Missing values are imputed with some representative values and statistical methods.
imputate_na(.data, xvar, yvar, method, seed, print_flag, no_attrs)a data.frame or a tbl_df.
variable name to replace missing value.
target variable.
method of missing values imputation.
integer. the random seed used in mice. only used "mice" method.
logical. If TRUE, mice will print running log on console. Use print_flag=FALSE for silent computation. Used only when method is "mice".
logical. If TRUE, return numerical variable or categorical variable. else If FALSE, imputation class.
An object of imputation class. or numerical variable or categorical variable. if no_attrs is FALSE then return imputation class, else no_attrs is TRUE then return numerical vector or factor. Attributes of imputation class is as follows.
var_type : the data type of predictor to replace missing value.
method : method of missing value imputation.
predictor is numerical variable.
"mean" : arithmetic mean.
"median" : median.
"mode" : mode.
"knn" : K-nearest neighbors.
"rpart" : Recursive Partitioning and Regression Trees.
"mice" : Multivariate Imputation by Chained Equations.
predictor is categorical variable.
"mode" : mode.
"rpart" : Recursive Partitioning and Regression Trees.
"mice" : Multivariate Imputation by Chained Equations.
na_pos : position of missing value in predictor.
seed : the random seed used in mice. only used "mice" method.
type : "missing values". type of imputation.
message : a message tells you if the result was successful.
success : Whether the imputation was successful.
imputate_na() creates an imputation class. The `imputation` class includes missing value position, imputed value, and method of missing value imputation, etc. The `imputation` class compares the imputed value with the original value to help determine whether the imputed value is used in the analysis.
See vignette("transformation") for an introduction to these concepts.
# Generate data for the example
heartfailure2 <- heartfailure
heartfailure2[sample(seq(NROW(heartfailure2)), 20), "platelets"] <- NA
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "smoking"] <- NA
# Replace the missing value of the platelets variable with median
imputate_na(heartfailure2, platelets, method = "median")
#> [1] 265000 263358 162000 210000 327000 204000 127000 454000 263358 388000
<<<<<<< HEAD
#> [11] 368000 253000 136000 276000 427000 262000 262000 166000 237000 87000
#> [21] 276000 297000 289000 368000 263358 149000 196000 284000 153000 200000
#> [31] 263358 360000 319000 302000 188000 228000 226000 321000 262000 329000
#> [41] 262000 153000 185000 218000 194000 310000 271000 451000 140000 395000
#> [51] 166000 418000 263358 351000 255000 461000 223000 216000 319000 254000
#> [61] 390000 216000 262000 385000 263358 119000 213000 274000 244000 497000
#> [71] 374000 122000 243000 149000 266000 262000 317000 237000 283000 324000
#> [81] 293000 263358 196000 172000 302000 262000 173000 304000 235000 262000
#> [91] 249000 297000 263358 262000 327000 219000 254000 255000 318000 221000
#> [101] 298000 263358 149000 226000 286000 621000 263000 226000 304000 850000
#> [111] 306000 228000 252000 351000 328000 262000 271000 507000 203000 263358
#> [121] 210000 162000 228000 127000 217000 237000 271000 300000 267000 227000
#> [131] 249000 250000 263358 295000 231000 263358 172000 305000 221000 211000
#> [141] 262000 348000 329000 229000 338000 266000 218000 242000 225000 228000
#> [151] 235000 244000 262000 263358 235000 194000 277000 262000 262000 362000
#> [161] 242000 174000 448000 75000 334000 192000 220000 70000 270000 305000
#> [171] 263358 325000 176000 189000 281000 337000 105000 132000 267000 279000
