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, ...)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 0.02291571 0.08087335 0.2833531 0.7772022
#> conf.low conf.high df outcome
#> 1 -0.1365679 0.1823994 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 0 1 1 1
#> 2 002 0 0 0 0
#> 3 003 1 1 1 1
#> 4 004 1 0 1 0
#> 5 005 0 0 0 0
#> 6 006 0 1 1 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 0.8554179 1
#> 2 002 0.6752466 1
#> 3 003 0.5269439 1
#> 4 004 0.3955966 1
#> 5 005 0.1413085 0
#> 6 006 0.1830983 1
if(require("psych")) {
# 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)
}
#> Loading required package: psych
#> ID X Y_1 Y_2 Y_3 MR1
#> 1 001 0 -0.0711851459 -0.342287729 0.1845648072 -0.6836113830
#> 2 002 0 0.0303726277 0.256863266 -0.3870754270 -0.2532872743
#> 3 003 0 -0.3552338225 -0.136984191 0.4922903784 -0.6059223568
#> 4 004 0 0.0841552269 0.631810865 -0.3324421071 0.2713721634
#> 5 005 0 -0.0565980652 -0.188426429 0.6191836361 -0.2102122730
#> 6 006 0 -0.5463059462 -0.318410203 -0.2113643938 -1.5043508970
#> 7 007 0 0.0655355159 0.342037724 0.2486451797 0.2987888102
#> 8 008 0 -0.1973674653 0.184853512 0.2409842843 -0.2101371482
#> 9 009 0 0.0573134367 -0.081006975 -0.1630925049 -0.4550607956
#> 10 010 0 -0.2870543501 -0.380363010 0.4041825763 -0.8524462016
#> 11 011 0 -0.0023161131 0.012661109 -0.1356342723 -0.4058854641
#> 12 012 0 -0.1224885224 0.153224509 0.6695776482 0.1253855747
#> 13 013 0 0.6023252515 -0.437681126 -0.1508108962 -0.1711635589
#> 14 014 0 0.4424084292 -0.410382864 -0.0935728242 -0.3021291563
#> 15 015 0 0.0462677317 -0.055825728 0.2665124265 -0.1617326041
#> 16 016 0 -0.0254969821 0.306185068 0.0277778794 0.0019272965
#> 17 017 0 0.1338660915 0.138852806 0.0219950519 0.0078504490
#> 18 018 0 0.0954012372 -0.229986399 0.4677568939 -0.1662592747
#> 19 019 0 -0.4880787443 -0.195264315 -0.2885412477 -1.3433043499
#> 20 020 0 -0.2020671072 -0.138949793 -0.3802740906 -0.9837439744
#> 21 021 0 0.1008427230 -0.610803227 -0.2963350642 -1.0841371591
#> 22 022 0 -0.0501069098 0.140938506 0.7164125578 0.2319319445
#> 23 023 0 -0.1395411537 -0.115462241 0.2241528911 -0.4874290657
#> 24 024 0 -0.0709685739 -0.020906659 0.1933744652 -0.3156227006
#> 25 025 0 0.3782846176 0.219666478 0.1969915739 0.5163242492
#> 26 026 0 -0.4299557490 -0.149558121 0.2873378623 -0.8459570503
#> 27 027 0 0.0793679079 -0.098636933 -0.3644412241 -0.5781172894
#> 28 028 0 0.1366698489 -0.437788523 0.0686944817 -0.6078977647
#> 29 029 0 -0.0174783608 0.004652866 0.0167272458 -0.3349174253
#> 30 030 0 -0.2723749495 -0.575302728 -0.0079493814 -1.3211371158
#> 31 031 0 0.0263624401 -0.042085339 0.3881976494 -0.0920652474
#> 32 032 0 -0.5018631450 0.071513607 0.2726423457 -0.6958872394
#> 33 033 0 -0.3007015960 -0.189726799 0.2821510390 -0.7338485039
