Regular 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(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)
# S3 method for class 'data.frame'
fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)
# S3 method for class 'data.table'
fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)
# S3 method for class 'matrix'
fill_NA(x, model, posit_y, posit_x, w = NULL, logreg = FALSE, ridge = 1e-06)a numeric matrix or data.frame/data.table (factor/character/numeric/logical) - variables
a character - possible options ("lda","lm_pred","lm_bayes","lm_noise")
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
a numeric - a value added to diagonal elements of the x'x matrix, Default: 1e-6
load imputations in a numeric/logical/character/factor (similar to the input type) vector format
fill_NA(data.frame): S3 method for data.frame
fill_NA(data.table): s3 method for data.table
fill_NA(matrix): S3 method for matrix
There is assumed that users add the intercept by their own. The miceFast module provides the most efficient environment, the second recommended option is to use data.table and the numeric matrix data type. The lda model is assessed only if there are more than 15 complete observations and for the lms models if number of independent variables is smaller than number of observations.
library(miceFast)
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
library(data.table)
#>
#> Attaching package: ‘data.table’
#> The following objects are masked from ‘package:dplyr’:
#>
#> between, first, last
data(air_miss)
# dplyr: continuous variable with Bayesian linear model
air_miss %>%
mutate(Ozone_imp = fill_NA(
x = ., model = "lm_bayes",
posit_y = "Ozone", posit_x = c("Solar.R", "Wind", "Temp")
))
#> 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 31.124953
#> 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 -63.716975
#> 26 <NA> <NA> NA 14.310568
#> 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 24.987382
#> 33 <NA> <NA> NA 47.794673
#> 34 <NA> <NA> NA 24.201507
#> 35 <NA> <NA> NA 72.776342
#> 36 <NA> <NA> NA 64.607265
#> 37 <NA> <NA> NA 44.348397
#> 38 (20,40] (20,40] FALSE 29.000000
#> 39 <NA> <NA> NA 55.381153
#> 40 (60,80] (60,80] TRUE 71.000000
#> 41 (20,40] (20,40] FALSE 39.000000
#> 42 <NA> <NA> NA 39.614555
#> 43 <NA> <NA> NA 89.518648
#> 44 (20,40] (20,40] FALSE 23.000000
#> 45 <NA> <NA> NA 49.647783
#> 46 <NA> <NA> NA 40.016245
#> 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 50.310951
#> 53 <NA> <NA> NA 107.855017
#> 54 <NA> <NA> NA 98.714671
#> 55 <NA> <NA> NA 45.341713
#> 56 <NA> <NA> NA 29.479901
#> 57 <NA> <NA> NA 77.108448
#> 58 <NA> <NA> NA 31.494130
#> 59 <NA> <NA> NA 17.710995
#> 60 <NA> <NA> NA 3.467952
#> 61 <NA> <NA> NA 19.729031
#> 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 49.361794
#> 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 31.954823
#> 73 (0,20] (0,20] FALSE 10.000000