#> [181] 303000 221000 265000 224000 219000 389000 153000 365000 201000 275000
#> [191] 350000 309000 260000 160000 126000 223000 263358 259000 279000 263358
#> [201] 73000 377000 220000 212000 277000 362000 226000 186000 262000 262000
#> [211] 389000 147000 481000 244000 290000 203000 358000 151000 271000 371000
#> [221] 263358 194000 262000 130000 504000 265000 189000 141000 237000 274000
#> [231] 62000 185000 255000 330000 262000 406000 248000 173000 257000 262000
#> [241] 533000 249000 255000 220000 264000 282000 314000 246000 301000 223000
#> [251] 404000 231000 274000 236000 263358 334000 294000 253000 233000 308000
#> [261] 203000 262000 198000 208000 147000 362000 263358 133000 302000 222000
#> [271] 263358 221000 215000 189000 150000 422000 262000 25100 232000 451000
#> [281] 241000 51000 215000 263358 279000 336000 279000 543000 263358 390000
#> [291] 222000 133000 382000 262000 155000 270000 742000 140000 395000
=======
#> [11] 368000 253000 136000 276000 427000 47000 262000 166000 237000 87000
#> [21] 276000 297000 289000 368000 263358 149000 263358 284000 153000 200000
#> [31] 263358 360000 319000 302000 188000 228000 226000 321000 305000 329000
#> [41] 263358 153000 185000 218000 194000 310000 271000 451000 140000 395000
#> [51] 166000 418000 263358 351000 255000 461000 223000 216000 319000 254000
#> [61] 390000 216000 254000 385000 263358 119000 213000 263358 263358 497000
#> [71] 374000 122000 243000 149000 266000 204000 317000 263358 283000 324000
#> [81] 293000 263358 196000 172000 302000 406000 173000 304000 235000 181000
#> [91] 249000 297000 263358 210000 327000 219000 254000 255000 318000 221000
#> [101] 298000 263358 149000 226000 286000 621000 263000 263358 304000 850000
#> [111] 306000 228000 252000 351000 328000 164000 271000 507000 203000 263358
#> [121] 210000 263358 228000 127000 217000 237000 263358 300000 267000 227000
#> [131] 249000 250000 263358 295000 263358 263358 172000 305000 221000 211000
#> [141] 263358 348000 329000 229000 338000 266000 218000 242000 225000 228000
#> [151] 235000 244000 184000 263358 235000 263358 263358 262000 235000 362000
#> [161] 242000 174000 448000 75000 334000 192000 220000 70000 270000 305000
#> [171] 263358 325000 176000 263358 281000 337000 105000 132000 267000 279000
#> [181] 303000 221000 265000 224000 219000 389000 153000 365000 201000 275000
#> [191] 350000 309000 260000 263358 126000 223000 263358 259000 279000 263358
#> [201] 73000 377000 220000 212000 263358 362000 263358 263358 283000 268000
#> [211] 389000 147000 481000 244000 290000 203000 358000 151000 271000 371000
#> [221] 263358 194000 365000 130000 504000 265000 189000 141000 237000 274000
#> [231] 62000 185000 255000 330000 305000 263358 263358 173000 257000 263358
#> [241] 533000 249000 255000 220000 264000 282000 314000 246000 301000 223000
#> [251] 404000 231000 274000 236000 263358 334000 294000 253000 233000 308000
#> [261] 203000 283000 198000 208000 147000 362000 263358 133000 302000 222000
#> [271] 263358 221000 215000 189000 150000 422000 327000 25100 232000 451000
#> [281] 241000 51000 215000 263358 279000 336000 279000 543000 263358 390000
#> [291] 222000 133000 382000 179000 155000 270000 742000 263358 395000
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"var_type")
#> [1] "numerical"
#> attr(,"method")
#> [1] "median"
#> attr(,"na_pos")
<<<<<<< HEAD
#> [1] 16 39 41 63 76 86 90 94 116 141 153 159 209 210 223 235 240 262 277
#> [20] 294
=======
#> [1] 9 27 68 69 78 108 122 127 135 156 157 174 194 205 207 208 236 237 267
#> [20] 298
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"type")
#> [1] "missing values"
#> attr(,"message")
#> [1] "complete imputation"
#> attr(,"success")
#> [1] TRUE
#> attr(,"class")
#> [1] "imputation" "numeric"