#> 34 034 0 0.5808254462 -0.046296346 0.6041266324 0.7327117916
#> 35 035 0 -0.2141574884 -0.225159281 -0.4321311010 -1.1295254723
#> 36 036 0 -0.2659503190 -0.342280295 0.0456378630 -1.0159077746
#> 37 037 0 0.1649542375 0.238628636 0.1002235321 0.2096436638
#> 38 038 0 -0.1099155676 -0.257851218 0.0223714973 -0.7418808472
#> 39 039 0 0.1380971115 -0.415083741 -0.1004439744 -0.6902739346
#> 40 040 0 0.3573634745 0.314575296 0.6173572991 0.8699204519
#> 41 041 0 -0.1180036914 0.128147537 0.3398451944 -0.1111926893
#> 42 042 0 0.4315495388 -0.302997337 -0.0478118594 -0.1649856183
#> 43 043 0 -0.0294006704 0.030288929 0.3813271664 -0.0843377863
#> 44 044 0 0.2862012714 0.098949394 -0.1043835505 0.0703365790
#> 45 045 0 -0.0080050091 0.134351978 0.2453814225 -0.0287091941
#> 46 046 0 -0.1697414571 -0.151450507 -0.2551991865 -0.8764897315
#> 47 047 0 -0.1823218250 0.189280715 -0.0436395091 -0.3710866929
#> 48 048 0 0.1668963233 -0.354687345 0.2314651070 -0.3711131190
#> 49 049 0 0.2725762947 -0.007523067 0.2338727938 0.1529018206
#> 50 050 0 0.2822248727 -0.110582116 0.0522616180 -0.0690037422
#> 51 051 0 -0.0302115473 -0.032078080 0.5790590380 -0.0273213154
#> 52 052 0 -0.0457538934 0.003391563 -0.1481670279 -0.4784692769
#> 53 053 0 0.2520334650 -0.192484679 0.3147622913 -0.0285073998
#> 54 054 0 -0.2481922974 -0.463415261 0.0743199951 -1.1116664203
#> 55 055 0 -0.1431178295 0.450940179 0.1919586562 0.1252349346
#> 56 056 0 -0.1181823984 0.496530428 -0.2823837236 -0.1001277263
#> 57 057 0 -0.0137684404 -0.136123720 -0.1415800094 -0.5915744405
#> 58 058 0 -0.1671253270 -0.246963030 -0.5534196003 -1.1742874732
#> 59 059 0 -0.2799181853 0.041226719 -0.0565490393 -0.6675848337
#> 60 060 0 -0.0089071650 -0.059127787 -0.1195350740 -0.4845001110
#> 61 061 0 -0.5031008097 -0.073886739 -0.0954357223 -1.0999896008
#> 62 062 0 0.2796582469 -0.148428913 0.0175312381 -0.1373566117
#> 63 063 0 -0.1379548659 0.122291238 0.0970235129 -0.3001235671
#> 64 064 0 0.1815171627 -0.108152423 -0.1846640500 -0.3451920473
#> 65 065 0 0.3082471342 -0.264977377 0.1297581572 -0.1602832972
#> 66 066 0 -0.0560541320 0.052112918 -0.4480379844 -0.6309331509
#> 67 067 0 0.1989341781 0.133916275 0.6869310968 0.5145635874
#> 68 068 0 0.2870642271 0.209833410 -0.0322568104 0.2431005387
#> 69 069 0 -0.1076486800 0.022722794 0.3621124112 -0.2026220543
#> 70 070 0 -0.0216898873 0.066078256 0.1218826452 -0.2027468005
#> 71 071 0 0.3777274453 0.434829756 0.0356092181 0.6532978250
#> 72 072 0 0.0048825335 -0.189628372 0.2178075719 -0.3955045010
#> 73 073 0 0.0517204851 0.082645426 -0.2929076884 -0.3618922055
#> 74 074 0 0.0543250246 -0.140577560 -0.4186102649 -0.6916397717
#> 75 075 0 -0.2274247544 -0.333874821 -0.0715953293 -1.0345886610
#> 76 076 0 -0.1155179090 -0.198739145 -0.7139163714 -1.1599168374
