My dataframe is below. All variables are numeric, one of them (Total) has about 20 NAs. I would like the missForest package to create imputed values for the NAs in Total. I am running
R version 4.2.1 (2022-06-23 ucrt) on Windows.
imp <- structure(list(Years = c(21, 5, 5, 25, 4, 4, 4, 1, 12, 17, 5.5,
4, 13, 1, 1, 5, 1, 12, 8, 1, 14, 0.8, 6, 5, 4, 7, 4, 21, 3, 2,
20, 1, 2, 2, 20, 2, 1, 9, 12, 22, 1, 27, 5, 3, 1, 8, 5, 25, 1,
0.4, 4, 1, 1.5, 1, 1, 21, 5, 0.5, 3, 12, 3, 28, 7, 5, 22, 3.25,
4, 4, 12, 1, 3, 25, 17, 12, 40, 12, 6, 3, 8, 7, 17, 1, 3, 3,
6, 4, 7, 1, 7, 6, 4, 11, 1, 5, 2, 15, 1, 3, 7.5, 21, 4, 1.5,
7, 13, 5, 6, 9, 12.5, 2.5, 1, 17, 8, 5, 22, 25, 13, 5.5, 19,
9, 3.3, 14, 3, 22, 5, 6, 2.8, 9, 1, 8, 11, 8, 4, 2, 10, 1, 19,
13, 5, 1, 1.5, 7, 12, 2, 2.5, 1.5, 1, 2, 8, 5, 4, 3, 2, 2.5,
7, 11, 3, 8, 22, 5, 5, 8, 3.5, 1, 8, 11, 1, 5, 7, 9, 7, 4, 1,
14, 4, 20, 4, 5, 15.5, 9, 2, 7.5, 1, 13.5, 14, 1, 7, 4, 20, 9.5,
0, 10, 3, 8, 1, 3, 1, 19, 1, 20, 8, 25, 16, 14, 10, 24, 1, 2,
4, 0, 11, 2, 1.5, 2, 1, 21, 1, 20, 1.75, 5, 22, 5, 3), Staff = c(7,
8, 6, 10, 15, 6, 7, 17, 9, 5, 7, 12, 15, 8, 7, 5, 8, 8, 2, 8,
7, 8, 7, 7, 12, 8, 8, 7, 12, 10, 5, 7, 3, 6, 11, 4, 8, 8, 9,
6, 9, 9, 18, 10, 9, 5, 7, 20, 9, 4, 9, 6, 5, 4, 3, 5, 11, 8,
4, 7, 6, 16, 5, 5, 8, 8, 7, 4, 9, 9, 9, 14, 8, 5, 6, 6, 4, 3,
6, 7, 10, 7, 7, 3, 7, 13, 12, 4, 10, 8, 9, 5, 15, 7, 9, 9, 6,
5, 15, 7, 6, 5, 7, 8, 7, 7, 5, 9, 15, 12, 15, 5, 8, 7, 7, 5,
8, 12, 6, 6, 12, 9, 5, 4, 6, 7, 15, 5, 20, 6, 6, 11, 6, 8, 6,
2, 7, 4, 4, 2, 6, 15, 5, 15, 6, 3, 8, 15, 12, 7, 6, 9, 7, 1,
10, 5, 7, 4, 5, 1, 6, 5, 20, 8, 10, 1, 11, 9, 9, 5, 3, 8, 6,
5, 5, 5, 6, 8, 4, 7, 5, 4, 10, 8, 13, 5, 13, 3, 0, 15, 20, 5,
15, 14, 19, 20, 5, 7, 5, 9, 6, 6, 7, 20, 10, 25, 7, 5, 6, 10,
45, 10, 6, 5, 6, 8, 13, 12, 15, 7, 4, 1), JDs = c(64, 64, 120,
200, 30, 70, 370, 75, 300, 20, 68, 170, 77, 275, 132, 81, 875,
135, 75, 84, 74, 110, 120, 60, 1800, 94, 54, 125, 140, 150, 52,
190, 53, 170, 325, 18, 300, 86, 130, 375, 140, 200, 104, 50,
100, 95, 360, 40, 45, 52, 165, 20, 150, 58, 230, 95, 150, 95,
85, 120, 100, 265, 18, 90, 130, 77, 80, 75, 133, 73, 302, 500,
70, 50, 55, 72, 35, 60, 100, 90, 130, 41, 200, 29, 90, 35, 68,
30, 115, 51, 40, 125, 460, 400, 125, 400, 250, 51, 190, 200,
235, 150, 250, 137, 760, 90, 70, 100, 325, 200, 350, 150, 325,
23, 17, 50, 415, 650, 120, 96, 200, 4, 71, 700, 60, 224, 203,
16, 40, 62, 105, 41, 340, 22, 60, 11, 60, 30, 95, 27, 300, 120,
70, 96, 100, 6, 750, 14, 80, 60, 51, 90, 350, 250, 31, 78, 95,
32, 185, 65, 65, 30, 24, 65, 550, 100, 200, 80, 47, 45, 37, 250,
55, 25, 27, 90, 190, 65, 27, 80, 68, 110, 220, 325, 25, 43, 14,
5, 7, 17, 15, 135, 20, 26, 26, 29, 75, 93, 50, 127, 14, 75, 90,
50, 105, 190, 8, 45, 150, 300, 15, 25, 150, 60, 32, 85, 15, 144,
190, 155, 10, 20), Total = c(325000, 250000, 275000, 340000,
