SimulateSMD.RdThis function simulates a meta-analytic dataset based on the random-effects model. The simulated effect size is Hedges' G, an estimator of the Standardized Mean Difference. The functional form of the model can be specified, and moderators can be either normally distributed or Bernoulli-distributed. See Van Lissa, 2018, for a detailed explanation of the simulation procedure.
SimulateSMD(
k_train = 20,
k_test = 100,
mean_n = 40,
es = 0.5,
tau2 = 0.04,
moderators = 5,
distribution = "normal",
model = es * x[, 1]
)Atomic integer. The number of studies in the training dataset. Defaults to 20.
Atomic integer. The number of studies in the testing dataset. Defaults to 100.
Atomic integer. The mean sample size of each simulated study in the meta-analytic dataset. Defaults to 40. For each simulated study, the sample size n is randomly drawn from a normal distribution with mean mean_n, and sd mean_n/3.
Atomic numeric vector. The effect size, also known as beta, used in the model statement. Defaults to .5.
Atomic numeric vector. The residual heterogeneity. Defaults to 0.04.
Atomic integer. The number of moderators to simulate for each study. Make sure that the number of moderators to be simulated is at least as large as the number of moderators referred to in the model parameter. Internally, the matrix of moderators is referred to as "x". Defaults to 5.
Atomic character. The distribution of the moderators. Can be set to either "normal" or "bernoulli". Defaults to "normal".
Expression. An expression to specify the model from which to
simulate the mean true effect size, mu. This formula may use the terms "es"
(referring to the es parameter of the call to SimulateSMD), and "x[, ]"
(referring to the matrix of moderators, x). Thus, to specify that the mean
effect size, mu, is a function of the effect size and the first moderator,
one would pass the value model = es * x[ , 1].
Defaults to es * x[ , 1].
List of length 4. The "training" element of this list is a data.frame with k_train rows. The columns are the variance of the effect size, vi; the effect size, yi, and the moderators, X. The "testing" element of this list is a data.frame with k_test rows. The columns are the effect size, yi, and the moderators, X. The "housekeeping" element of this list is a data.frame with k_train + k_test rows. The columns are n, the sample size n for each simulated study; mu_i, the mean true effect size for each simulated study; and theta_i, the true effect size for each simulated study.
Van Lissa, C. J. (2020). Small sample meta-analyses: exploring heterogeneity using metaForest. In R. Van De Schoot & M. Miočević (Eds.), Small sample size solutions (open access): A guide for applied researchers and practitioners. CRC Press (pp.186–202). doi:10.4324/9780429273872-16 Van Lissa, C. J. (2018). MetaForest: Exploring heterogeneity in meta-analysis using random forests. PsyArxiv. doi:10.31234/osf.io/myg6s
set.seed(8)
SimulateSMD()
#> $training
#> vi yi X1 X2 X3 X4
#> 1 0.10203008 -0.29398896 -1.1444094 -1.04043881 0.062535228 1.0148301
#> 2 0.08522324 -1.05004992 -1.7215353 0.02465913 0.231113964 -0.8321755
#> 3 0.11681403 -0.56288787 -0.3696855 0.86220033 1.517608501 -1.4727851
#> 4 0.14655758 1.33340253 1.8220755 0.70828681 -0.498742894 0.4537334
#> 5 0.07785210 0.18586277 0.4775898 -0.63656810 0.004252849 0.6631271
#> 6 0.11685791 1.10151909 0.1405485 0.90875200 0.359516304 -0.7153895
#> 7 0.10640890 -0.64747180 -1.7260245 -0.58273245 2.082965890 2.0207644