#> 77 077 0 -0.1847329578 -0.227334820 0.0161349243 -0.8045837455
#> 78 078 0 -0.1672260987 0.333350756 -0.1977519521 -0.2900130497
#> 79 079 0 -0.2048123568 0.093528716 0.3102595667 -0.2773217196
#> 80 080 0 0.2043944961 0.170687429 -0.0319485944 0.0964117182
#> 81 081 0 0.1553918779 0.005829733 -0.3394262947 -0.3496867640
#> 82 082 0 0.3994464801 0.079077321 -0.7187407495 -0.2097874130
#> 83 083 0 -0.0321254059 0.128763100 -0.2095884225 -0.3601504010
#> 84 084 0 0.0521757479 -0.207854702 0.1965356648 -0.3710282841
#> 85 085 0 0.2776987254 -0.157268451 0.7401841171 0.3190582967
#> 86 086 0 -0.1207415238 -0.066379789 0.4011232977 -0.2939598918
#> 87 087 0 -0.3375291216 0.216196423 0.1370398043 -0.4165468680
#> 88 088 0 -0.2743256100 -0.072822538 -0.5431106988 -1.1047444300
#> 89 089 0 0.0059689482 0.076417656 -0.0292951835 -0.2547823942
#> 90 090 0 0.0828229701 -0.332249236 -0.0572298308 -0.6376645198
#> 91 091 0 -0.0793200875 -0.048950049 0.3443336814 -0.2596632853
#> 92 092 0 -0.1144552078 0.435865366 0.4280178795 0.2970340816
#> 93 093 0 -0.2050161911 -0.139798743 0.0745246360 -0.6933245742
#> 94 094 0 -0.0955320684 0.031006362 -0.1828176181 -0.5317389318
#> 95 095 0 0.2453200779 -0.095937226 -0.2192585327 -0.2745404367
#> 96 096 0 0.3103369225 0.263955910 0.5616995019 0.7183210723
#> 97 097 0 -0.3225670798 0.221692631 -0.1280872345 -0.5637480572
#> 98 098 0 0.0370412044 -0.403564019 -0.3313362705 -0.9527421309
#> 99 099 0 0.2862848189 -0.031507409 -0.2910757158 -0.1976195915
#> 100 100 0 0.1146529054 -0.214170146 0.3388275821 -0.2081461862
#> 101 101 0 -0.0004401573 -0.166288810 0.1419378224 -0.4250523695
#> 102 102 0 0.2015556837 0.203762048 -0.2468927234 -0.0093040692
#> 103 103 0 -0.2381711680 0.209474873 -0.2106398406 -0.5261236553
#> 104 104 0 0.2372464442 -0.095202445 0.5834863919 0.2370147904
#> 105 105 0 -0.0730998146 -0.075490567 1.1617989767 0.2484921537
#> 106 106 0 0.0772960386 -0.019410887 -0.4018039135 -0.5156915397
#> 107 107 0 -0.5490274932 -0.011484512 -0.1410370599 -1.1163896411
#> 108 108 0 -0.3841564281 0.009027583 -0.0878932872 -0.8538017937
#> 109 109 0 0.1560720318 -0.035904536 0.1450251997 -0.0815712260
#> 110 110 0 -0.2302694140 0.199456661 -0.3737314181 -0.6333856374
#> 111 111 0 0.1620721762 -0.166358087 0.1888261318 -0.1926384771
#> 112 112 0 -0.4900328282 0.138198558 -0.0604844171 -0.8221730347
#> 113 113 0 -0.3336095175 0.051881039 -0.1387215035 -0.7756535511
#> 114 114 0 0.0756732358 0.004571091 -0.0607322149 -0.2694317909
#> 115 115 0 0.2092757342 0.103880560 0.4265865504 0.3246964402
#> 116 116 0 -0.1163875807 0.132409490 -0.1985765500 -0.4536741060
#> 117 117 0 0.2223069425 -0.244279350 0.1893152691 -0.2051936452
#> 118 118 0 0.2446646052 0.335990362 0.6110834515 0.7498383548
#> 119 119 0 0.0512367800 -0.077571182 0.0385365152 -0.3279433805