#> 74 (20,40] (20,40] FALSE 27.000000
#> 75 <NA> <NA> NA 47.292303
#> 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 51.585814
#> 84 <NA> <NA> NA 42.635715
#> 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 86.062910
#> 103 <NA> <NA> NA 45.822671
#> 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 31.096150
#> 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 29.801207
#> 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 83.070172
#> 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 9.602626
#> 151 (0,20] (0,20] FALSE 14.000000
#> 152 (0,20] (0,20] FALSE 18.000000
#> 153 (0,20] (0,20] FALSE 20.000000
# dplyr: categorical variable with LDA
air_miss %>%
mutate(x_char_imp = fill_NA(
x = ., model = "lda",
posit_y = "x_character", posit_x = c("Wind", "Temp")
))
#> 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 x_char_imp
#> 1 (40,60] (40,60] FALSE (140,210]
#> 2 (20,40] (20,40] FALSE (70,140]
#> 3 (0,20] (0,20] FALSE (140,210]
#> 4 (0,20] (0,20] FALSE (280,350]
#> 5 <NA> <NA> NA (0,70]
#> 6 (20,40] (20,40] FALSE (0,70]
#> 7 (20,40] (20,40] FALSE (280,350]
#> 8 (0,20] (0,20] FALSE (70,140]
#> 9 (0,20] (0,20] FALSE (0,70]
#> 10 <NA> <NA> NA (140,210]
#> 11 (0,20] (0,20] FALSE (210,280]
#> 12 (0,20] (0,20] FALSE (210,280]
#> 13 (0,20] (0,20] FALSE (280,350]
#> 14 (0,20] (0,20] FALSE (210,280]
#> 15 (0,20] (0,20] FALSE (0,70]
#> 16 (0,20] (0,20] FALSE (280,350]
#> 17 (20,40] (20,40] FALSE (280,350]
#> 18 (0,20] (0,20] FALSE (70,140]
#> 19 (20,40] (20,40] FALSE (280,350]
#> 20 (0,20] (0,20] FALSE (0,70]
#> 21 (0,20] (0,20] FALSE (0,70]
#> 22 (0,20] (0,20] FALSE (280,350]
#> 23 (0,20] (0,20] FALSE (0,70]
#> 24 (20,40] (20,40] FALSE (70,140]
#> 25 <NA> <NA> NA (0,70]
#> 26 <NA> <NA> NA (210,280]
#> 27 <NA> <NA> NA (0,70]
#> 28 (20,40] (20,40] FALSE (0,70]
#> 29 (40,60] (40,60] TRUE (210,280]
#> 30 (100,120] (100,120] TRUE (210,280]
#> 31 (20,40] (20,40] FALSE (210,280]
#> 32 <NA> <NA> NA (280,350]
#> 33 <NA> <NA> NA (280,350]
#> 34 <NA> <NA> NA (210,280]
#> 35 <NA> <NA> NA (140,210]
#> 36 <NA> <NA> NA (210,280]
#> 37 <NA> <NA> NA (210,280]
#> 38 (20,40] (20,40] FALSE (70,140]
#> 39 <NA> <NA> NA (210,280]
#> 40 (60,80] (60,80] TRUE (280,350]
#> 41 (20,40] (20,40] FALSE (280,350]
#> 42 <NA> <NA> NA (210,280]
#> 43 <NA> <NA> NA (210,280]
#> 44 (20,40] (20,40] FALSE (140,210]
#> 45 <NA> <NA> NA (280,350]
#> 46 <NA> <NA> NA (280,350]
#> 47 (20,40] (20,40] FALSE (140,210]
#> 48 (20,40] (20,40] FALSE (280,350]
#> 49 (0,20] (0,20] FALSE (0,70]
#> 50 (0,20] (0,20] FALSE (70,140]
#> 51 (0,20] (0,20] FALSE (70,140]
#> 52 <NA> <NA> NA (140,210]
#> 53 <NA> <NA> NA (0,70]
#> 54 <NA> <NA> NA (70,140]
#> 55 <NA> <NA> NA (210,280]
#> 56 <NA> <NA> NA (70,140]
#> 57 <NA> <NA> NA (70,140]
#> 58 <NA> <NA> NA (0,70]
#> 59 <NA> <NA> NA (70,140]
#> 60 <NA> <NA> NA (0,70]
#> 61 <NA> <NA> NA (70,140]
#> 62 (120,140] (120,140] TRUE (210,280]
#> 63 (40,60] (40,60] TRUE (210,280]
