This function adds measured data columns that can be functions of unmeasured data columns.
Usage
declare_measurement(..., handler = measurement_handler, label = NULL)
measurement_handler(data, ...)Arguments
- ...
arguments to be captured, and later passed to the handler
- handler
a tidy-in, tidy-out function
- label
a string describing the step
- data
A data.frame.
Details
It is also possible to include measured variables in your declare_model call or to add variables using declare_step. However, putting latent variables in declare_model and variables-as-measured in declare_measurement helps communicate which parts of your research design are in M and which parts are in D.
Examples
# declare_measurement in use
## Two-arm randomized experiment
design <-
declare_model(
N = 500,
X = rep(c(0, 1), each = N / 2),
U = rnorm(N, sd = 0.25),
potential_outcomes(Y ~ 0.2 * Z + X + U)
) +
declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0)) +
declare_sampling(S = complete_rs(N = N, n = 200)) +
declare_assignment(Z = complete_ra(N = N, m = 100)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z)) +
declare_estimator(Y ~ Z, inquiry = "ATE")
run_design(design)
#> inquiry estimand estimator term estimate std.error statistic p.value
#> 1 ATE 0.2 estimator Z 0.1343672 0.08278017 1.623181 0.1061418
#> conf.low conf.high df outcome
#> 1 -0.02887678 0.2976111 198 Y
# Reveal potential outcomes according to treatment assignment
design <-
declare_model(N = 100,
potential_outcomes(Y ~ rbinom(
N, size = 1, prob = 0.1 * Z + 0.5
))) +
declare_assignment(Z = complete_ra(N, m = 50)) +
declare_measurement(Y = reveal_outcomes(Y ~ Z))
head(draw_data(design))
#> ID Y_Z_0 Y_Z_1 Z Y
#> 1 001 1 0 0 1
#> 2 002 1 1 0 1
#> 3 003 0 0 0 0
#> 4 004 1 1 1 1
#> 5 005 1 1 0 1
#> 6 006 1 0 0 1
# Generate observed measurement from a latent value
design <-
declare_model(N = 100, latent = runif(N)) +
declare_measurement(observed = rbinom(N, 1, prob = latent))
head(draw_data(design))
#> ID latent observed
#> 1 001 0.85799465 1
#> 2 002 0.65768689 1
#> 3 003 0.04111667 0
#> 4 004 0.08502660 0
#> 5 005 0.67294424 0
#> 6 006 0.37739894 0
# Index creation
library(psych)
design <-
declare_model(
N = 500,
X = rep(c(0, 1), each = N / 2),
Y_1 = 0.2 * X + rnorm(N, sd = 0.25),
Y_2 = 0.3 * X + 0.5 * rnorm(N, sd = 0.50),
Y_3 = 0.1 * X + 0.4 * rnorm(N, sd = 0.75)) +
declare_measurement(
index = fa(
r = cbind(Y_1, Y_2, Y_3),
nfactors = 1,
rotate = "varimax"
)$scores
)
draw_data(design)
#> ID X Y_1 Y_2 Y_3 MR1
#> 1 001 0 -0.2432641846 0.022031261 0.3338628478 -0.426159570
#> 2 002 0 0.1489107558 0.017082577 -0.1876286329 -0.427686004
#> 3 003 0 0.2347926889 -0.183423370 0.3127436184 -1.138190355
#> 4 004 0 -0.0934134497 0.379648159 0.3717280497 0.783825034
#> 5 005 0 0.3949096222 -0.444719430 -0.0770785600 -2.011565075
#> 6 006 0 -0.0201315515 0.117211641 -0.1201636863 -0.085547075
#> 7 007 0 -0.5973338081 0.212894794 0.1899633847 0.241086878
#> 8 008 0 -0.0269761478 -0.696345090 0.2139565749 -2.869940085