#> 120 120 0 0.0980853073 0.042169471 -0.1025675520 -0.2263528233
#> 121 121 0 0.1106054010 0.073901333 -0.0324886904 -0.1295796077
#> 122 122 0 0.3646613877 0.159432188 0.4473331724 0.5939412892
#> 123 123 0 0.4074772406 -0.094627309 0.4050082417 0.3335494570
#> 124 124 0 0.4973836956 -0.224115079 -0.1413288945 -0.0549393428
#> 125 125 0 0.2392339384 0.066619110 -0.1749059262 -0.0702314186
#> 126 126 0 -0.2477883595 -0.439919428 0.0165496096 -1.1221759155
#> 127 127 0 0.0554139495 0.610332204 0.0307036617 0.4470249890
#> 128 128 0 0.0924315419 0.348962161 -0.5719201280 -0.1922952467
#> 129 129 0 0.0844291047 -0.140414275 0.3632639720 -0.1467961614
#> 130 130 0 0.0258745769 0.206436645 0.2120329623 0.0729800465
#> 131 131 0 0.1373997818 -0.044958924 -0.1131226905 -0.2824567819
#> 132 132 0 0.0206050352 0.144753605 0.5261815930 0.2007461899
#> 133 133 0 -0.4122474741 -0.212001137 0.3285303528 -0.8675509486
#> 134 134 0 0.0697428292 -0.195928140 0.0458956251 -0.4334756827
#> 135 135 0 0.4219089933 -0.225162191 0.0944654560 0.0030005294
#> 136 136 0 0.1469610821 -0.105721217 -0.1952756405 -0.3923059484
#> 137 137 0 0.0847004475 0.543484612 0.0329230866 0.4095834772
#> 138 138 0 0.2667150380 -0.119504983 0.2351468991 0.0203036128
#> 139 139 0 -0.1457100070 0.188822872 -0.0079511309 -0.3029256872
#> 140 140 0 0.0299858483 -0.003105637 -0.0719355738 -0.3421562740
#> 141 141 0 -0.1389600320 -0.198854575 -0.0934169713 -0.7866576219
#> 142 142 0 0.1843027517 0.097309117 0.0473174557 0.0401978624
#> 143 143 0 0.0182861703 0.104178440 0.0057605680 -0.1854550926
#> 144 144 0 -0.2665027323 0.156595591 -0.0699339913 -0.5296350777
#> 145 145 0 0.0600914532 -0.274581219 -0.3486625408 -0.7900342119
#> 146 146 0 0.3381920251 -0.431928473 -0.4163711928 -0.6653945337
#> 147 147 0 0.0968917610 -0.560191507 -0.2918928148 -1.0291589607
#> 148 148 0 0.2346150139 0.022395619 0.0983860235 0.0515051458
#> 149 149 0 -0.0939349358 0.195291485 0.4143002616 0.0426677984
#> 150 150 0 0.5135308616 -0.100660860 -0.0276307570 0.1779573939
#> 151 151 0 0.1524074919 0.024468199 0.0174726540 -0.1008713504
#> 152 152 0 0.1219361364 0.214428418 -0.0328380651 0.0425727937
#> 153 153 0 -0.0053352464 0.086170010 -0.0092508862 -0.2448500006
#> 154 154 0 -0.1987521765 0.263983293 -0.1734840104 -0.3916060112
#> 155 155 0 -0.1341837233 0.263257332 -0.0516616289 -0.2331067630
#> 156 156 0 -0.5143661418 -0.132044148 -0.1088298287 -1.1881951506
#> 157 157 0 0.1463114091 0.131141658 -0.3328648923 -0.2155695640
#> 158 158 0 0.2498771262 -0.043203180 -0.0698408864 -0.1125422311
#> 159 159 0 -0.2196678172 0.347358400 -0.2448773310 -0.3700145805
#> 160 160 0 -0.2105492847 0.134165624 -0.1912863124 -0.5640507918
#> 161 161 0 0.2774456318 0.404983038 -0.4842168183 0.1577562523
#> 162 162 0 -0.2638734482 0.153371969 -0.1632059357 -0.5905053522