# Replace the missing value of the platelets variable with rpart
# The target variable is death_event.
# Require rpart package
imputate_na(heartfailure2, platelets, death_event, method = "rpart")
#> [1] 265000.0 263358.0 162000.0 210000.0 327000.0 204000.0 127000.0 454000.0
<<<<<<< HEAD
#> [9] 263358.0 388000.0 368000.0 253000.0 136000.0 276000.0 427000.0 339272.7
#> [17] 262000.0 166000.0 237000.0 87000.0 276000.0 297000.0 289000.0 368000.0
#> [25] 263358.0 149000.0 196000.0 284000.0 153000.0 200000.0 263358.0 360000.0
#> [33] 319000.0 302000.0 188000.0 228000.0 226000.0 321000.0 271498.8 329000.0
#> [41] 271498.8 153000.0 185000.0 218000.0 194000.0 310000.0 271000.0 451000.0
#> [49] 140000.0 395000.0 166000.0 418000.0 263358.0 351000.0 255000.0 461000.0
#> [57] 223000.0 216000.0 319000.0 254000.0 390000.0 216000.0 268694.3 385000.0
#> [65] 263358.0 119000.0 213000.0 274000.0 244000.0 497000.0 374000.0 122000.0
#> [73] 243000.0 149000.0 266000.0 195176.5 317000.0 237000.0 283000.0 324000.0
#> [81] 293000.0 263358.0 196000.0 172000.0 302000.0 349600.0 173000.0 304000.0
#> [89] 235000.0 268694.3 249000.0 297000.0 263358.0 349250.0 327000.0 219000.0
#> [97] 254000.0 255000.0 318000.0 221000.0 298000.0 263358.0 149000.0 226000.0
#> [105] 286000.0 621000.0 263000.0 226000.0 304000.0 850000.0 306000.0 228000.0
#> [113] 252000.0 351000.0 328000.0 268694.3 271000.0 507000.0 203000.0 263358.0
#> [121] 210000.0 162000.0 228000.0 127000.0 217000.0 237000.0 271000.0 300000.0
#> [129] 267000.0 227000.0 249000.0 250000.0 263358.0 295000.0 231000.0 263358.0
#> [137] 172000.0 305000.0 221000.0 211000.0 271498.8 348000.0 329000.0 229000.0
#> [145] 338000.0 266000.0 218000.0 242000.0 225000.0 228000.0 235000.0 244000.0
#> [153] 295666.7 263358.0 235000.0 194000.0 277000.0 262000.0 216467.2 362000.0
#> [161] 242000.0 174000.0 448000.0 75000.0 334000.0 192000.0 220000.0 70000.0
#> [169] 270000.0 305000.0 263358.0 325000.0 176000.0 189000.0 281000.0 337000.0
#> [177] 105000.0 132000.0 267000.0 279000.0 303000.0 221000.0 265000.0 224000.0
#> [185] 219000.0 389000.0 153000.0 365000.0 201000.0 275000.0 350000.0 309000.0
#> [193] 260000.0 160000.0 126000.0 223000.0 263358.0 259000.0 279000.0 263358.0
#> [201] 73000.0 377000.0 220000.0 212000.0 277000.0 362000.0 226000.0 186000.0
#> [209] 220081.0 271498.8 389000.0 147000.0 481000.0 244000.0 290000.0 203000.0
#> [217] 358000.0 151000.0 271000.0 371000.0 263358.0 194000.0 349600.0 130000.0
#> [225] 504000.0 265000.0 189000.0 141000.0 237000.0 274000.0 62000.0 185000.0
#> [233] 255000.0 330000.0 216467.2 406000.0 248000.0 173000.0 257000.0 238260.9
#> [241] 533000.0 249000.0 255000.0 220000.0 264000.0 282000.0 314000.0 246000.0
#> [249] 301000.0 223000.0 404000.0 231000.0 274000.0 236000.0 263358.0 334000.0
#> [257] 294000.0 253000.0 233000.0 308000.0 203000.0 271498.8 198000.0 208000.0
#> [265] 147000.0 362000.0 263358.0 133000.0 302000.0 222000.0 263358.0 221000.0
#> [273] 215000.0 189000.0 150000.0 422000.0 220081.0 25100.0 232000.0 451000.0
#> [281] 241000.0 51000.0 215000.0 263358.0 279000.0 336000.0 279000.0 543000.0