165000, 3e+05, 420000, 8e+05, 5e+05, 100776, 440000, 440000,
191500, NA, 4e+05, 145000, 6e+05, 4e+05, 125000, 155000, 230000,
250000, 240000, 2e+05, NA, 250000, 188000, 375000, 190000, 450000,
290558, 725000, 355000, 350000, 8e+05, 125000, 450000, 255000,
212500, 6e+05, 342000, 450000, 250000, 228000, 325000, 325000,
425000, 175000, NA, 240000, NA, 250000, 237000, 330000, 345000,
195000, 295000, 208000, 225000, NA, 445000, 253000, 75000, 285000,
4e+05, 2e+05, 308000, 236000, 470000, 190000, 1250000, 480000,
2e+05, 285000, 232000, 240000, 2e+05, 209000, 250000, 309000,
NA, 170000, 1e+06, 115200, 565000, 182500, 175000, 250000, 250000,
265000, 120000, 345000, 425000, 630000, 165000, 650000, 3e+05,
265000, 345000, 425000, 4e+05, 230000, 425000, 161500, 6e+05,
251000, 265000, 190000, 420000, 6e+05, 510000, 340000, 650000,
275000, 120000, 185000, 480000, 550000, 185000, 240000, 560000,
114000, 150000, 1050000, 230000, NA, 335000, 225000, 260000,
410000, 315000, 206000, 650000, 160000, 210000, 180000, 275000,
2e+05, 2e+05, 201094, 395000, 297000, 265000, 3e+05, 275000,
80000, 134000, 180000, 195000, 850000, 4e+05, 385000, 420000,
NA, 187000, 180000, 182700, 96597.28, 380000, 2e+05, 260000,
257500, 185000, 220000, 550000, 315000, 360000, 380000, 185000,
280000, 225000, 375000, 310000, 170000, 165000, 260000, 350000,
208000, 110000, 192500, 187500, 216000, 495000, 550000, 114500,
215000, 185000, NA, 114500, 110000, 250000, 350000, 180000, 118000,
191500, 1e+05, 230000, 350000, 240000, NA, 180000, 215000, 203000,
99800, 389900, NA, NA, NA, 4e+05, 6e+05, NA, NA, NA, 220000,
217500, NA, NA, 210000, 337000, 275000, NA, NA)), row.names = c(NA,
-222L), class = c("tbl_df", "tbl", "data.frame"))
library(missForest) # installed with dependencies = TRUE
impFor <- missForest(imp)
The statement above returns the following warnings and error.
Warning: argument is not numeric or logical: returning NAWarning: argument is not numeric or logical: returning NAWarning: argument is not numeric or logical: returning NAWarning: argument is not numeric or logical: returning NA
Warning: The response has five or fewer unique values. Are you sure you want to do regression?
Error in randomForest.default(x = obsX, y = obsY, ntree = ntree, mtry = mtry, :
length of response must be the same as predictors
The first four warnings appear to say that my four variables are neither numeric nor logical, but they are all numeric. The warning regarding regression and "five or fewer unique values" puzzles me because the package's manual makes no reference to a minimum number of unique values. Finally, the error confounds me completely.
I have searched StackOverflow, but the two questions that came up are not relevant.
Thank you for setting me right.