#> 9 009 0 0.0353797438 0.329510636 0.2587229311 0.615292275
#> 10 010 0 -0.1048781306 0.102563417 0.2834044157 -0.153750907
#> 11 011 0 0.0846300701 0.207896370 0.1594165609 0.205299669
#> 12 012 0 -0.3154070644 -0.252194598 0.1420986094 -1.346839453
#> 13 013 0 -0.2177552048 0.210856786 0.2435492791 0.220041588
#> 14 014 0 -0.6315128641 -0.461433297 0.0109882529 -2.042323911
#> 15 015 0 0.1472454940 0.109873512 0.2358991370 -0.133954608
#> 16 016 0 0.0087485309 -0.107978151 0.0414175595 -0.860756981
#> 17 017 0 0.2935792319 -0.175911097 0.2718136272 -1.112272718
#> 18 018 0 0.1549847490 0.060897782 -0.2252405859 -0.276882720
#> 19 019 0 -0.1123285835 0.250603393 0.0371781464 0.362782021
#> 20 020 0 -0.0095894937 0.086326046 0.7062239648 -0.233734651
#> 21 021 0 -0.1683284051 0.148834427 0.0683696971 0.016674711
#> 22 022 0 0.5037635202 -0.867327398 -0.3030993504 -3.440592632
#> 23 023 0 -0.1024661726 -0.154567972 0.3265619601 -1.030682867
#> 24 024 0 0.5202312761 0.108766578 -0.3327058337 -0.119399321
#> 25 025 0 -0.0195100735 0.391454722 -0.0152032563 0.841824924
#> 26 026 0 -0.5945966907 0.016738913 -0.3520235515 -0.398131411
#> 27 027 0 0.2759267899 -0.249137440 0.0276357294 -1.348171243
#> 28 028 0 -0.2697786530 0.139347660 0.0459415162 -0.011402205
#> 29 029 0 0.0136957799 0.378076904 0.2049357836 0.783923237
#> 30 030 0 -0.1404540513 0.011149694 0.5677756536 -0.478362820
#> 31 031 0 0.1363860535 -0.169846845 -0.2094408925 -1.062016021
#> 32 032 0 -0.2685274187 0.219661655 -0.5179873106 0.290963814
#> 33 033 0 -0.2515770632 -0.085126810 -0.2760843019 -0.758803622
#> 34 034 0 0.4417453270 -0.208338061 -0.2079909242 -1.202137767
#> 35 035 0 -0.1913080888 -0.233411772 -0.0789359470 -1.275204568
#> 36 036 0 0.0708053726 0.769350651 0.4164150782 2.102165142
#> 37 037 0 -0.0317078241 -0.115268338 0.0700673459 -0.885830404
#> 38 038 0 0.0083360888 0.262782079 0.2526761353 0.389438302
#> 39 039 0 -0.2202999595 -0.010630455 -0.0523811539 -0.517931286
#> 40 040 0 -0.0796833786 0.303205095 -0.4387286919 0.565387417
#> 41 041 0 0.0738873065 -0.081507480 0.0960110202 -0.775491912
#> 42 042 0 -0.1984017761 0.392411810 -0.4403904041 0.872452897
#> 43 043 0 0.5170559792 -0.184617408 0.0678544250 -1.137993990
#> 44 044 0 -0.0311323812 -0.400396076 0.0678277977 -1.855584074
#> 45 045 0 -0.0132521365 0.265555088 -0.1085810783 0.418232650
#> 46 046 0 0.0606858607 -0.295939907 -0.0353787955 -1.497674893
#> 47 047 0 0.0698126733 0.132669764 0.0403500626 -0.043969567
#> 48 048 0 -0.0955417446 0.428606007 0.0760894958 0.965734571
#> 49 049 0 -0.0723655633 0.103399594 0.3172202074 -0.153629767
#> 50 050 0 0.1454499684 0.021187540 -0.1457777170 -0.415788107
#> 51 051 0 -0.0729511621 0.303833496 0.3474835171 0.526588985
#> 52 052 0 0.7710605845 0.199838046 0.0776134676 0.161626577
#> 53 053 0 0.0905161753 -0.116516419 -0.5276775939 -0.862756047
#> 54 054 0 -0.4725337905 -0.042623817 -0.3461426281 -0.604002059
#> 55 055 0 -0.2683945086 0.475666954 -0.1056303751 1.140388516
