9993627 6 (70,140]
#> 39 NA 273 6.9 87 8 1 39 1.0002943 6 (210,280]
#> 40 71 291 13.8 90 9 1 40 0.9968199 6 (280,350]
#> 41 39 323 11.5 87 10 1 41 1.0056739 6 (280,350]
#> 42 NA 259 10.9 93 11 1 42 0.9976106 6 (210,280]
#> 43 NA 250 9.2 92 12 1 43 1.0102772 6 (210,280]
#> 44 23 148 8.0 82 13 1 44 1.0037450 6 (140,210]
#> 45 NA 332 13.8 80 14 1 45 1.0085720 6 (280,350]
#> 46 NA 322 11.5 79 15 1 46 1.0039686 6 (280,350]
#> 47 21 191 14.9 77 16 1 47 1.0039100 6 (140,210]
#> 48 37 284 20.7 72 17 1 48 0.9826485 6 (280,350]
#> 49 20 37 9.2 65 18 1 49 1.0094902 6 (0,70]
#> 50 12 120 11.5 73 19 1 50 0.9970971 6 (70,140]
#> 51 13 137 10.3 76 20 1 51 0.9952965 6 (70,140]
#> 52 NA 150 6.3 77 21 1 52 0.9990534 6 (140,210]
#> 53 NA 59 1.7 76 22 1 53 0.9897811 6 (0,70]
#> 54 NA 91 4.6 76 23 1 54 0.9877399 6 (70,140]
#> 55 NA 250 6.3 76 24 1 55 1.0068612 6 (210,280]
#> 56 NA 135 8.0 75 25 1 56 0.9949438 6 (70,140]
#> 57 NA 127 8.0 78 26 1 57 1.0101760 6 (70,140]
#> 58 NA 47 10.3 73 27 1 58 1.0028999 6 (0,70]
#> 59 NA 98 11.5 80 28 1 59 1.0022141 6 (70,140]
#> 60 NA 31 14.9 77 29 1 60 0.9993376 6 (0,70]
#> 61 NA 138 8.0 83 30 1 61 1.0075570 6 (70,140]
#> 62 135 269 4.1 84 1 1 62 1.0020979 7 (210,280]
#> 63 49 248 9.2 85 2 1 63 0.9998938 7 (210,280]
#> 64 32 236 9.2 81 3 1 64 1.0004625 7 (210,280]
#> 65 NA 101 10.9 84 4 1 65 0.9827890 7 (70,140]
#> 66 64 175 4.6 83 5 1 66 0.9935117 7 (140,210]
#> 67 40 314 10.9 83 6 1 67 0.9978889 7 (280,350]
#> 68 77 276 5.1 88 7 1 68 1.0117213 7 (210,280]
#> 69 97 267 6.3 92 8 1 69 1.0030677 7 (210,280]
#> 70 97 272 5.7 92 9 1 70 0.9887215 7 (210,280]
#> 71 85 175 7.4 89 10 1 71 0.9944119 7 (140,210]
#> 72 NA 139 8.6 82 11 1 72 1.0045765 7 (70,140]
#> 73 10 264 14.3 73 12 1 73 0.9998116 7 (210,280]
#> 74 27 175 14.9 81 13 1 74 1.0046056 7 (140,210]
#> 75 NA 291 14.9 91 14 1 75 1.0153114 7 (280,350]
#> 76 7 48 14.3 80 15 1 76 1.0051227 7 (0,70]
#> 77 48 260 6.9 81 16 1 77 1.0076395 7 (210,280]
#> 78 35 274 10.3 82 17 1 78 1.0144368 7 (210,280]
#> 79 61 285 6.3 84 18 1 79 1.0047472 7 (280,350]
#> 80 79 187 5.1 87 19 1 80 0.9952804 7 (140,210]
#> 81 63 220 11.5 85 20 1 81 1.0015820 7 (210,280]
#> 82 16 7 6.9 74 21 1 82 0.9938580 7 (0,70]
#> 83 NA 258 9.7 81 22 1 83 0.9992910 7 (210,280]
#> 84 NA 295 11.5 82 23 1 84 0.9902536 7 (280,350]
#> 85 80 294 8.6 86 24 1 85 0.9919899 7 (280,350]
#> 86 108 223 8.0 85 25 1 86 1.0068377 7 (210,280]
#> 87 20 81 8.6 82 26 1 87 0.9953272 7 (70,140]
#> 88 52 82 12.0 86 27 1 88 1.0210192 7 (70,140]