#> 64 (20,40] (20,40] FALSE (210,280]
#> 65 <NA> <NA> NA (70,140]
#> 66 (60,80] (60,80] TRUE (140,210]
#> 67 (20,40] (20,40] FALSE (280,350]
#> 68 (60,80] (60,80] TRUE (210,280]
#> 69 (80,100] (80,100] TRUE (210,280]
#> 70 (80,100] (80,100] TRUE (210,280]
#> 71 (80,100] (80,100] TRUE (140,210]
#> 72 <NA> <NA> NA (70,140]
#> 73 (0,20] (0,20] FALSE (210,280]
#> 74 (20,40] (20,40] FALSE (140,210]
#> 75 <NA> <NA> NA (280,350]
#> 76 (0,20] (0,20] FALSE (0,70]
#> 77 (40,60] (40,60] TRUE (210,280]
#> 78 (20,40] (20,40] FALSE (210,280]
#> 79 (60,80] (60,80] TRUE (280,350]
#> 80 (60,80] (60,80] TRUE (140,210]
#> 81 (60,80] (60,80] TRUE (210,280]
#> 82 (0,20] (0,20] FALSE (0,70]
#> 83 <NA> <NA> NA (210,280]
#> 84 <NA> <NA> NA (280,350]
#> 85 (60,80] (60,80] TRUE (280,350]
#> 86 (100,120] (100,120] TRUE (210,280]
#> 87 (0,20] (0,20] FALSE (70,140]
#> 88 (40,60] (40,60] TRUE (70,140]
#> 89 (80,100] (80,100] TRUE (210,280]
#> 90 (40,60] (40,60] TRUE (210,280]
#> 91 (60,80] (60,80] TRUE (210,280]
#> 92 (40,60] (40,60] TRUE (210,280]
#> 93 (20,40] (20,40] FALSE (70,140]
#> 94 (0,20] (0,20] FALSE (0,70]
#> 95 (0,20] (0,20] FALSE (70,140]
#> 96 (60,80] (60,80] TRUE (210,280]
#> 97 (20,40] (20,40] FALSE (210,280]
#> 98 (60,80] (60,80] TRUE (210,280]
#> 99 (120,140] (120,140] TRUE (210,280]
#> 100 (80,100] (80,100] TRUE (210,280]
#> 101 (100,120] (100,120] TRUE (140,210]
#> 102 <NA> <NA> NA (210,280]
#> 103 <NA> <NA> NA (70,140]
#> 104 (40,60] (40,60] TRUE (140,210]
#> 105 (20,40] (20,40] FALSE (210,280]
#> 106 (60,80] (60,80] TRUE (140,210]
#> 107 <NA> <NA> NA (0,70]
#> 108 (20,40] (20,40] FALSE (70,140]
#> 109 (40,60] (40,60] TRUE (0,70]
#> 110 (20,40] (20,40] FALSE (70,140]
#> 111 (20,40] (20,40] FALSE (210,280]
#> 112 (40,60] (40,60] TRUE (140,210]
#> 113 (20,40] (20,40] FALSE (210,280]
#> 114 (0,20] (0,20] FALSE (0,70]
#> 115 <NA> <NA> NA (210,280]
#> 116 (40,60] (40,60] TRUE (210,280]
#> 117 <NA> <NA> TRUE (210,280]
#> 118 (60,80] (60,80] TRUE (210,280]
#> 119 <NA> <NA> NA (140,210]
#> 120 (60,80] (60,80] TRUE (140,210]
#> 121 (100,120] (100,120] TRUE (210,280]
#> 122 (80,100] (80,100] TRUE (210,280]
#> 123 (80,100] (80,100] TRUE (140,210]
#> 124 (80,100] (80,100] TRUE (140,210]
#> 125 (60,80] (60,80] TRUE (140,210]
#> 126 (60,80] (60,80] TRUE (140,210]
#> 127 (80,100] (80,100] TRUE (140,210]
#> 128 (40,60] (40,60] TRUE (70,140]
#> 129 (20,40] (20,40] FALSE (70,140]
#> 130 (0,20] (0,20] FALSE (210,280]
#> 131 (20,40] (20,40] FALSE (210,280]
#> 132 (20,40] (20,40] FALSE (210,280]
#> 133 (20,40] (20,40] FALSE (210,280]
#> 134 (40,60] (40,60] TRUE (210,280]
#> 135 (20,40] (20,40] FALSE (210,280]
#> 136 (20,40] (20,40] FALSE (210,280]
#> 137 (0,20] (0,20] FALSE (0,70]
#> 138 (0,20] (0,20] FALSE (70,140]
#> 139 (40,60] (40,60] TRUE (210,280]
#> 140 (0,20] (0,20] FALSE (210,280]
#> 141 (0,20] (0,20] FALSE (0,70]
#> 142 (20,40] (20,40] FALSE (210,280]