#> 56 056 0 -0.0811096778 0.222048158 -0.2085385685 0.277450896
#> 57 057 0 -0.1132267930 -0.130678565 -0.1472371565 -0.924554211
#> 58 058 0 -0.2871366947 0.200083763 0.4707912529 0.173695945
#> 59 059 0 -0.2240969970 -0.569352650 -0.1054157075 -2.415545041
#> 60 060 0 0.0212834894 0.199347355 0.3574364171 0.167852714
#> 61 061 0 0.4173543910 0.614421734 -0.0434261600 1.588654431
#> 62 062 0 0.0696536978 0.134068455 0.1262643304 -0.043658569
#> 63 063 0 0.5140058055 -0.165547162 -0.4510057450 -1.046153150
#> 64 064 0 0.2874584130 0.241336774 0.2433525065 0.308639457
#> 65 065 0 -0.6164292389 0.011791750 0.1054996743 -0.438011930
#> 66 066 0 -0.0474594339 -0.540941264 -0.6653118852 -2.295171144
#> 67 067 0 -0.0851273648 0.016414623 -0.5851532047 -0.402371766
#> 68 068 0 -0.0997664663 -0.157135308 -0.0844976968 -1.018198585
#> 69 069 0 -0.0983699426 -0.304792004 0.7131326498 -1.561816071
#> 70 070 0 -0.2942987228 -0.168517558 -0.0707559913 -1.051816899
#> 71 071 0 -0.2342953794 -0.045385099 0.1242561241 -0.644881901
#> 72 072 0 -0.5123561370 0.069302252 0.2964247804 -0.255392295
#> 73 073 0 -0.2426594185 0.453790120 -0.2066297880 1.070439506
#> 74 074 0 -0.0690292650 -0.177225841 0.0275570683 -1.093259605
#> 75 075 0 -0.6387459809 0.345692036 0.4345831137 0.681354999
#> 76 076 0 0.2763092782 0.028714059 -0.1068702001 -0.396111077
#> 77 077 0 0.0489900471 0.093693706 0.5239981823 -0.200981976
#> 78 078 0 -0.1192567524 -0.235080531 0.1854386242 -1.296730371
#> 79 079 0 0.2255709237 -0.183730652 -0.0615159649 -1.119569187
#> 80 080 0 -0.2884415655 -0.012879349 0.4095408200 -0.547478547
#> 81 081 0 -0.6453887094 0.247168315 0.3375409542 0.351456137
#> 82 082 0 0.2600110357 0.073200568 -0.1804908717 -0.240490526
#> 83 083 0 0.3949402890 -0.420532787 -0.1511829025 -1.925456471
#> 84 084 0 0.1286301468 -0.381277600 -0.2503943635 -1.778837197
#> 85 085 0 0.1353611952 0.023974553 0.0367585946 -0.415464341
#> 86 086 0 0.0897180621 -0.045632180 -0.7209797190 -0.611606553
#> 87 087 0 0.2287571752 -0.374234328 -0.6660429977 -1.736334804
#> 88 088 0 -0.1332201734 -0.045494798 0.0552149433 -0.644696678
#> 89 089 0 0.2224241195 -0.202808719 -0.5667552870 -1.158191399
#> 90 090 0 -0.1559644002 -0.196531754 -0.0514437638 -1.152238418
#> 91 091 0 0.1840198955 -0.426234333 -0.2369293494 -1.934107970
#> 92 092 0 0.2512064258 -0.043170352 -0.1568731358 -0.637283049
#> 93 093 0 -0.1641797320 0.037187748 0.1171273978 -0.365737873
#> 94 094 0 0.1907086883 0.302498320 0.1035237761 0.526812744
#> 95 095 0 -0.0670205117 -0.146414806 -0.0415227071 -0.984937760
#> 96 096 0 -0.1218226072 -0.417609555 -0.1977882719 -1.897664573
#> 97 097 0 -0.2680359686 0.031977751 0.2407701514 -0.386763794
#> 98 098 0 -0.1142622085 -0.045346195 0.4771787999 -0.666620008
#> 99 099 0 0.2951934220 -0.063142496 0.0820890862 -0.718912366
#> 100 100 0 -0.4823802482 -0.304824429 0.0227840180 -1.514689289
#> 101 101 0 0.2607981818 0.138236842 -0.2852521843 -0.013867472