#> 89 82 213 7.4 88 28 1 89 1.0027222 7 (210,280]
#> 90 50 275 7.4 86 29 1 90 1.0046704 7 (210,280]
#> 91 64 253 7.4 83 30 1 91 1.0008055 7 (210,280]
#> 92 59 254 9.2 81 31 1 92 1.0069694 7 (210,280]
#> 93 39 83 6.9 81 1 1 93 1.0025879 8 (70,140]
#> 94 9 24 13.8 81 2 1 94 0.9977274 8 (0,70]
#> 95 16 77 7.4 82 3 1 95 1.0052109 8 (70,140]
#> 96 78 NA 6.9 86 4 1 96 0.9780448 8 <NA>
#> 97 35 NA 7.4 85 5 1 97 0.9885361 8 <NA>
#> 98 66 NA 4.6 87 6 1 98 0.9786223 8 <NA>
#> 99 122 255 4.0 89 7 1 99 1.0007045 8 (210,280]
#> 100 89 229 10.3 90 8 1 100 0.9965138 8 (210,280]
#> 101 110 207 8.0 90 9 1 101 1.0000343 8 (140,210]
#> 102 NA 222 8.6 92 10 1 102 0.9949271 8 (210,280]
#> 103 NA 137 11.5 86 11 1 103 0.9995715 8 (70,140]
#> 104 44 192 11.5 86 12 1 104 1.0010441 8 (140,210]
#> 105 28 273 11.5 82 13 1 105 1.0180383 8 (210,280]
#> 106 65 157 9.7 80 14 1 106 1.0116379 8 (140,210]
#> 107 NA 64 11.5 79 15 1 107 0.9996890 8 (0,70]
#> 108 22 71 10.3 77 16 1 108 1.0103102 8 (70,140]
#> 109 59 51 6.3 79 17 1 109 1.0040485 8 (0,70]
#> 110 23 115 7.4 76 18 1 110 1.0125059 8 (70,140]
#> 111 31 244 10.9 78 19 1 111 0.9907597 8 (210,280]
#> 112 44 190 10.3 78 20 1 112 1.0016896 8 (140,210]
#> 113 21 259 15.5 77 21 1 113 1.0040223 8 (210,280]
#> 114 9 36 14.3 72 22 1 114 0.9997726 8 (0,70]
#> 115 NA 255 12.6 75 23 1 115 0.9946828 8 (210,280]
#> 116 45 212 9.7 79 24 1 116 0.9796297 8 (210,280]
#> 117 168 238 3.4 81 25 1 117 1.0134912 8 (210,280]
#> 118 73 215 8.0 86 26 1 118 0.9943290 8 (210,280]
#> 119 NA 153 5.7 88 27 1 119 0.9947362 8 (140,210]
#> 120 76 203 9.7 97 28 1 120 1.0202685 8 (140,210]
#> 121 118 225 2.3 94 29 1 121 0.9915690 8 (210,280]
#> 122 84 237 6.3 96 30 1 122 1.0045993 8 (210,280]
#> 123 85 188 6.3 94 31 1 123 0.9997329 8 (140,210]
#> 124 96 167 6.9 91 1 1 124 0.9912182 9 (140,210]
#> 125 78 197 5.1 92 2 1 125 0.9945931 9 (140,210]
#> 126 73 183 2.8 93 3 1 126 0.9908711 9 (140,210]
#> 127 91 189 4.6 93 4 1 127 1.0046064 9 (140,210]
#> 128 47 95 7.4 87 5 1 128 1.0122550 9 (70,140]
#> 129 32 92 15.5 84 6 1 129 0.9870982 9 (70,140]
#> 130 20 252 10.9 80 7 1 130 0.9920913 9 (210,280]
#> 131 23 220 10.3 78 8 1 131 1.0118155 9 (210,280]
#> 132 21 230 10.9 75 9 1 132 0.9948104 9 (210,280]
#> 133 24 259 9.7 73 10 1 133 0.9900645 9 (210,280]
#> 134 44 236 14.9 81 11 1 134 0.9902580 9 (210,280]
#> 135 21 259 15.5 76 12 1 135 1.0146258 9 (210,280]
#> 136 28 238 6.3 77 13 1 136 1.0108690 9 (210,280]
#> 137 9 24 10.9 71 14 1 137 0.9860623 9 (0,70]
#> 138 13 112 11.5 71 15 1 138 1.0082647 9 (70,140]