Your data should be in a data.frame format instead of tibble. You could use as.data.frame like this:
library(missForest)
class(imp)
#> [1] "tbl_df" "tbl" "data.frame"
imp <- as.data.frame(imp)
class(imp)
#> [1] "data.frame"
imp <- missForest(imp)
imp
#> $ximp
#> Years Staff JDs Total
#> 1 21.00 7 64 325000.00
#> 2 5.00 8 64 250000.00
#> 3 5.00 6 120 275000.00
#> 4 25.00 10 200 340000.00
#> 5 4.00 15 30 165000.00
#> 6 4.00 6 70 300000.00
#> 7 4.00 7 370 420000.00
#> 8 1.00 17 75 800000.00
#> 9 12.00 9 300 500000.00
#> 10 17.00 5 20 100776.00
#> 11 5.50 7 68 440000.00
#> 12 4.00 12 170 440000.00
#> 13 13.00 15 77 191500.00
#> 14 1.00 8 275 422030.00
#> 15 1.00 7 132 400000.00
#> 16 5.00 5 81 145000.00
#> 17 1.00 8 875 600000.00
#> 18 12.00 8 135 400000.00
#> 19 8.00 2 75 125000.00
#> 20 1.00 8 84 155000.00
#> 21 14.00 7 74 230000.00
#> 22 0.80 8 110 250000.00
#> 23 6.00 7 120 240000.00
#> 24 5.00 7 60 200000.00
#> 25 4.00 12 1800 564720.00
#> 26 7.00 8 94 250000.00
#> 27 4.00 8 54 188000.00
#> 28 21.00 7 125 375000.00
#> 29 3.00 12 140 190000.00
#> 30 2.00 10 150 450000.00
#> 31 20.00 5 52 290558.00
#> 32 1.00 7 190 725000.00
#> 33 2.00 3 53 355000.00
#> 34 2.00 6 170 350000.00
#> 35 20.00 11 325 800000.00
#> 36 2.00 4 18 125000.00
#> 37 1.00 8 300 450000.00
#> 38 9.00 8 86 255000.00
#> 39 12.00 9 130 212500.00
#> 40 22.00 6 375 600000.00
#> 41 1.00 9 140 342000.00
#> 42 27.00 9 200 450000.00
#> 43 5.00 18 104 250000.00
#> 44 3.00 10 50 228000.00
#> 45 1.00 9 100 325000.00
#> 46 8.00 5 95 325000.00
#> 47 5.00 7 360 425000.00
#> 48 25.00 20 40 175000.00
#> 49 1.00 9 45 185352.00
#> 50 0.40 4 52 240000.00
#> 51 4.00 9 165 403167.00
#> 52 1.00 6 20 250000.00
#> 53 1.50 5 150 237000.00
#> 54 1.00 4 58 330000.00
#> 55 1.00 3 230 345000.00
#> 56 21.00 5 95 195000.00
#> 57 5.00 11 150 295000.00
#> 58 0.50 8 95 208000.00
#> 59 3.00 4 85 225000.00
#> 60 12.00 7 120 261252.00
#> 61 3.00 6 100 445000.00
#> 62 28.00 16 265 253000.00
#> 63 7.00 5 18 75000.00
#> 64 5.00 5 90 285000.00
#> 65 22.00 8 130 400000.00
#> 66 3.25 8 77 200000.00
#> 67 4.00 7 80 308000.00
#> 68 4.00 4 75 236000.00
#> 69 12.00 9 133 470000.00
#> 70 1.00 9 73 190000.00
#> 71 3.00 9 302 1250000.00
#> 72 25.00 14 500 480000.00
#> 73 17.00 8 70 200000.00
#> 74 12.00 5 50 285000.00
#> 75 40.00 6 55 232000.00
#> 76 12.00 6 72 240000.00
#> 77 6.00 4 35 200000.00
#> 78 3.00 3 60 209000.00
#> 79 8.00 6 100 250000.00
#> 80 7.00 7 90 309000.00
#> 81 17.00 10 130 279905.83
#> 82 1.00 7 41 170000.00
#> 83 3.00 7 200 1000000.00
#> 84 3.00 3 29 115200.00
#> 85 6.00 7 90 565000.00
#> 86 4.00 13 35 182500.00
#> 87 7.00 12 68 175000.00
#> 88 1.00 4 30 250000.00
#> 89 7.00 10 115 250000.00
#> 90 6.00 8 51 265000.00
#> 91 4.00 9 40 120000.00
#> 92 11.00 5 125 345000.00
#> 93 1.00 15 460 425000.00
#> 94 5.00 7 400 630000.00
#> 95 2.00 9 125 165000.00
#> 96 15.00 9 400 650000.00
#> 97 1.00 6 250 300000.00
#> 98 3.00 5 51 265000.00
#> 99 7.50 15 190 345000.00
#> 100 21.00 7 200 425000.00
#> 101 4.00 6 235 400000.00
#> 102 1.50 5 150 230000.00
#> 103 7.00 7 250 425000.00
#> 104 13.00 8 137 161500.00
#> 105 5.00 7 760 600000.00
#> 106 6.00 7 90 251000.00
#> 107 9.00 5 70 265000.00
#> 108 12.50 9 100 190000.00
#> 109 2.50 15 325 420000.00
#> 110 1.00 12 200 600000.00