#> 102 102 0 -0.3572808209 -0.111722109 0.0654732976 -0.863806256
#> 103 103 0 -0.0731736238 0.387690677 0.1419009314 0.822484588
#> 104 104 0 0.0836641757 -0.355477268 0.0629604086 -1.705970633
#> 105 105 0 0.2056274841 -0.092421816 -0.5811465577 -0.781466729
#> 106 106 0 -0.2317792220 -0.111207278 -0.0540676430 -0.859609778
#> 107 107 0 0.4236045535 -0.062396334 0.2141005548 -0.727049232
#> 108 108 0 0.0812596502 -0.144754781 -0.1834958583 -0.976364014
#> 109 109 0 -0.2755698120 0.039448330 0.1460251507 -0.356218977
#> 110 110 0 -0.0409975046 -0.463817583 -0.2294889317 -2.055611153
#> 111 111 0 -0.0220115249 0.377600807 0.1346015336 0.787014390
#> 112 112 0 0.1844273021 -0.083722843 -0.2669583961 -0.767522849
#> 113 113 0 -0.3781345036 0.002755259 0.2424905931 -0.482964005
#> 114 114 0 -0.0533830958 -0.304191991 0.2350916237 -1.536350720
#> 115 115 0 0.1087758491 0.207899344 -0.1897824368 0.222681163
#> 116 116 0 -0.0470473274 -0.345699811 0.2871595080 -1.680425130
#> 117 117 0 0.1202415211 0.278454646 0.5877737710 0.422043870
#> 118 118 0 -0.1441681760 -0.186076572 -0.2531701849 -1.106576026
#> 119 119 0 -0.0140115743 0.161866732 0.0753204366 0.056034622
#> 120 120 0 0.1181808960 -0.032746717 0.0693756742 -0.609576679
#> 121 121 0 0.2043206229 0.401634765 0.5415188759 0.840922332
#> 122 122 0 -0.1774226090 0.571524634 0.2294509771 1.446367134
#> 123 123 0 -0.0904940733 -0.125807932 0.0121260527 -0.916922714
#> 124 124 0 0.2091946119 0.383946059 0.3058262488 0.792820808
#> 125 125 0 0.1037604494 0.249149256 0.1880949257 0.343562760
#> 126 126 0 0.2376159216 -0.426018073 0.1810748767 -1.956630534
#> 127 127 0 -0.3898660249 0.250191139 0.2626834781 0.357985067
#> 128 128 0 -0.0233386256 0.167798484 -0.3821198093 0.100190519
#> 129 129 0 -0.3691236231 -0.108322267 0.0673425697 -0.851984951
#> 130 130 0 0.2716136333 0.314981399 0.5252857084 0.545005044
#> 131 131 0 -0.1901646538 0.052567041 -0.0008245592 -0.306538370
#> 132 132 0 0.5093533894 0.427280977 0.0630824538 0.943835307
#> 133 133 0 0.1797906466 0.076680549 -0.1864558320 -0.225948526
#> 134 134 0 -0.0286613594 0.125003024 0.1146778371 -0.070957683
#> 135 135 0 0.0161415053 0.172566250 -0.4990294025 0.121286042
#> 136 136 0 -0.0200841842 -0.240679495 0.2272998849 -1.320905896
#> 137 137 0 -0.2413515702 -0.313661328 0.2154885301 -1.561930717
#> 138 138 0 -0.3318736259 -0.457855454 0.3560732682 -2.056982564
#> 139 139 0 -0.4739405084 0.213149997 -0.0532274436 0.250869695
#> 140 140 0 0.1480106082 -0.332057806 0.2318806105 -1.636983930
#> 141 141 0 0.2429479877 -0.044078496 -0.0675368388 -0.644754067
#> 142 142 0 -0.4760243389 0.107913985 -0.1890875327 -0.099985745
#> 143 143 0 -0.1278134026 -0.122933788 -0.3048457485 -0.889609021
#> 144 144 0 0.2613777644 0.228972505 -0.0823365645 0.284236193
#> 145 145 0 -0.5184815250 -0.292921090 0.2325449489 -1.483990288
#> 146 146 0 0.0607670058 -0.017567445 -0.2870004174 -0.537765750
#> 147 147 0 -0.3142871302 0.061471980 -0.2528280492 -0.259484720
