Thin wrapper around MatchIt::matchit(distance = "mahalanobis").
Usage
morie_matching_mahalanobis(
data,
treatment,
covariates,
n_neighbors = 1L,
caliper = NULL,
replace = FALSE,
exact = NULL
)Arguments
- data
Data frame.
- treatment
Binary treatment column name.
- covariates
Character vector of continuous covariates.
- n_neighbors
Number of matches per treated unit.
- caliper
Maximum Mahalanobis distance for a valid match.
- replace
If
TRUE, controls may be re-used.- exact
Optional character vector of variables to match exactly prior to distance matching.
Examples
# \donttest{
set.seed(1)
df <- data.frame(y = rnorm(200), d = rbinom(200, 1, 0.4),
x1 = rnorm(200), x2 = rnorm(200))
morie_matching_mahalanobis(df, "d", c("x1", "x2"), n_neighbors = 1)
#> $matched_data
#> y d x1 x2
#> 1 -0.626453811 1 0.893673702 0.077303123
#> 3 -0.835628612 1 1.971337386 -1.183242240
#> 4 1.595280802 1 -0.383632106 0.011292688
#> 5 0.329507772 1 1.654145302 0.991601036
#> 6 -0.820468384 1 1.512212694 1.593967454
#> 8 0.738324705 1 0.567220915 -0.249610933
#> 9 0.575781352 0 -1.024548480 1.159424527
#> 10 -0.305388387 1 0.323006503 -1.114222348
#> 11 1.511781168 1 1.043612458 -2.528500689
#> 12 0.389843236 0 0.099078487 -0.935902559
#> 13 -0.621240581 1 -0.454136909 -0.967239458
#> 14 -2.214699887 1 -0.655781852 0.047488592
#> 15 1.124930918 1 -0.035922423 -0.403736793
#> 16 -0.044933609 0 1.069161461 0.231496128
#> 17 -0.016190263 0 -0.483974930 -0.422372408
#> 18 0.943836211 0 -0.121010111 0.374118395
#> 19 0.821221195 1 -1.294140004 -0.366005775
#> 20 0.593901321 1 0.494312836 1.190101447
#> 22 0.782136301 0 1.497041009 0.290666645
#> 23 0.074564983 1 0.814702731 -0.884849568
#> 24 -1.989351696 0 -1.869788790 0.208006479
#> 25 0.619825748 0 0.482029504 -0.047730172
#> 27 -0.155795507 0 -0.353400286 -0.144226557
#> 28 -1.470752384 1 0.170489471 1.180213666
#> 29 -0.478150055 1 -0.864035954 0.681399923
#> 30 0.417941560 1 0.679230774 0.143247631
#> 31 1.358679552 1 -0.327101015 -1.192316444
#> 32 -0.102787727 0 -1.569082185 1.169228653
#> 33 0.387671612 0 -0.367450756 0.079201709
#> 34 -0.053805041 0 1.364434929 -0.451773753
#> 35 -1.377059557 0 -0.334281365 1.642028213
#> 36 -0.414994563 1 0.732750042 -0.769592322
#> 37 -0.394289954 1 0.946585640 0.303360961
#> 38 -0.059313397 0 0.004398704 1.281737421
#> 39 1.100025372 0 -0.352322306 0.602222795
#> 40 0.763175748 1 -0.529695509 -0.307022265
#> 42 -0.253361680 0 -1.063457415 0.355135530
#> 43 0.696963375 0 0.246210844 0.513481115
#> 44 0.556663199 1 -0.289499367 0.018607400
#> 46 -0.707495157 0 -1.408850456 -0.065832000
#> 48 0.768532925 1 -0.191278951 0.537326132
#> 49 -0.112346212 0 0.803283216 -2.201782322
#> 50 0.881107726 0 1.887474463 0.391973744
#> 51 0.398105880 1 1.473881181 0.496960952
#> 52 -0.612026393 0 0.677268492 -0.224874715