#> 163 163 0 -0.1413005377 -0.245594447 0.0510318036 -0.7485071822
#> 164 164 0 0.1755503299 0.507936202 -0.2411669963 0.3046967324
#> 165 165 0 -0.4035397310 0.223842768 0.5910388413 -0.1954902117
#> 166 166 0 -0.1543720770 0.130169303 0.3620937465 -0.1397038335
#> 167 167 0 0.1662326605 -0.118492879 -0.3223264918 -0.4651509677
#> 168 168 0 -0.0881969715 -0.483044361 -0.2211140838 -1.1264901128
#> 169 169 0 -0.2227794805 -0.060198110 0.2164620608 -0.5336704017
#> 170 170 0 -0.1738777260 0.161438384 0.0120456735 -0.3558238581
#> 171 171 0 -0.2872950783 0.040179139 0.1847404198 -0.5214052505
#> 172 172 0 0.3495145725 0.202803615 -0.2484665509 0.1725730170
#> 173 173 0 0.2612488557 0.039429690 -0.0070715305 0.0353961494
#> 174 174 0 0.0546835606 -0.024814183 0.3488159238 -0.0629436342
#> 175 175 0 -0.2857253270 -0.166894873 -0.5050740415 -1.2002069227
#> 176 176 0 0.3112809312 -0.363481592 -0.1774751360 -0.4667784908
#> 177 177 0 -0.4040996963 -0.032503550 -0.0054765432 -0.8719143446
#> 178 178 0 0.4909626920 -0.307135147 -0.3572910887 -0.2965391000
#> 179 179 0 0.0827506322 0.074686122 -0.4008206400 -0.4022801526
#> 180 180 0 0.2087533857 0.013442567 0.2397803190 0.1009901449
#> 181 181 0 -0.0403887774 0.196338055 -0.2153849248 -0.2980692030
#> 182 182 0 0.1252777573 -0.119430829 0.5762696786 0.0658160883
#> 183 183 0 0.1784075350 -0.640600080 -0.3565588158 -1.0603224800
#> 184 184 0 -0.2357382846 -0.086443944 -0.0901791393 -0.7782753041
#> 185 185 0 -0.0465656585 0.208873340 -0.0243771994 -0.1677171026
#> 186 186 0 -0.1868916817 0.126970956 0.2642259937 -0.2472325405
#> 187 187 0 -0.1083378624 0.033574221 0.2659166305 -0.2536612590
#> 188 188 0 -0.1233745345 0.015817799 -0.4785551652 -0.7753222550
#> 189 189 0 0.2399623504 -0.308282306 -0.1109352843 -0.4501155600
#> 190 190 0 -0.2119320300 0.169209330 -0.0749525998 -0.4508274025
#> 191 191 0 0.3397791388 0.008417233 -0.1275126663 0.0199771049
#> 192 192 0 0.4827431053 -0.051632900 -0.2320669251 0.0622755676
#> 193 193 0 0.5593447821 0.211467407 -0.0221107501 0.5900870601
#> 194 194 0 -0.0537742751 -0.348888141 -0.2675720390 -0.9627125807
#> 195 195 0 -0.4594839759 0.237510284 -0.0718607823 -0.6797027308
#> 196 196 0 0.0392008815 0.073989331 -0.2229544188 -0.3418288440
#> 197 197 0 -0.0974908450 0.296221136 -0.7109131183 -0.5780287506
#> 198 198 0 0.0446460143 -0.171569343 -0.1349009596 -0.5545328096
#> 199 199 0 -0.1803109530 -0.101908152 -0.2900299633 -0.8564234868
#> 200 200 0 0.0665561702 -0.273871703 -0.6267448445 -0.9615975347
#> 201 201 0 -0.1177876961 0.056497199 -0.1469902670 -0.5074571262
#> 202 202 0 0.3517464750 0.181188686 0.1464717689 0.4072104276
#> 203 203 0 0.2273076371 0.751332467 -0.0079363015 0.7945200617
#> 204 204 0 -0.0862197999 0.117476252 0.3367019967 -0.0857306517
#> 205 205 0 -0.3911270781 0.050647007 -0.3279669210 -0.9713327012
