Skip to contents

Impute Missing Data Using Multiple Imputation

Source: R/ImputeData.R
ImputeData.Rd

The ImputeData function performs multiple imputation on a data frame with missing values using the mice package. It handles missing data by creating multiple imputed datasets based on a specified imputation method and returns a list of completed data frames.

Usage

ImputeData(
  data_missing,
  m = 5,
  method = "pmm",
  seed = 123,
  predictorMatrix = NULL
)

Arguments

data_missing

A data frame containing missing values to be imputed. The function replaces values coded as -999 with NA before imputation.

m

An integer specifying the number of imputed datasets to generate.

method

A character string specifying the imputation method. Default is "pmm" (predictive mean matching).

seed

An integer for setting the random seed to ensure reproducibility. Default is 123.

predictorMatrix

An optional matrix specifying the predictor structure for the imputation model. Default is NULL, meaning that the function will use the default predictor matrix created by mice.

Value

A list of m imputed data frames.

Details

This function replaces specified missing value placeholders (e.g., -999) with NA, and then applies the multiple imputation by chained equations (MICE) procedure to generate multiple imputed datasets. It supports flexible imputation methods and allows for specifying a custom predictor matrix.

Author

Wendie Yang, Shufai Cheung

Examples

# Example data with missing values
data <- data.frame(
  M1 = c(rnorm(99), rep(NA, 1)),
  M2 = c(rnorm(99), rep(NA, 1)),
  Y1 = rnorm(100),
  Y2 = rnorm(100)
)
# Perform multiple imputation
imputed_data_list <- ImputeData(data, m = 5)
# Display the first imputed dataset
head(imputed_data_list[[1]])
#> $data
#>              M1          M2           Y1           Y2
#> 1   -1.10735295  1.46242573  1.733784762  1.500961160
#> 2   -0.62788279  1.07465712 -1.432535684 -0.482768943
#> 3   -1.47950195  0.65505667 -0.176478952  0.605210639
#> 4    0.15434317 -0.12313366  0.756314868  0.800685483
#> 5   -2.29948441 -0.92972180 -0.980007712  0.318362191
#> 6   -0.43872768  1.51655653 -0.386424379  0.103472107
#> 7   -0.64968061 -0.07679296  0.381516254 -0.657636600
#> 8    0.42404095 -0.36979194  1.345768539  0.033874701
#> 9    0.04704973  0.60623267  0.983748677 -0.649756559
#> 10  -1.28504407  1.52673800  0.086637298  0.911239650
#> 11   0.04553236 -0.08151085  0.469669925 -0.047272921
#> 12  -1.44942970  1.42835885 -0.892900170 -1.178515013
#> 13  -1.63370440  1.01357184  0.002229617  2.242660967
#> 14  -0.51728496 -0.67657783  0.150747679  1.467485607
#> 15  -0.99115143 -0.23656567 -0.574554552  0.552061735
#> 16  -0.72848504  0.50559912  0.235160435  0.359622515
#> 17   0.45195163 -2.52775293  1.265858408  1.837521669
#> 18   0.88718133 -0.70066867 -1.390298039  0.883273304