#> [289] 263358.0 390000.0 222000.0 133000.0 382000.0 238260.9 155000.0 270000.0
#> [297] 742000.0 140000.0 395000.0
=======
#> [9] 275544.1 388000.0 368000.0 253000.0 136000.0 276000.0 427000.0 47000.0
#> [17] 262000.0 166000.0 237000.0 87000.0 276000.0 297000.0 289000.0 368000.0
#> [25] 263358.0 149000.0 275544.1 284000.0 153000.0 200000.0 263358.0 360000.0
#> [33] 319000.0 302000.0 188000.0 228000.0 226000.0 321000.0 305000.0 329000.0
#> [41] 263358.0 153000.0 185000.0 218000.0 194000.0 310000.0 271000.0 451000.0
#> [49] 140000.0 395000.0 166000.0 418000.0 263358.0 351000.0 255000.0 461000.0
#> [57] 223000.0 216000.0 319000.0 254000.0 390000.0 216000.0 254000.0 385000.0
#> [65] 263358.0 119000.0 213000.0 275544.1 275544.1 497000.0 374000.0 122000.0
#> [73] 243000.0 149000.0 266000.0 204000.0 317000.0 366181.1 283000.0 324000.0
#> [81] 293000.0 263358.0 196000.0 172000.0 302000.0 406000.0 173000.0 304000.0
#> [89] 235000.0 181000.0 249000.0 297000.0 263358.0 210000.0 327000.0 219000.0
#> [97] 254000.0 255000.0 318000.0 221000.0 298000.0 263358.0 149000.0 226000.0
#> [105] 286000.0 621000.0 263000.0 366181.1 304000.0 850000.0 306000.0 228000.0
#> [113] 252000.0 351000.0 328000.0 164000.0 271000.0 507000.0 203000.0 263358.0
#> [121] 210000.0 193538.5 228000.0 127000.0 217000.0 237000.0 366181.1 300000.0
#> [129] 267000.0 227000.0 249000.0 250000.0 263358.0 295000.0 251498.1 263358.0
#> [137] 172000.0 305000.0 221000.0 211000.0 263358.0 348000.0 329000.0 229000.0
#> [145] 338000.0 266000.0 218000.0 242000.0 225000.0 228000.0 235000.0 244000.0
#> [153] 184000.0 263358.0 235000.0 275544.1 193538.5 262000.0 235000.0 362000.0
#> [161] 242000.0 174000.0 448000.0 75000.0 334000.0 192000.0 220000.0 70000.0
#> [169] 270000.0 305000.0 263358.0 325000.0 176000.0 275544.1 281000.0 337000.0
#> [177] 105000.0 132000.0 267000.0 279000.0 303000.0 221000.0 265000.0 224000.0
#> [185] 219000.0 389000.0 153000.0 365000.0 201000.0 275000.0 350000.0 309000.0
#> [193] 260000.0 275544.1 126000.0 223000.0 263358.0 259000.0 279000.0 263358.0
#> [201] 73000.0 377000.0 220000.0 212000.0 258900.0 362000.0 261953.7 275544.1
#> [209] 283000.0 268000.0 389000.0 147000.0 481000.0 244000.0 290000.0 203000.0
#> [217] 358000.0 151000.0 271000.0 371000.0 263358.0 194000.0 365000.0 130000.0
#> [225] 504000.0 265000.0 189000.0 141000.0 237000.0 274000.0 62000.0 185000.0
#> [233] 255000.0 330000.0 305000.0 194866.7 194866.7 173000.0 257000.0 263358.0
#> [241] 533000.0 249000.0 255000.0 220000.0 264000.0 282000.0 314000.0 246000.0
#> [249] 301000.0 223000.0 404000.0 231000.0 274000.0 236000.0 263358.0 334000.0
#> [257] 294000.0 253000.0 233000.0 308000.0 203000.0 283000.0 198000.0 208000.0
#> [265] 147000.0 362000.0 251498.1 133000.0 302000.0 222000.0 263358.0 221000.0
#> [273] 215000.0 189000.0 150000.0 422000.0 327000.0 25100.0 232000.0 451000.0
#> [281] 241000.0 51000.0 215000.0 263358.0 279000.0 336000.0 279000.0 543000.0
#> [289] 263358.0 390000.0 222000.0 133000.0 382000.0 179000.0 155000.0 270000.0
#> [297] 742000.0 261953.7 395000.0
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"var_type")