#> 111 17.00 15 350 510000.00
#> 112 8.00 5 150 340000.00
#> 113 5.00 8 325 650000.00
#> 114 22.00 7 23 275000.00
#> 115 25.00 7 17 120000.00
#> 116 13.00 5 50 185000.00
#> 117 5.50 8 415 480000.00
#> 118 19.00 12 650 550000.00
#> 119 9.00 6 120 185000.00
#> 120 3.30 6 96 240000.00
#> 121 14.00 12 200 560000.00
#> 122 3.00 9 4 114000.00
#> 123 22.00 5 71 150000.00
#> 124 5.00 4 700 1050000.00
#> 125 6.00 6 60 230000.00
#> 126 2.80 7 224 756680.00
#> 127 9.00 15 203 335000.00
#> 128 1.00 5 16 225000.00
#> 129 8.00 20 40 260000.00
#> 130 11.00 6 62 410000.00
#> 131 8.00 6 105 315000.00
#> 132 4.00 11 41 206000.00
#> 133 2.00 6 340 650000.00
#> 134 10.00 8 22 160000.00
#> 135 1.00 6 60 210000.00
#> 136 19.00 2 11 180000.00
#> 137 13.00 7 60 275000.00
#> 138 5.00 4 30 200000.00
#> 139 1.00 4 95 200000.00
#> 140 1.50 2 27 201094.00
#> 141 7.00 6 300 395000.00
#> 142 12.00 15 120 297000.00
#> 143 2.00 5 70 265000.00
#> 144 2.50 15 96 300000.00
#> 145 1.50 6 100 275000.00
#> 146 1.00 3 6 80000.00
#> 147 2.00 8 750 134000.00
#> 148 8.00 15 14 180000.00
#> 149 5.00 12 80 195000.00
#> 150 4.00 7 60 850000.00
#> 151 3.00 6 51 400000.00
#> 152 2.00 9 90 385000.00
#> 153 2.50 7 350 420000.00
#> 154 7.00 1 250 434900.00
#> 155 11.00 10 31 187000.00
#> 156 3.00 5 78 180000.00
#> 157 8.00 7 95 182700.00
#> 158 22.00 4 32 96597.28
#> 159 5.00 5 185 380000.00
#> 160 5.00 1 65 200000.00
#> 161 8.00 6 65 260000.00
#> 162 3.50 5 30 257500.00
#> 163 1.00 20 24 185000.00
#> 164 8.00 8 65 220000.00
#> 165 11.00 10 550 550000.00
#> 166 1.00 1 100 315000.00
#> 167 5.00 11 200 360000.00
#> 168 7.00 9 80 380000.00
#> 169 9.00 9 47 185000.00
#> 170 7.00 5 45 280000.00
#> 171 4.00 3 37 225000.00
#> 172 1.00 8 250 375000.00
#> 173 14.00 6 55 310000.00
#> 174 4.00 5 25 170000.00
#> 175 20.00 5 27 165000.00
#> 176 4.00 5 90 260000.00
#> 177 5.00 6 190 350000.00
#> 178 15.50 8 65 208000.00
#> 179 9.00 4 27 110000.00
#> 180 2.00 7 80 192500.00
#> 181 7.50 5 68 187500.00
#> 182 1.00 4 110 216000.00
#> 183 13.50 10 220 495000.00
#> 184 14.00 8 325 550000.00
#> 185 1.00 13 25 114500.00
#> 186 7.00 5 43 215000.00
#> 187 4.00 13 14 185000.00
#> 188 20.00 3 5 132532.25
#> 189 9.50 0 7 114500.00
#> 190 0.00 15 17 110000.00
#> 191 10.00 20 15 250000.00
#> 192 3.00 5 135 350000.00
#> 193 8.00 15 20 180000.00
#> 194 1.00 14 26 118000.00
#> 195 3.00 19 26 191500.00
#> 196 1.00 20 29 100000.00
#> 197 19.00 5 75 230000.00
#> 198 1.00 7 93 350000.00
#> 199 20.00 5 50 240000.00
#> 200 8.00 9 127 259289.83
#> 201 25.00 6 14 180000.00
#> 202 16.00 6 75 215000.00
#> 203 14.00 7 90 203000.00
#> 204 10.00 20 50 99800.00
#> 205 24.00 10 105 389900.00
#> 206 1.00 25 190 466223.67
#> 207 2.00 7 8 153912.76
#> 208 4.00 5 45 249760.00
#> 209 0.00 6 150 400000.00
#> 210 11.00 10 300 600000.00
#> 211 2.00 45 15 190321.17
#> 212 1.50 10 25 143960.88
#> 213 2.00 6 150 350892.50
#> 214 1.00 5 60 220000.00
#> 215 21.00 6 32 217500.00
#> 216 1.00 8 85 193365.00
#> 217 20.00 13 15 193093.52
#> 218 1.75 12 144 210000.00
#> 219 5.00 15 190 337000.00
#> 220 22.00 7 155 275000.00
#> 221 5.00 4 10 143128.61
#> 222 3.00 1 20 149726.72
#>
#> $OOBerror
#> NRMSE
#> 0.4584988
#>
#> attr(,"class")
#> [1] "missForest"
Created on 2023-02-11 with reprex v2.0.2