#> 19   1.26457340  0.09160758  0.115795724 -1.713201462
#> 20  -1.29141939  0.85217358  0.892577296  0.293728600
#> 21   0.18251031  1.12390932  0.038752879  1.330807535
#> 22  -0.08980967  0.82786930 -0.038372698  0.001025496
#> 23  -0.11262928 -1.36811281  0.811453346  0.944012124
#> 24   0.16280134  0.18975968  0.033486905 -0.163110155
#> 25   0.55586101  1.96075593  0.663318866  0.020936334
#> 26   0.85339468 -0.80601772 -0.220091795  0.551059233
#> 27  -0.19489936  0.24735027 -0.015736545  0.754814726
#> 28  -0.04341191  0.50782823  2.087713501 -1.017904646
#> 29  -0.52943832 -0.49580175  1.496586647 -0.320525550
#> 30  -0.59851060 -0.90210237 -0.309764025 -0.457239570
#> 31   0.14929768  1.01253254  1.584290933 -2.168675688
#> 32   1.25582092  1.63654057 -1.147278380  0.066409650
#> 33  -0.23436953 -0.82552051  0.937994138  1.513114967
#> 34   1.42234028 -1.30128401 -1.176473378  0.242563643
#> 35   0.29212754 -0.48404261  1.402133012  0.196097401
#> 36  -0.73688675 -0.19156194 -0.177175377 -0.656023781
#> 37  -0.12999655 -1.20399135 -0.650604263 -0.478355050
#> 38   2.20088565 -1.55456583 -1.229364408  0.568878744
#> 39   1.61308129 -0.14928970  0.251325895 -2.102626810
#> 40  -0.26749958 -0.98824895 -1.563215351 -0.003301496
#> 41   0.12760317 -1.30053093  0.813079991  0.416918015
#> 42  -0.37089274  0.76400192 -0.110848678  0.473867599
#> 43  -0.45240228 -0.76422002 -0.761172061 -0.062925119
#> 44   1.19747825  1.05984222  0.829512582 -1.636219349
#> 45   0.96133970  0.57440044 -1.055171964  1.168229652
#> 46   1.09924455  1.45435482  0.222967963 -0.408686195
#> 47   0.05353454  1.92660650  2.441195038 -0.094117059
#> 48  -0.07108256  0.37769313  2.000641700  0.467589309
#> 49  -0.26521262  0.45781732 -0.754805825 -0.828195377
#> 50  -1.08747279 -0.31447817 -1.303238961  0.396813229
#> 51   0.11299534 -0.19633722  0.246026158  0.244475509
#> 52  -0.36876161  0.53220725  0.548893707  0.627437586
#> 53  -1.81381842 -1.29693082  0.486414289 -0.805206472
#> 54  -0.72799437 -0.11645988 -0.857244463 -1.932053070
#> 55  -0.32334346 -1.79143581 -0.208889627  0.717082011
#> 56   0.11817806  1.03763845 -0.021828351  1.792486559
#> 57   0.52272118 -0.75173731  0.371728862  1.123194865
#> 58  -0.39471204  1.44332021 -2.300614875  1.609947684
#> 59  -0.73826236  0.73418909 -0.686391490  1.732799496
#> 60   1.31970010 -1.05455799  0.823954712  1.275703717
#> 61  -0.54771747  0.03042473  0.671560953  0.424007988
#> 62   2.11653074 -0.62059166 -0.136811085 -0.137778131
#> 63   0.23888968 -0.19350202 -0.324855419 -0.597937324
#> 64  -0.51596937  0.39360687  1.165364420 -0.722073272
#> 65   1.15875218 -0.03322616 -0.903741440  0.072762483
#> 66  -0.63435781 -0.52872257  0.985612115  1.473362511
#> 67   0.86271083  3.07100715 -0.126323527 -0.316206077
#> 68  -1.68157303 -1.42472377 -1.443967173 -1.414721408