#> [1] "numerical"
#> attr(,"method")
#> [1] "rpart"
#> attr(,"na_pos")
<<<<<<< HEAD
#> [1] 16 39 41 63 76 86 90 94 116 141 153 159 209 210 223 235 240 262 277
#> [20] 294
=======
#> [1] 9 27 68 69 78 108 122 127 135 156 157 174 194 205 207 208 236 237 267
#> [20] 298
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"type")
#> [1] "missing values"
#> attr(,"message")
#> [1] "complete imputation"
#> attr(,"success")
#> [1] TRUE
#> attr(,"class")
#> [1] "imputation" "numeric"
# Replace the missing value of the smoking variable with mode
imputate_na(heartfailure2, smoking, method = "mode")
#> [1] No No Yes No No Yes No Yes No Yes Yes Yes No No No No No No
#> [19] No No No No Yes No No Yes No Yes No Yes No No No No No No
#> [37] No No No No Yes Yes Yes No No Yes No Yes No No No No No No
<<<<<<< HEAD
#> [55] Yes No Yes Yes Yes Yes No No Yes No No Yes No Yes No No Yes Yes
=======
#> [55] Yes No Yes Yes Yes Yes No No Yes No No No No Yes No No Yes Yes
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> [73] Yes Yes Yes Yes Yes No Yes No No Yes No No No No No No No No
#> [91] Yes No No No No No No No No No No No Yes Yes No Yes No No
#> [109] Yes Yes Yes Yes No No No No No No No No Yes No No No No No
#> [127] No No Yes No Yes No No Yes Yes No No No No No No No No No
<<<<<<< HEAD
#> [145] Yes Yes Yes No No No No No Yes Yes No No No Yes No Yes No Yes
=======
#> [145] Yes Yes Yes No No No No No No Yes No No No Yes No Yes No Yes
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> [163] Yes No No No Yes No No No Yes Yes Yes No Yes Yes Yes No No Yes
#> [181] No Yes Yes No Yes No No No No No No No No Yes No No No No
#> [199] No Yes No No No Yes Yes No No No No No Yes Yes Yes No No No
#> [217] No No No No No Yes Yes No No No Yes No No No No Yes No Yes
<<<<<<< HEAD
#> [235] Yes No No No No Yes No Yes No No No No No No Yes No No No
=======
#> [235] Yes No No No No Yes No Yes Yes No No No No No Yes No No No
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> [253] No No No Yes No No No Yes No No No No No Yes Yes No No No
#> [271] Yes No No No Yes No No No No No No Yes Yes Yes No No No No
#> [289] No No No No Yes Yes Yes No No Yes Yes
#> attr(,"var_type")
#> [1] categorical
#> attr(,"method")
#> [1] mode
#> attr(,"na_pos")
<<<<<<< HEAD
#> [1] 4 13 34 69 243
=======
#> [1] 66 153 179 258 296
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"type")
#> [1] missing values
#> attr(,"message")
#> [1] complete imputation
#> attr(,"success")
#> [1] TRUE
#> Levels: No Yes
## using dplyr -------------------------------------
library(dplyr)
# The mean before and after the imputation of the platelets variable
heartfailure2 %>%
mutate(platelets_imp = imputate_na(heartfailure2, platelets, death_event,
method = "knn", no_attrs = TRUE)) %>%
group_by(death_event) %>%
summarise(orig = mean(platelets, na.rm = TRUE),
imputation = mean(platelets_imp))
#> # A tibble: 2 × 3
#> death_event orig imputation
#> <fct> <dbl> <dbl>
<<<<<<< HEAD
#> 1 No 266644. 266413.
#> 2 Yes 259614. 259658.
=======
#> 1 No 269696. 269742.
#> 2 Yes 256676. 256539.