#> 139 46 237 6.9 78 16 1 139 1.0173902 9 (210,280]
#> 140 18 224 13.8 67 17 1 140 0.9848134 9 (210,280]
#> 141 13 27 10.3 76 18 1 141 0.9990203 9 (0,70]
#> 142 24 238 10.3 68 19 1 142 1.0047265 9 (210,280]
#> 143 16 201 8.0 82 20 1 143 1.0091025 9 (140,210]
#> 144 13 238 12.6 64 21 1 144 0.9877918 9 (210,280]
#> 145 23 14 9.2 71 22 1 145 1.0007646 9 (0,70]
#> 146 36 139 10.3 81 23 1 146 0.9880016 9 (70,140]
#> 147 7 49 10.3 69 24 1 147 0.9926906 9 (0,70]
#> 148 14 20 16.6 63 25 1 148 1.0072197 9 (0,70]
#> 149 30 193 6.9 70 26 1 149 0.9985280 9 (140,210]
#> 150 NA 145 13.2 77 27 1 150 1.0001786 9 (140,210]
#> 151 14 191 14.3 75 28 1 151 1.0024673 9 (140,210]
#> 152 18 131 8.0 76 29 1 152 0.9968826 9 (70,140]
#> 153 20 223 11.5 68 30 1 153 1.0056592 9 (210,280]
#> Ozone_chac Ozone_f Ozone_high Ozone_imp
#> 1 (40,60] (40,60] FALSE 41.000000
#> 2 (20,40] (20,40] FALSE 36.000000
#> 3 (0,20] (0,20] FALSE 12.000000
#> 4 (0,20] (0,20] FALSE 18.000000
#> 5 <NA> <NA> NA NA
#> 6 (20,40] (20,40] FALSE 28.000000
#> 7 (20,40] (20,40] FALSE 23.000000
#> 8 (0,20] (0,20] FALSE 19.000000
#> 9 (0,20] (0,20] FALSE 8.000000
#> 10 <NA> <NA> NA 20.230008
#> 11 (0,20] (0,20] FALSE 7.000000
#> 12 (0,20] (0,20] FALSE 16.000000
#> 13 (0,20] (0,20] FALSE 11.000000
#> 14 (0,20] (0,20] FALSE 14.000000
#> 15 (0,20] (0,20] FALSE 18.000000
#> 16 (0,20] (0,20] FALSE 14.000000
#> 17 (20,40] (20,40] FALSE 34.000000
#> 18 (0,20] (0,20] FALSE 6.000000
#> 19 (20,40] (20,40] FALSE 30.000000
#> 20 (0,20] (0,20] FALSE 11.000000
#> 21 (0,20] (0,20] FALSE 1.000000
#> 22 (0,20] (0,20] FALSE 11.000000
#> 23 (0,20] (0,20] FALSE 4.000000
#> 24 (20,40] (20,40] FALSE 32.000000
#> 25 <NA> <NA> NA 6.139656
#> 26 <NA> <NA> NA 9.527552
#> 27 <NA> <NA> NA NA
#> 28 (20,40] (20,40] FALSE 23.000000
#> 29 (40,60] (40,60] TRUE 45.000000
#> 30 (100,120] (100,120] TRUE 115.000000
#> 31 (20,40] (20,40] FALSE 37.000000
#> 32 <NA> <NA> NA 37.907856
#> 33 <NA> <NA> NA 36.414466
#> 34 <NA> <NA> NA 14.242672
#> 35 <NA> <NA> NA 37.946011
#> 36 <NA> <NA> NA 54.995630
#> 37 <NA> <NA> NA 32.404855
#> 38 (20,40] (20,40] FALSE 29.000000
#> 39 <NA> <NA> NA 74.522731
#> 40 (60,80] (60,80] TRUE 71.000000
#> 41 (20,40] (20,40] FALSE 39.000000
#> 42 <NA> <NA> NA 66.801551
#> 43 <NA> <NA> NA 89.472204
#> 44 (20,40] (20,40] FALSE 23.000000
#> 45 <NA> <NA> NA 40.026038
#> 46 <NA> <NA> NA 34.766945
#> 47 (20,40] (20,40] FALSE 21.000000
#> 48 (20,40] (20,40] FALSE 37.000000
#> 49 (0,20] (0,20] FALSE 20.000000
#> 50 (0,20] (0,20] FALSE 12.000000