#> 69  -1.63679212  0.33070126  0.032154077 -1.986179040
#> 70   1.43029016 -1.75947902 -2.117716447  0.705734028
#> 71   1.53937650 -1.01638420  1.665816928 -0.790427631
#> 72  -0.07721086  1.03686204  0.575671055  0.758480158
#> 73   0.72223242  0.03471228 -0.002235961  2.078375621
#> 74   0.76201239 -1.69369136 -0.722014992  1.919134900
#> 75  -1.10521674 -0.37408213  0.448672361  0.075529822
#> 76  -1.46116859 -1.25836245  0.051736240  0.267751527
#> 77   0.29556190 -0.60810563 -1.253649454  0.436290736
#> 78   0.65852651 -1.36870786  0.558374959  1.209748781
#> 79   0.64514272 -0.94216345  0.298702402 -1.253055561
#> 80   0.62365346  0.73985773 -0.415528828  1.765028630
#> 81   2.31584930  0.02209434 -1.625374469 -0.159322027
#> 82  -0.38159888  0.54888273  0.299647475  0.898874217
#> 83   0.03168816 -1.35069806 -0.146007948 -0.704995332
#> 84   0.77662540 -0.64446763  0.330470436  1.126896570
#> 85  -1.98304783  1.04784473 -0.344358479 -1.974322214
#> 86  -0.00544671 -0.32139336  0.891762344 -1.244772830
#> 87   0.70241171  0.03432588 -0.136294335  1.031250388
#> 88   1.22316402  1.06394837  0.281191185  0.579097050
#> 89  -0.14139745 -1.58760815 -0.110812460 -1.037890010
#> 90  -0.49537901 -1.58346240 -0.036247432  1.561622325
#> 91   0.84052263  0.11053133  0.024977216  0.101959622
#> 92   0.56005014  0.24629884  0.131053564  2.324238302
#> 93   1.13704594  0.05714992  2.595523922  2.325851550
#> 94   0.47380131 -1.08330350  0.419283992 -1.293149956
#> 95  -0.15044036  0.21630111 -0.281698421 -0.045018855
#> 96   2.13899523 -0.82368553  0.544863114  0.195694234
#> 97   0.21799655 -1.50078040  0.868230689  0.480623395
#> 98  -1.89596918  1.22656701 -0.206675268  2.017342464
#> 99  -0.14273839 -0.21586746  0.074947320  1.195608651
#> 100          NA          NA -2.250901330  0.656575520
#> 
#> $imp
#> $imp$M1
#>             1         2         3         4          5
#> 100 -0.495379 0.8627108 0.2179965 0.4240409 -0.1413975
#> 
#> $imp$M2
#>            1          2         3         4        5
#> 100 1.012533 0.02209434 -1.693691 0.2163011 1.636541
#> 
#> $imp$Y1
#> [1] 1 2 3 4 5
#> <0 rows> (or 0-length row.names)
#> 
#> $imp$Y2
#> [1] 1 2 3 4 5
#> <0 rows> (or 0-length row.names)
#> 
#> 
#> $m
#> [1] 5
#> 
#> $where
#>        M1    M2    Y1    Y2
#> 1   FALSE FALSE FALSE FALSE
#> 2   FALSE FALSE FALSE FALSE
#> 3   FALSE FALSE FALSE FALSE
#> 4   FALSE FALSE FALSE FALSE
#> 5   FALSE FALSE FALSE FALSE
#> 6   FALSE FALSE FALSE FALSE
#> 7   FALSE FALSE FALSE FALSE
#> 8   FALSE FALSE FALSE FALSE
#> 9   FALSE FALSE FALSE FALSE
#> 10  FALSE FALSE FALSE FALSE
#> 11  FALSE FALSE FALSE FALSE
#> 12  FALSE FALSE FALSE FALSE
#> 13  FALSE FALSE FALSE FALSE
#> 14  FALSE FALSE FALSE FALSE
#> 15  FALSE FALSE FALSE FALSE
#> 16  FALSE FALSE FALSE FALSE
#> 17  FALSE FALSE FALSE FALSE
#> 18  FALSE FALSE FALSE FALSE
#> 19  FALSE FALSE FALSE FALSE