#> 148 148 0 -0.3784020578 -0.094234634 -0.0390891155 -0.798274834
#> 149 149 0 0.5259169721 0.281405852 -0.1807915290 0.459786704
#> 150 150 0 -0.0541422351 -0.083099876 0.0050217808 -0.772370305
#> 151 151 0 -0.0466024832 -0.109702459 0.1921678898 -0.872779613
#> 152 152 0 -0.0963853294 0.237731550 -0.4579318412 0.344175104
#> 153 153 0 -0.2718316041 -0.146723336 -0.1888939683 -0.972234687
#> 154 154 0 0.2379167542 0.054758764 -0.1090647400 -0.306260525
#> 155 155 0 -0.0617328124 0.200979072 -0.1166123589 0.200443543
#> 156 156 0 -0.0413231673 -0.014589069 -0.4977584557 -0.513666128
#> 157 157 0 -0.0563923906 -0.317197986 -0.0644815745 -1.564978960
#> 158 158 0 -0.3185076449 -0.170623388 -0.0296824265 -1.060384857
#> 159 159 0 -0.1082837103 -0.416218160 -0.0773302895 -1.899577260
#> 160 160 0 -0.3358075756 0.527442148 -0.0474801269 1.315500659
#> 161 161 0 0.0835596907 0.153317829 -0.3926599625 0.048288466
#> 162 162 0 -0.2102432394 -0.014573333 0.1393730537 -0.541578289
#> 163 163 0 -0.4010660809 -0.587136300 -0.1699604193 -2.467405921
#> 164 164 0 -0.0539029905 0.187902267 -0.2670973636 0.163526233
#> 165 165 0 -0.2553365922 0.309373565 -0.3065501887 0.584767050
#> 166 166 0 0.4228436633 0.280195984 0.6358522900 0.416438267
#> 167 167 0 -0.0326226631 -0.167671531 0.0378559497 -1.062381879
#> 168 168 0 -0.3095759618 0.187638637 -0.2230462598 0.167983220
#> 169 169 0 0.4013729592 -0.099002987 0.2448520212 -0.852494843
#> 170 170 0 0.3461798111 0.082135945 0.3651116432 -0.240939197
#> 171 171 0 0.1168837322 0.176969583 -0.3613905204 0.126123730
#> 172 172 0 0.0248471635 -0.124311299 0.4983247974 -0.940467505
#> 173 173 0 0.0716114127 -0.089219502 -0.1757609742 -0.787575209
#> 174 174 0 -0.1875101601 0.013692036 0.2378760715 -0.451217126
#> 175 175 0 -0.0630743470 -0.491682717 0.3021436791 -2.177278812
#> 176 176 0 -0.3100002065 0.055661165 0.1469937224 -0.300093424
#> 177 177 0 0.0317536188 -0.185455400 -0.2232815498 -1.111265884
#> 178 178 0 -0.5431924308 -0.285405515 -0.0272845020 -1.444226123
#> 179 179 0 -0.5595913602 0.202391762 0.1562062325 0.205982935
#> 180 180 0 -0.0636263437 -0.146589107 0.3816170322 -1.007555522
#> 181 181 0 0.0185278985 -0.120917914 0.3912404906 -0.923188084
#> 182 182 0 -0.1696928832 -0.114192250 -0.3290685947 -0.857369149
#> 183 183 0 0.0832050300 0.129764596 -0.1642270131 -0.043651935
#> 184 184 0 0.1546306927 0.279416462 -0.0400549943 0.456817129
#> 185 185 0 0.1029862366 0.390808427 -0.1265559880 0.841737383
#> 186 186 0 -0.0821388772 -0.401375183 0.3729959031 -1.873202307
#> 187 187 0 0.0445414149 -0.003285305 -0.3733273364 -0.484228195
#> 188 188 0 0.0603081560 -0.340085039 -0.2989459765 -1.634165996
#> 189 189 0 0.1857185107 -0.101877820 0.3534083961 -0.861457110
#> 190 190 0 0.2910911086 0.002579157 0.1332933359 -0.497892796
#> 191 191 0 0.2136166741 0.114040863 -0.2215556755 -0.098060352
#> 192 192 0 0.0575021742 -0.071602702 -0.0619650122 -0.733126796
#> 193 193 0 -0.0543361452 -0.058561466 0.1270959793 -0.695222777