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
# If the variable of interest is a numerical variable
# Require rpart package
platelets <- imputate_na(heartfailure2, platelets, death_event, method = "rpart")
platelets
#> [1] 265000.0 263358.0 162000.0 210000.0 327000.0 204000.0 127000.0 454000.0
<<<<<<< HEAD
#> [9] 263358.0 388000.0 368000.0 253000.0 136000.0 276000.0 427000.0 339272.7
#> [17] 262000.0 166000.0 237000.0 87000.0 276000.0 297000.0 289000.0 368000.0
#> [25] 263358.0 149000.0 196000.0 284000.0 153000.0 200000.0 263358.0 360000.0
#> [33] 319000.0 302000.0 188000.0 228000.0 226000.0 321000.0 271498.8 329000.0
#> [41] 271498.8 153000.0 185000.0 218000.0 194000.0 310000.0 271000.0 451000.0
#> [49] 140000.0 395000.0 166000.0 418000.0 263358.0 351000.0 255000.0 461000.0
#> [57] 223000.0 216000.0 319000.0 254000.0 390000.0 216000.0 268694.3 385000.0
#> [65] 263358.0 119000.0 213000.0 274000.0 244000.0 497000.0 374000.0 122000.0
#> [73] 243000.0 149000.0 266000.0 195176.5 317000.0 237000.0 283000.0 324000.0
#> [81] 293000.0 263358.0 196000.0 172000.0 302000.0 349600.0 173000.0 304000.0
#> [89] 235000.0 268694.3 249000.0 297000.0 263358.0 349250.0 327000.0 219000.0
#> [97] 254000.0 255000.0 318000.0 221000.0 298000.0 263358.0 149000.0 226000.0
#> [105] 286000.0 621000.0 263000.0 226000.0 304000.0 850000.0 306000.0 228000.0
#> [113] 252000.0 351000.0 328000.0 268694.3 271000.0 507000.0 203000.0 263358.0
#> [121] 210000.0 162000.0 228000.0 127000.0 217000.0 237000.0 271000.0 300000.0
#> [129] 267000.0 227000.0 249000.0 250000.0 263358.0 295000.0 231000.0 263358.0
#> [137] 172000.0 305000.0 221000.0 211000.0 271498.8 348000.0 329000.0 229000.0
#> [145] 338000.0 266000.0 218000.0 242000.0 225000.0 228000.0 235000.0 244000.0
#> [153] 295666.7 263358.0 235000.0 194000.0 277000.0 262000.0 216467.2 362000.0
#> [161] 242000.0 174000.0 448000.0 75000.0 334000.0 192000.0 220000.0 70000.0
#> [169] 270000.0 305000.0 263358.0 325000.0 176000.0 189000.0 281000.0 337000.0
#> [177] 105000.0 132000.0 267000.0 279000.0 303000.0 221000.0 265000.0 224000.0
#> [185] 219000.0 389000.0 153000.0 365000.0 201000.0 275000.0 350000.0 309000.0
#> [193] 260000.0 160000.0 126000.0 223000.0 263358.0 259000.0 279000.0 263358.0
#> [201] 73000.0 377000.0 220000.0 212000.0 277000.0 362000.0 226000.0 186000.0
#> [209] 220081.0 271498.8 389000.0 147000.0 481000.0 244000.0 290000.0 203000.0
#> [217] 358000.0 151000.0 271000.0 371000.0 263358.0 194000.0 349600.0 130000.0
#> [225] 504000.0 265000.0 189000.0 141000.0 237000.0 274000.0 62000.0 185000.0
#> [233] 255000.0 330000.0 216467.2 406000.0 248000.0 173000.0 257000.0 238260.9
#> [241] 533000.0 249000.0 255000.0 220000.0 264000.0 282000.0 314000.0 246000.0
#> [249] 301000.0 223000.0 404000.0 231000.0 274000.0 236000.0 263358.0 334000.0
#> [257] 294000.0 253000.0 233000.0 308000.0 203000.0 271498.8 198000.0 208000.0
#> [265] 147000.0 362000.0 263358.0 133000.0 302000.0 222000.0 263358.0 221000.0
#> [273] 215000.0 189000.0 150000.0 422000.0 220081.0 25100.0 232000.0 451000.0
#> [281] 241000.0 51000.0 215000.0 263358.0 279000.0 336000.0 279000.0 543000.0
#> [289] 263358.0 390000.0 222000.0 133000.0 382000.0 238260.9 155000.0 270000.0
#> [297] 742000.0 140000.0 395000.0
=======
#> [9] 275544.1 388000.0 368000.0 253000.0 136000.0 276000.0 427000.0 47000.0