#> 51 (0,20] (0,20] FALSE 13.000000
#> 52 <NA> <NA> NA 30.744434
#> 53 <NA> <NA> NA 29.706460
#> 54 <NA> <NA> NA 29.379999
#> 55 <NA> <NA> NA 45.034269
#> 56 <NA> <NA> NA 27.343695
#> 57 <NA> <NA> NA 30.991078
#> 58 <NA> <NA> NA 15.995600
#> 59 <NA> <NA> NA 32.567271
#> 60 <NA> <NA> NA 14.812372
#> 61 <NA> <NA> NA 36.878057
#> 62 (120,140] (120,140] TRUE 135.000000
#> 63 (40,60] (40,60] TRUE 49.000000
#> 64 (20,40] (20,40] FALSE 32.000000
#> 65 <NA> <NA> NA 31.969254
#> 66 (60,80] (60,80] TRUE 64.000000
#> 67 (20,40] (20,40] FALSE 40.000000
#> 68 (60,80] (60,80] TRUE 77.000000
#> 69 (80,100] (80,100] TRUE 97.000000
#> 70 (80,100] (80,100] TRUE 97.000000
#> 71 (80,100] (80,100] TRUE 85.000000
#> 72 <NA> <NA> NA 36.748717
#> 73 (0,20] (0,20] FALSE 10.000000
#> 74 (20,40] (20,40] FALSE 27.000000
#> 75 <NA> <NA> NA 46.560554
#> 76 (0,20] (0,20] FALSE 7.000000
#> 77 (40,60] (40,60] TRUE 48.000000
#> 78 (20,40] (20,40] FALSE 35.000000
#> 79 (60,80] (60,80] TRUE 61.000000
#> 80 (60,80] (60,80] TRUE 79.000000
#> 81 (60,80] (60,80] TRUE 63.000000
#> 82 (0,20] (0,20] FALSE 16.000000
#> 83 <NA> <NA> NA 44.022068
#> 84 <NA> <NA> NA 45.097103
#> 85 (60,80] (60,80] TRUE 80.000000
#> 86 (100,120] (100,120] TRUE 108.000000
#> 87 (0,20] (0,20] FALSE 20.000000
#> 88 (40,60] (40,60] TRUE 52.000000
#> 89 (80,100] (80,100] TRUE 82.000000
#> 90 (40,60] (40,60] TRUE 50.000000
#> 91 (60,80] (60,80] TRUE 64.000000
#> 92 (40,60] (40,60] TRUE 59.000000
#> 93 (20,40] (20,40] FALSE 39.000000
#> 94 (0,20] (0,20] FALSE 9.000000
#> 95 (0,20] (0,20] FALSE 16.000000
#> 96 (60,80] (60,80] TRUE 78.000000
#> 97 (20,40] (20,40] FALSE 35.000000
#> 98 (60,80] (60,80] TRUE 66.000000
#> 99 (120,140] (120,140] TRUE 122.000000
#> 100 (80,100] (80,100] TRUE 89.000000
#> 101 (100,120] (100,120] TRUE 110.000000
#> 102 <NA> <NA> NA 65.786335
#> 103 <NA> <NA> NA 34.520176
#> 104 (40,60] (40,60] TRUE 44.000000
#> 105 (20,40] (20,40] FALSE 28.000000
#> 106 (60,80] (60,80] TRUE 65.000000
#> 107 <NA> <NA> NA 19.769399
#> 108 (20,40] (20,40] FALSE 22.000000
#> 109 (40,60] (40,60] TRUE 59.000000
#> 110 (20,40] (20,40] FALSE 23.000000
#> 111 (20,40] (20,40] FALSE 31.000000
#> 112 (40,60] (40,60] TRUE 44.000000
#> 113 (20,40] (20,40] FALSE 21.000000
#> 114 (0,20] (0,20] FALSE 9.000000
#> 115 <NA> <NA> NA 26.618954
#> 116 (40,60] (40,60] TRUE 45.000000
#> 117 <NA> <NA> TRUE 168.000000
#> 118 (60,80] (60,80] TRUE 73.000000
#> 119 <NA> <NA> NA 60.502027
#> 120 (60,80] (60,80] TRUE 76.000000
#> 121 (100,120] (100,120] TRUE 118.000000