#> 53 0.341119691 0 0.379962687 -1.117143165
#> 55 1.433023702 0 1.577891795 1.549830342
#> 56 1.980399899 1 0.596234109 -0.743514480
#> 57 -0.367221476 0 -1.173576941 -2.331712118
#> 58 -1.044134626 1 -0.155642535 0.812245442
#> 59 0.569719627 1 -1.918909820 -0.501310657
#> 61 2.401617761 0 -2.592327670 -1.215364041
#> 62 -0.039240003 0 1.314002167 -0.022558628
#> 63 0.689739362 0 -0.635543001 0.701239300
#> 66 0.188792300 1 0.612218174 1.096640215
#> 67 -1.804958629 1 0.678340177 -0.247509677
#> 68 1.465554862 0 0.567951972 -0.159901713
#> 69 0.153253338 0 -0.572542604 -0.625778251
#> 70 2.172611670 1 -1.363291256 0.900434636
#> 71 0.475509529 0 -0.388722244 -0.994193629
#> 72 -0.709946431 1 0.277914132 0.849250386
#> 73 0.610726353 1 -0.823081122 0.805702289
#> 74 -0.934097632 0 -0.068840934 -0.467600936
#> 75 -1.253633400 0 -1.167662326 0.848420314
#> 76 0.291446236 0 -0.008309014 0.986769864
#> 77 -0.443291873 1 0.128855402 0.575620289
#> 78 0.001105352 0 -0.145875628 2.024842045
#> 80 -0.589520946 1 1.763552003 -1.164920931
#> 81 -0.568668733 1 0.762586512 -1.376519214
#> 82 -0.135178615 0 1.111431081 0.167679934
#> 83 1.178086997 1 -0.923206953 1.584629079
#> 84 -1.523566800 1 0.164341838 1.677888953
#> 85 0.593946188 0 1.154825187 0.488296698
#> 86 0.332950371 0 -0.056521425 0.878673263
#> 87 1.063099837 0 -2.129360648 -0.144874874
#> 88 -0.304183924 0 0.344845762 0.468971760
#> 89 0.370018810 1 -1.904955446 0.376235477
#> 90 0.267098791 0 -0.811170153 -0.761040275
#> 91 -0.542520031 0 1.324004321 -0.293294934
#> 92 1.207867806 1 0.615636849 -0.134841264
#> 93 1.160402616 0 1.091668956 1.393845816
#> 94 0.700213650 0 0.306604862 -1.036988690
#> 96 0.558486426 0 -0.924312773 0.768278218
#> 97 -1.276592208 0 1.592913754 -0.816160621
#> 99 -1.224612615 1 -0.715128401 0.904705031
#> 100 -0.473400636 0 0.865223100 -0.763086265
#> 101 -0.620366677 0 1.074440958 -0.341066980
#> 102 0.042115873 1 1.895654774 1.502424534
#> 103 -0.910921649 1 -0.602997304 0.528307712
#> 104 0.158028772 1 -0.390867821 0.542191355
#> 105 -0.654584644 0 -0.416222032 -0.136673356
#> 106 1.767287269 1 -0.375657423 -1.136733853
#> 107 0.716707476 0 -0.366630946 -1.496627154
#> 108 0.910174229 1 -0.295677453 -0.223385644
#> 109 0.384185358 1 1.441820410 2.001719228
#> 110 1.682176081 0 -0.697538292 0.221703816
#> 111 -0.635736454 1 -0.388167506 0.164372909
#> 112 -0.461644730 0 0.652536452 0.332623609
#> 113 1.432282239 0 1.124772447 -0.385207999
#> 114 -0.650696353 1 -0.772110803 -1.398754027
#> 115 -0.207380744 1 -0.508086216 2.675740796
#> 117 -0.319992869 1 1.017754227 -0.298601512
#> 118 -0.279113303 0 -0.251164588 -1.792341727
#> 119 0.494188331 0 -1.429993447 -0.248008225
#> 120 -0.177330482 1 1.709121032 -0.247303918
#> 121 -0.505957462 1 1.435069572 -0.255510379
#> 122 1.343038825 1 -0.710371146 -1.786938100