#> [17] 262000.0 166000.0 237000.0 87000.0 276000.0 297000.0 289000.0 368000.0
#> [25] 263358.0 149000.0 275544.1 284000.0 153000.0 200000.0 263358.0 360000.0
#> [33] 319000.0 302000.0 188000.0 228000.0 226000.0 321000.0 305000.0 329000.0
#> [41] 263358.0 153000.0 185000.0 218000.0 194000.0 310000.0 271000.0 451000.0
#> [49] 140000.0 395000.0 166000.0 418000.0 263358.0 351000.0 255000.0 461000.0
#> [57] 223000.0 216000.0 319000.0 254000.0 390000.0 216000.0 254000.0 385000.0
#> [65] 263358.0 119000.0 213000.0 275544.1 275544.1 497000.0 374000.0 122000.0
#> [73] 243000.0 149000.0 266000.0 204000.0 317000.0 366181.1 283000.0 324000.0
#> [81] 293000.0 263358.0 196000.0 172000.0 302000.0 406000.0 173000.0 304000.0
#> [89] 235000.0 181000.0 249000.0 297000.0 263358.0 210000.0 327000.0 219000.0
#> [97] 254000.0 255000.0 318000.0 221000.0 298000.0 263358.0 149000.0 226000.0
#> [105] 286000.0 621000.0 263000.0 366181.1 304000.0 850000.0 306000.0 228000.0
#> [113] 252000.0 351000.0 328000.0 164000.0 271000.0 507000.0 203000.0 263358.0
#> [121] 210000.0 193538.5 228000.0 127000.0 217000.0 237000.0 366181.1 300000.0
#> [129] 267000.0 227000.0 249000.0 250000.0 263358.0 295000.0 251498.1 263358.0
#> [137] 172000.0 305000.0 221000.0 211000.0 263358.0 348000.0 329000.0 229000.0
#> [145] 338000.0 266000.0 218000.0 242000.0 225000.0 228000.0 235000.0 244000.0
#> [153] 184000.0 263358.0 235000.0 275544.1 193538.5 262000.0 235000.0 362000.0
#> [161] 242000.0 174000.0 448000.0 75000.0 334000.0 192000.0 220000.0 70000.0
#> [169] 270000.0 305000.0 263358.0 325000.0 176000.0 275544.1 281000.0 337000.0
#> [177] 105000.0 132000.0 267000.0 279000.0 303000.0 221000.0 265000.0 224000.0
#> [185] 219000.0 389000.0 153000.0 365000.0 201000.0 275000.0 350000.0 309000.0
#> [193] 260000.0 275544.1 126000.0 223000.0 263358.0 259000.0 279000.0 263358.0
#> [201] 73000.0 377000.0 220000.0 212000.0 258900.0 362000.0 261953.7 275544.1
#> [209] 283000.0 268000.0 389000.0 147000.0 481000.0 244000.0 290000.0 203000.0
#> [217] 358000.0 151000.0 271000.0 371000.0 263358.0 194000.0 365000.0 130000.0
#> [225] 504000.0 265000.0 189000.0 141000.0 237000.0 274000.0 62000.0 185000.0
#> [233] 255000.0 330000.0 305000.0 194866.7 194866.7 173000.0 257000.0 263358.0
#> [241] 533000.0 249000.0 255000.0 220000.0 264000.0 282000.0 314000.0 246000.0
#> [249] 301000.0 223000.0 404000.0 231000.0 274000.0 236000.0 263358.0 334000.0
#> [257] 294000.0 253000.0 233000.0 308000.0 203000.0 283000.0 198000.0 208000.0
#> [265] 147000.0 362000.0 251498.1 133000.0 302000.0 222000.0 263358.0 221000.0
#> [273] 215000.0 189000.0 150000.0 422000.0 327000.0 25100.0 232000.0 451000.0
#> [281] 241000.0 51000.0 215000.0 263358.0 279000.0 336000.0 279000.0 543000.0
#> [289] 263358.0 390000.0 222000.0 133000.0 382000.0 179000.0 155000.0 270000.0
#> [297] 742000.0 261953.7 395000.0
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"var_type")
#> [1] "numerical"
#> attr(,"method")
#> [1] "rpart"
#> attr(,"na_pos")
<<<<<<< HEAD
#> [1] 16 39 41 63 76 86 90 94 116 141 153 159 209 210 223 235 240 262 277
#> [20] 294
=======
#> [1] 9 27 68 69 78 108 122 127 135 156 157 174 194 205 207 208 236 237 267
#> [20] 298
>>>>>>> 2455413f029244b566a37aeed1916eea79ac483b
#> attr(,"type")
#> [1] "missing values"
#> attr(,"message")
#> [1] "complete imputation"
#> attr(,"success")
#> [1] TRUE
#> attr(,"class")
#> [1] "imputation" "numeric"