#> 206 206 0 -0.1545759686 -0.210216795 0.0856382202 -0.7027145921
#> 207 207 0 -0.2397026547 0.039954020 0.1662225349 -0.4744937629
#> 208 208 0 -0.1382327381 0.147423673 -0.1889884038 -0.4577050509
#> 209 209 0 0.0941213458 -0.020970192 -0.5408641792 -0.5867396206
#> 210 210 0 -0.1139270932 0.290576659 -0.5145548188 -0.4774401633
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#> 460 460 1 0.2707568337 0.017904105 0.4315711338 0.3075339670
#> 461 461 1 0.4015823028 0.666125037 0.1587880565 1.0234014219
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#> 463 463 1 0.5573184336 1.067155307 0.3706851085 1.8062353725
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#> 465 465 1 0.1080622083 0.126157331 -0.6744664513 -0.4903524119
#> 466 466 1 0.2751980205 0.072712121 0.4120132428 0.3621043278
#> 467 467 1 0.2352249965 0.186141893 0.5739283468 0.5452074556
#> 468 468 1 0.2974850375 0.491985471 0.2543561762 0.7598099377
#> 469 469 1 0.0441999202 0.597359032 0.2624334342 0.5687986440
#> 470 470 1 -0.1107830373 0.366780519 0.2076616746 0.0808306816
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#> 475 475 1 0.4263193980 0.469986502 0.3208238746 0.9383475036
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#> 477 477 1 0.2512374828 0.237711457 -0.0970156784 0.1879429273
#> 478 478 1 0.5319711283 0.099999816 0.3288504260 0.6581721905
#> 479 479 1 0.1676586725 0.366479657 0.0195753859 0.3046990413
#> 480 480 1 -0.0718772696 -0.003607032 -0.0461871286 -0.4526776368
#> 481 481 1 0.0397293277 0.460093482 -0.2216431623 0.0945873870
#> 482 482 1 0.4121970371 0.328432208 -0.0371146502 0.5291338764
#> 483 483 1 0.1528279718 0.411953911 0.4542321064 0.6194561690
#> 484 484 1 -0.0834311819 -0.118446539 -0.4788562978 -0.8770861572
#> 485 485 1 -0.0166471386 0.511384521 0.4129578182 0.4939472991
#> 486 486 1 0.6508246903 0.296819568 -0.3287369851 0.6010591016
#> 487 487 1 -0.2998416179 0.401840137 0.0121916514 -0.2415683952
#> 488 488 1 -0.0784465126 0.384483816 0.2003970109 0.1362673416
#> 489 489 1 0.3924913966 0.711043892 0.3467529809 1.1846330676
#> 490 490 1 -0.0295462039 0.030063151 -0.1781350664 -0.4477143322
#> 491 491 1 0.7084306401 0.524017535 0.6083254641 1.5365075113
#> 492 492 1 0.2281885203 0.623101669 0.1975115803 0.7844566535
#> 493 493 1 0.0812353836 0.342650049 -0.2306837744 0.0080441267
#> 494 494 1 0.2585399560 0.290922794 0.0508029945 0.3528552143
#> 495 495 1 0.6275251215 0.244102455 0.0735861701 0.7737069386
#> 496 496 1 -0.0785142581 0.205413005 0.3213127625 0.0129193496
#> 497 497 1 -0.0338927607 0.605359973 -0.3537614043 0.0809619903
#> 498 498 1 -0.1791019830 0.604331684 0.0359402851 0.1520572086
#> 499 499 1 0.0615829635 0.422404152 0.2305310190 0.3726477190
#> 500 500 1 -0.0462786527 0.085321415 0.0435432071 -0.2624671964