#> 123 -0.214579409 0 -0.065067574 1.784662816
#> 124 -0.179556530 0 -1.759468735 1.763586348
#> 125 -0.100190741 0 0.569722972 0.689600222
#> 126 0.712666307 1 1.612346798 -1.100740644
#> 127 -0.073564404 1 -1.637280647 0.714509357
#> 128 -0.037634171 1 -0.779568513 -0.246470317
#> 129 -0.681660479 1 -0.641176934 -0.319786166
#> 130 -0.324270272 1 -0.681131394 1.362644293
#> 131 0.060160440 1 -2.033285596 -1.227882590
#> 132 -0.588894486 0 0.500963559 -0.511219233
#> 134 -1.518394082 0 -0.024997639 0.019752007
#> 135 0.306557861 0 0.592984721 -1.572863915
#> 136 -1.536449824 0 -0.198195421 -0.703333270
#> 137 -0.300976127 0 0.892008392 0.715932089
#> 138 -0.528279904 0 -0.025715071 0.465214906
#> 139 -0.652094781 0 -0.647660451 -0.973902306
#> 140 -0.056896778 1 0.646359415 0.559217730
#> 142 1.176583312 1 1.772611185 -0.340484927
#> 143 -1.664972436 1 -0.018259711 0.713033195
#> 144 -0.463530401 0 0.852814994 -0.659037386
#> 147 2.087166546 1 -1.366111931 0.847792797
#> 148 0.017395620 0 -0.424102260 -1.850388849
#> 149 -1.286300530 0 0.236803664 -0.323650632
#> 150 -1.640605534 1 -2.342723120 -0.255248113
#> 151 0.450187101 1 0.961696633 0.060921227
#> 153 -0.318068375 0 -0.752877279 1.829730485
#> 154 -0.929362147 1 -1.555611593 -1.429916216
#> 155 -1.487460310 1 -1.453893738 0.254137143
#> 157 1.000028804 0 0.509369407 0.002415809
#> 158 -0.621266695 0 -2.097882960 0.509665571
#> 159 -1.384426847 1 -1.004361979 -1.084720001
#> 160 1.869290622 1 0.535771722 0.704832977
#> 161 0.425100377 1 -0.453037085 0.330976350
#> 162 -0.238647101 0 2.165368502 0.976327473
#> 163 1.058483049 0 1.245746673 -0.843339880
#> 164 0.886422651 1 0.595498034 -0.970579905
#> 165 -0.619243048 1 0.004884450 -1.771531349
#> 166 2.206102465 1 0.279360782 -0.322470342
#> 167 -0.255027030 0 -0.705906125 -1.338800742
#> 168 -1.424494650 1 0.628017153 0.688156028
#> 169 -0.144399602 0 1.480213960 0.071280652
#> 170 0.207538339 1 1.083429910 2.189752359
#> 171 2.307978399 0 -0.813244257 -1.157707599
#> 175 -0.334000842 1 1.350993980 0.572967370
#> 176 -0.034726028 0 -1.318609485 -1.433377705
#> 177 0.787639606 1 0.364384593 -1.055185019
#> 178 2.075245009 0 0.233499835 -0.733111877
#> 179 1.027392439 1 1.193955261 0.210907264
#> 180 1.207908398 1 -0.027909972 -0.998920727
#> 181 -1.231323422 0 -0.357298855 1.077850323
#> 182 0.983895570 1 -1.146814136 -1.198974383
#> 183 0.219924804 1 -0.517420484 0.216637035
#> 184 -1.467250029 0 -0.362123773 0.143087030
#> 185 0.521022743 0 2.350554326 -1.065750091
#> 187 1.464587312 0 -0.166703279 -0.656179477
#> 188 -0.766082000 0 -1.043667439 0.959394327
#> 189 -0.430211754 1 -1.972934934 1.556052636
#> 190 -0.926109497 1 0.514671633 -1.040796434
#> 191 -0.177103961 0 -1.090573584 0.930572409
#> 192 0.402011779 0 2.284659326 -0.075445931