#> 122 (80,100] (80,100] TRUE 84.000000
#> 123 (80,100] (80,100] TRUE 85.000000
#> 124 (80,100] (80,100] TRUE 96.000000
#> 125 (60,80] (60,80] TRUE 78.000000
#> 126 (60,80] (60,80] TRUE 73.000000
#> 127 (80,100] (80,100] TRUE 91.000000
#> 128 (40,60] (40,60] TRUE 47.000000
#> 129 (20,40] (20,40] FALSE 32.000000
#> 130 (0,20] (0,20] FALSE 20.000000
#> 131 (20,40] (20,40] FALSE 23.000000
#> 132 (20,40] (20,40] FALSE 21.000000
#> 133 (20,40] (20,40] FALSE 24.000000
#> 134 (40,60] (40,60] TRUE 44.000000
#> 135 (20,40] (20,40] FALSE 21.000000
#> 136 (20,40] (20,40] FALSE 28.000000
#> 137 (0,20] (0,20] FALSE 9.000000
#> 138 (0,20] (0,20] FALSE 13.000000
#> 139 (40,60] (40,60] TRUE 46.000000
#> 140 (0,20] (0,20] FALSE 18.000000
#> 141 (0,20] (0,20] FALSE 13.000000
#> 142 (20,40] (20,40] FALSE 24.000000
#> 143 (0,20] (0,20] FALSE 16.000000
#> 144 (0,20] (0,20] FALSE 13.000000
#> 145 (20,40] (20,40] FALSE 23.000000
#> 146 (20,40] (20,40] FALSE 36.000000
#> 147 (0,20] (0,20] FALSE 7.000000
#> 148 (0,20] (0,20] FALSE 14.000000
#> 149 (20,40] (20,40] FALSE 30.000000
#> 150 <NA> <NA> NA 20.928118
#> 151 (0,20] (0,20] FALSE 14.000000
#> 152 (0,20] (0,20] FALSE 18.000000
#> 153 (0,20] (0,20] FALSE 20.000000
# data.table: PMM grouped
data(air_miss)
setDT(air_miss)
air_miss[, Ozone_pmm := fill_NA_N(
x = .SD, model = "pmm",
posit_y = "Ozone",
posit_x = c("Wind", "Temp", "Intercept"),
k = 20
), by = .(groups)]
#> Ozone Solar.R Wind Temp Day Intercept index weights groups
#> <num> <num> <num> <num> <num> <num> <num> <num> <fctr>
#> 1: 41 190 7.4 67 1 1 1 1.0186350 5
#> 2: 36 118 8.0 72 2 1 2 1.0107583 5
#> 3: 12 149 12.6 74 3 1 3 0.9891023 5
#> 4: 18 313 11.5 62 4 1 4 0.9913450 5
#> 5: NA NA 14.3 56 5 1 5 0.9945367 5
#> ---
#> 149: 30 193 6.9 70 26 1 149 0.9985280 9
#> 150: NA 145 13.2 77 27 1 150 1.0001786 9
#> 151: 14 191 14.3 75 28 1 151 1.0024673 9
#> 152: 18 131 8.0 76 29 1 152 0.9968826 9
#> 153: 20 223 11.5 68 30 1 153 1.0056592 9
#> x_character Ozone_chac Ozone_f Ozone_high Ozone_pmm
#> <char> <char> <fctr> <lgcl> <num>
#> 1: (140,210] (40,60] (40,60] FALSE 41
#> 2: (70,140] (20,40] (20,40] FALSE 36
#> 3: (140,210] (0,20] (0,20] FALSE 12
#> 4: (280,350] (0,20] (0,20] FALSE 18
#> 5: <NA> <NA> <NA> NA 14
#> ---
#> 149: (140,210] (20,40] (20,40] FALSE 30
#> 150: (140,210] <NA> <NA> NA 28
#> 151: (140,210] (0,20] (0,20] FALSE 14
#> 152: (70,140] (0,20] (0,20] FALSE 18
#> 153: (210,280] (0,20] (0,20] FALSE 20
# See the vignette for full examples:
# vignette("miceFast-intro", package = "miceFast")