#> 143 (0,20] (0,20] FALSE (140,210]
#> 144 (0,20] (0,20] FALSE (210,280]
#> 145 (20,40] (20,40] FALSE (0,70]
#> 146 (20,40] (20,40] FALSE (70,140]
#> 147 (0,20] (0,20] FALSE (0,70]
#> 148 (0,20] (0,20] FALSE (0,70]
#> 149 (20,40] (20,40] FALSE (140,210]
#> 150 <NA> <NA> NA (140,210]
#> 151 (0,20] (0,20] FALSE (140,210]
#> 152 (0,20] (0,20] FALSE (70,140]
#> 153 (0,20] (0,20] FALSE (210,280]
# dplyr: grouped imputation with weights
air_miss %>%
group_by(groups) %>%
do(mutate(., Solar_R_imp = fill_NA(
x = ., model = "lm_pred",
posit_y = "Solar.R",
posit_x = c("Wind", "Temp", "Intercept"),
w = .[["weights"]]
))) %>%
ungroup()
#> # A tibble: 153 × 14
#> Ozone Solar.R Wind Temp Day Intercept index weights groups x_character
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
#> 1 41 190 7.4 67 1 1 1 1.02 5 (140,210]
#> 2 36 118 8 72 2 1 2 1.01 5 (70,140]
#> 3 12 149 12.6 74 3 1 3 0.989 5 (140,210]
#> 4 18 313 11.5 62 4 1 4 0.991 5 (280,350]
#> 5 NA NA 14.3 56 5 1 5 0.995 5 NA
#> 6 28 NA 14.9 66 6 1 6 1.01 5 NA
#> 7 23 299 8.6 65 7 1 7 0.993 5 (280,350]
#> 8 19 99 13.8 59 8 1 8 0.996 5 (70,140]
#> 9 8 19 20.1 61 9 1 9 1.02 5 (0,70]
#> 10 NA 194 8.6 69 10 1 10 0.995 5 (140,210]
#> # ℹ 143 more rows
#> # ℹ 4 more variables: Ozone_chac <chr>, Ozone_f <fct>, Ozone_high <lgl>,
#> # Solar_R_imp <dbl>
# data.table
data(air_miss)
setDT(air_miss)
air_miss[, Ozone_imp := fill_NA(
x = .SD, model = "lm_bayes",
posit_y = "Ozone", posit_x = c("Solar.R", "Wind", "Temp")
)]
#> 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_imp
#> <char> <char> <fctr> <lgcl> <num>
#> 1: (140,210] (40,60] (40,60] FALSE 41.00000
#> 2: (70,140] (20,40] (20,40] FALSE 36.00000
#> 3: (140,210] (0,20] (0,20] FALSE 12.00000
#> 4: (280,350] (0,20] (0,20] FALSE 18.00000
#> 5: <NA> <NA> <NA> NA NA
#> ---
#> 149: (140,210] (20,40] (20,40] FALSE 30.00000
#> 150: (140,210] <NA> <NA> NA 15.84315
#> 151: (140,210] (0,20] (0,20] FALSE 14.00000
#> 152: (70,140] (0,20] (0,20] FALSE 18.00000
#> 153: (210,280] (0,20] (0,20] FALSE 20.00000
# data.table: grouped
air_miss[, Solar_R_imp := fill_NA(
x = .SD, model = "lm_pred",
posit_y = "Solar.R",
posit_x = c("Wind", "Temp", "Intercept"),
w = .SD[["weights"]]
), 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_imp Solar_R_imp
#> <char> <char> <fctr> <lgcl> <num> <num>
#> 1: (140,210] (40,60] (40,60] FALSE 41.00000 190.000
#> 2: (70,140] (20,40] (20,40] FALSE 36.00000 118.000
#> 3: (140,210] (0,20] (0,20] FALSE 12.00000 149.000
#> 4: (280,350] (0,20] (0,20] FALSE 18.00000 313.000
#> 5: <NA> <NA> <NA> NA NA 102.995
#> ---
#> 149: (140,210] (20,40] (20,40] FALSE 30.00000 193.000
#> 150: (140,210] <NA> <NA> NA 15.84315 145.000
#> 151: (140,210] (0,20] (0,20] FALSE 14.00000 191.000
#> 152: (70,140] (0,20] (0,20] FALSE 18.00000 131.000
#> 153: (210,280] (0,20] (0,20] FALSE 20.00000 223.000
# See the vignette for full examples:
# vignette("miceFast-intro", package = "miceFast")