#> 193 -0.731748173 1 -0.885617573 -1.967195349
#> 194 0.830373168 1 0.111106430 -0.755903643
#> 195 -1.208082786 1 3.810276681 0.461149161
#> 196 -1.047984413 1 -1.108909998 0.145106631
#> 198 -1.015847465 0 -1.106894472 0.580318685
#> 199 0.411974712 0 0.347653649 0.655051998
#> 200 -0.381076051 0 -0.873264535 -0.304508837
#>
#> $n_treated
#> [1] 87
#>
#> $n_matched_control
#> [1] 87
#>
#> $match_pairs
#> treated_idx control_idx distance
#> 1 1 82 0.22732872
#> 2 3 185 0.38077558
#> 3 4 33 0.06862290
#> 4 5 162 0.48182659
#> 5 6 55 0.07373736
#> 6 8 68 0.08746549
#> 7 10 53 0.05367455
#> 8 11 49 0.38204048
#> 9 13 71 0.06591694
#> 10 14 110 0.17247108
#> 11 15 74 0.07086416
#> 12 19 119 0.16809201
#> 13 20 38 0.46657399
#> 14 23 100 0.12996282
#> 15 28 76 0.25886626
#> 16 29 96 0.09973289
#> 17 30 112 0.18508867
#> 18 31 107 0.30066354
#> 19 36 144 0.15998990
#> 20 37 16 0.13232813
#> 21 40 17 0.11848668
#> 22 44 184 0.13647130
#> 23 48 39 0.16178096
#> 24 51 22 0.20120495
#> 25 56 132 0.23959118
#> 26 58 86 0.11623224
#> 27 59 87 0.39186214
#> 28 66 125 0.40060546
#> 29 67 52 0.02203514
#> 30 70 75 0.18906631
#> 31 72 199 0.19740739
#> 32 73 63 0.19990382
#> 33 77 43 0.12365345
#> 34 80 97 0.36913621
#> 35 81 135 0.25518959
#> 36 83 153 0.29414601
#> 37 84 123 0.23566190
#> 38 89 24 0.16572986
#> 39 92 25 0.14859911
#> 40 99 188 0.31200309
#> 41 102 93 0.77080017
#> 42 103 18 0.47209704
#> 43 104 138 0.34907829
#> 44 106 139 0.29525502
#> 45 108 27 0.09229952
#> 46 109 137 1.37876042
#> 47 111 105 0.29583599
#> 48 114 167 0.08752812
#> 49 115 78 0.70614515
#> 50 117 101 0.06605420
#> 51 120 91 0.36818669
#> 52 121 34 0.20551360
#> 53 122 148 0.27419177
#> 54 126 163 0.41752538
#> 55 127 32 0.45082052
#> 56 128 200 0.10717824
#> 57 129 69 0.30215314
#> 58 130 9 0.38765688
#> 59 131 61 0.52697900
#> 60 140 88 0.30169972
#> 61 142 169 0.47607648
#> 62 143 181 0.46684618
#> 63 147 191 0.27580956
#> 64 150 158 0.79083427
#> 65 151 62 0.33842187
#> 66 154 176 0.22346009
#> 67 155 46 0.31272035
#> 68 159 171 0.19066853
#> 69 160 85 0.61151262
#> 70 161 134 0.49342268
#> 71 164 94 0.28309790
#> 72 165 118 0.24336985
#> 73 166 149 0.04022053
#> 74 168 157 0.68283957
#> 75 170 35 1.46340588
#> 76 175 50 0.52803565
#> 77 177 12 0.27093412
#> 78 179 113 0.58769120
#> 79 180 136 0.32312737
#> 80 182 90 0.54336588
#> 81 183 42 0.52631268
#> 82 189 124 0.29211034
#> 83 190 178 0.39082832
#> 84 193 57 0.45733875
#> 85 194 187 0.27518108
#> 86 195 192 1.55449375
#> 87 196 198 0.42424953
#>
#> $method
#> [1] "mahalanobis (rmorie native)"
#>
#> $details
#> $details$engine
#> [1] "native-greedy-kd-whitened"
#>
#> $details$caliper
#> NULL
#>
#> $details$replace
#> [1] FALSE
#>
#> $details$exact_vars
#> NULL
#>
#> $details$n_neighbors
#> [1] 1
#>
#>
#> attr(,"class")
#> [1] "morie_match_result" "list"
# }