#> 194 194 0 -0.2661579486 0.176601464 -0.0068203284 0.117940903
#> 195 195 0 0.1779116866 -0.114013636 0.8954267688 -0.930586315
#> 196 196 0 -0.2538252029 0.084005901 -0.5233933832 -0.170623728
#> 197 197 0 0.1422292940 -0.519543040 -0.3178724231 -2.246052511
#> 198 198 0 0.4591911270 0.520897966 -0.2344292550 1.279183086
#> 199 199 0 0.2038105628 -0.110650255 -0.1117148939 -0.867737859
#> 200 200 0 0.0219724039 0.142213669 -0.1340709228 -0.001040454
#> 201 201 0 0.2726744950 -0.383882452 -0.4902696970 -1.779571317
#> 202 202 0 -0.0974905084 0.038404953 0.6333590430 -0.390336111
#> 203 203 0 -0.0355597159 0.048883963 -0.1270976692 -0.317141351
#> 204 204 0 0.3225628545 -0.204216893 0.7415683668 -1.233758287
#> 205 205 0 0.2949746842 -0.258727337 -0.1205413989 -1.373682571
#> 206 206 0 -0.3271238540 -0.254794621 -0.1738809548 -1.338962024
#> 207 207 0 0.3136961986 0.162641789 0.3405360995 0.035142223
#> 208 208 0 -0.0354329910 -0.075181136 0.6116029876 -0.777421644
#> 209 209 0 0.4180261846 0.006968354 -0.0709555654 -0.476171758
#> 210 210 0 -0.2831444641 0.096774614 -0.2519445066 -0.140379889
#> 211 211 0 -0.4138448975 0.185700013 -0.5848785507 0.183250288
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#> 463 463 1 0.4109159996 0.199816150 0.2589381000 0.162913672
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#> 470 470 1 0.3373625540 0.514819859 -0.0212931262 1.251104303
#> 471 471 1 0.0837868777 0.817744799 0.3184427238 2.271464612
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#> 475 475 1 0.1298449072 0.445379101 0.0889426862 1.015390225
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#> 477 477 1 0.0876669301 0.403586763 0.2370091924 0.866823139
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#> 479 479 1 0.0216252407 0.103374614 -0.1077563535 -0.134503236
#> 480 480 1 0.3025331304 0.527459256 -0.0347833864 1.295835975
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#> 483 483 1 0.4909810727 0.392907555 -0.2271685452 0.842502316
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#> 486 486 1 0.3240247770 0.354547735 -0.0954172735 0.710182843
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#> 488 488 1 0.0660641826 0.349617778 -0.1525237537 0.704076923
#> 489 489 1 0.0347062807 -0.037198399 -0.8495396436 -0.574615432
#> 490 490 1 0.7486671376 0.195557458 -0.0959580629 0.156728117
#> 491 491 1 0.5892582283 0.550019134 0.1881355228 1.352459393
#> 492 492 1 0.3903510001 0.394928342 -0.3037078947 0.856347051
#> 493 493 1 -0.2501269669 0.413744014 -0.2490886358 0.936646897
#> 494 494 1 0.3297015855 0.177710738 0.3994417719 0.082868733
#> 495 495 1 0.1355836963 0.170377407 0.1575036441 0.076257468
#> 496 496 1 0.5992158146 0.666538911 -0.1660310013 1.766850045
#> 497 497 1 0.2932911317 0.412117811 -0.2571940622 0.915305349
#> 498 498 1 0.3633587945 -0.021891595 -0.0275181186 -0.574955748
#> 499 499 1 0.1518543343 0.331943433 0.1077452390 0.627911042
#> 500 500 1 0.1309996830 0.191635339 0.0546244899 0.154032850