#> 20  FALSE FALSE FALSE FALSE
#> 21  FALSE FALSE FALSE FALSE
#> 22  FALSE FALSE FALSE FALSE
#> 23  FALSE FALSE FALSE FALSE
#> 24  FALSE FALSE FALSE FALSE
#> 25  FALSE FALSE FALSE FALSE
#> 26  FALSE FALSE FALSE FALSE
#> 27  FALSE FALSE FALSE FALSE
#> 28  FALSE FALSE FALSE FALSE
#> 29  FALSE FALSE FALSE FALSE
#> 30  FALSE FALSE FALSE FALSE
#> 31  FALSE FALSE FALSE FALSE
#> 32  FALSE FALSE FALSE FALSE
#> 33  FALSE FALSE FALSE FALSE
#> 34  FALSE FALSE FALSE FALSE
#> 35  FALSE FALSE FALSE FALSE
#> 36  FALSE FALSE FALSE FALSE
#> 37  FALSE FALSE FALSE FALSE
#> 38  FALSE FALSE FALSE FALSE
#> 39  FALSE FALSE FALSE FALSE
#> 40  FALSE FALSE FALSE FALSE
#> 41  FALSE FALSE FALSE FALSE
#> 42  FALSE FALSE FALSE FALSE
#> 43  FALSE FALSE FALSE FALSE
#> 44  FALSE FALSE FALSE FALSE
#> 45  FALSE FALSE FALSE FALSE
#> 46  FALSE FALSE FALSE FALSE
#> 47  FALSE FALSE FALSE FALSE
#> 48  FALSE FALSE FALSE FALSE
#> 49  FALSE FALSE FALSE FALSE
#> 50  FALSE FALSE FALSE FALSE
#> 51  FALSE FALSE FALSE FALSE
#> 52  FALSE FALSE FALSE FALSE
#> 53  FALSE FALSE FALSE FALSE
#> 54  FALSE FALSE FALSE FALSE
#> 55  FALSE FALSE FALSE FALSE
#> 56  FALSE FALSE FALSE FALSE
#> 57  FALSE FALSE FALSE FALSE
#> 58  FALSE FALSE FALSE FALSE
#> 59  FALSE FALSE FALSE FALSE
#> 60  FALSE FALSE FALSE FALSE
#> 61  FALSE FALSE FALSE FALSE
#> 62  FALSE FALSE FALSE FALSE
#> 63  FALSE FALSE FALSE FALSE
#> 64  FALSE FALSE FALSE FALSE
#> 65  FALSE FALSE FALSE FALSE
#> 66  FALSE FALSE FALSE FALSE
#> 67  FALSE FALSE FALSE FALSE
#> 68  FALSE FALSE FALSE FALSE
#> 69  FALSE FALSE FALSE FALSE
#> 70  FALSE FALSE FALSE FALSE
#> 71  FALSE FALSE FALSE FALSE
#> 72  FALSE FALSE FALSE FALSE
#> 73  FALSE FALSE FALSE FALSE
#> 74  FALSE FALSE FALSE FALSE
#> 75  FALSE FALSE FALSE FALSE
#> 76  FALSE FALSE FALSE FALSE
#> 77  FALSE FALSE FALSE FALSE
#> 78  FALSE FALSE FALSE FALSE
#> 79  FALSE FALSE FALSE FALSE
#> 80  FALSE FALSE FALSE FALSE
#> 81  FALSE FALSE FALSE FALSE
#> 82  FALSE FALSE FALSE FALSE
#> 83  FALSE FALSE FALSE FALSE
#> 84  FALSE FALSE FALSE FALSE
#> 85  FALSE FALSE FALSE FALSE
#> 86  FALSE FALSE FALSE FALSE
#> 87  FALSE FALSE FALSE FALSE
#> 88  FALSE FALSE FALSE FALSE
#> 89  FALSE FALSE FALSE FALSE
#> 90  FALSE FALSE FALSE FALSE
#> 91  FALSE FALSE FALSE FALSE
#> 92  FALSE FALSE FALSE FALSE
#> 93  FALSE FALSE FALSE FALSE
#> 94  FALSE FALSE FALSE FALSE
#> 95  FALSE FALSE FALSE FALSE
#> 96  FALSE FALSE FALSE FALSE
#> 97  FALSE FALSE FALSE FALSE
#> 98  FALSE FALSE FALSE FALSE
#> 99  FALSE FALSE FALSE FALSE
#> 100  TRUE  TRUE FALSE FALSE
#> 
#> $blocks
#> $blocks$M1
#> [1] "M1"
#> 
#> $blocks$M2
#> [1] "M2"
#> 
#> $blocks$Y1
#> [1] "Y1"
#> 
#> $blocks$Y2
#> [1] "Y2"
#> 
#> attr(,"calltype")
#>     M1     M2     Y1     Y2 
#> "type" "type" "type" "type" 
#> 
#> $call
#> mice::mice(data = data_missing, m = m, method = method, predictorMatrix = predictorMatrix, 
#>     printFlag = FALSE, seed = seed)
#>