#> 8 0.14712820 -0.38464515 0.2765317 0.08109775 -1.408034082 1.9224919
#> 9 0.38000714 0.17596855 -1.3627597 -0.04364549 1.217850494 -0.5379136
#> 10 0.11878046 -0.01505409 0.8354238 0.27538614 0.721791139 -1.1210611
#> 11 0.15549760 1.32530940 2.2256232 1.03476676 -0.201646518 -0.7519621
#> 12 0.08957362 0.40904980 0.1722670 -0.44401335 0.095368307 -0.4055921
#> 13 0.11684822 -1.73838165 -1.5005928 -1.34238940 -2.032627280 -0.1743360
#> 14 0.18469118 -0.84998262 -1.3466174 -0.16337964 -1.775771585 0.9972979
#> 15 0.11703169 -1.29509008 -2.4010823 -0.24919870 0.066025885 0.5076123
#> 16 0.12790515 0.31764430 0.7935204 0.66247074 -1.224103992 -0.9695288
#> 17 0.06828162 0.63264296 0.4438913 0.64397076 1.578454567 0.2136013
#> 18 0.10985373 0.51095349 0.5038261 -0.85192796 -0.204568006 -0.6238114
#> 19 0.07929649 1.31225812 1.4280847 -0.72472537 0.413325429 0.3302807
#> 20 0.10629565 0.06994284 -1.8320869 -0.13801556 -0.355544375 -1.3008866
#> X5
#> 1 0.88819728
#> 2 0.29270972
#> 3 0.31683564
#> 4 -2.02425302
#> 5 -0.91530048
#> 6 -0.34304121
#> 7 -0.61830454
#> 8 1.96917331
#> 9 1.28234666
#> 10 1.21800036
#> 11 -1.27702516
#> 12 2.08310267
#> 13 -0.65815868
#> 14 -0.34382308
#> 15 0.74423183
#> 16 -1.32233177
#> 17 0.39138003
#> 18 -0.09488615
#> 19 1.13265525
#> 20 0.46624137
#>
#> $testing
#> yi X1 X2 X3 X4 X5
#> 21 0.66822969 1.34278280 -0.345707310 1.57818995 -0.44913981 0.705276551
#> 22 1.14976311 2.43894434 0.407881830 -1.09289061 0.02348950 -1.346451232
#> 23 -0.08996973 0.67693611 -0.542383594 -0.76616169 0.32860881 1.606175428
#> 24 -0.85126944 -1.96898063 -2.233980278 -2.44080992 -0.65851847 2.978035548
#> 25 -0.21095617 0.08038142 1.317620135 -0.66787498 -0.69390119 0.889280799
#> 26 -0.28505282 -0.70476923 0.421883928 -0.87779243 -1.02011480 -0.364122280
#> 27 1.42561465 0.59725232 -0.742518989 -0.24948742 -1.10058841 0.054619107
#> 28 0.22666273 0.79577963 0.246548388 -0.91454381 -1.61812166 -0.589375171
#> 29 -1.02761756 -1.03083607 -0.534765887 0.08087580 2.14679976 0.050225736
#> 30 0.59061935 -0.41952683 -1.375543934 -0.08288448 -0.71783589 2.617582843
#> 31 -0.01700093 0.16009113 -0.410783842 -0.40642879 0.89826920 0.627148220
#> 32 0.03276619 -0.56448033 1.072109345 0.69900714 -1.15007197 -0.827227458
#> 33 0.03926500 0.22750654 0.944193458 0.47244670 -1.04069937 0.504060258
#> 34 0.41097112 0.25707888 -0.660594907 0.98195504 -0.06291613 -0.394589512
#> 35 -0.49098768 -0.33770080 0.463225218 -1.44548947 1.37773115 0.357938059
#> 36 -0.41254464 0.30457246 -0.833497207 0.01650441 0.62319204 -0.115376387
#> 37 0.72602926 0.72443709 -1.656108832 -0.19116183 -0.69531851 0.680534148
#> 38 -0.12909938 -0.68517330 -0.242947018 0.60615056 0.34736006 0.377851898
#> 39 -0.15842320 0.66148590 -0.734829314 1.11059186 -1.66093486 0.386261629
#> 40 0.49299914 1.72541962 0.288050068 -0.02910979 1.22541007 0.549553058
#> 41 0.50610349 0.32134570 -2.519865202 -0.29233821 1.04911272 0.613320135
#> 42 -1.00525772 0.57631589 0.559240717 1.47869193 0.07042518 -0.795713800
#> 43 -0.67371732 -1.40999218 -0.370775118 0.73176907 0.34117199 -1.490813982
#> 44 -0.50289312 -0.99550207 -0.246350631 -1.19898977 -0.45694682 -0.769902613
#> 45 0.50431041 1.56029895 -1.068533578 1.51577158 0.36241300 1.625447491
#> 46 0.88685955 0.20359400 0.188859634 -0.36821179 0.43088979 0.903666849
#> 47 -0.70385005 -0.38763480 -0.700101046 -0.61153291 -3.28193174 -0.938239707
#> 48 1.11709753 0.91961483 -0.054871878 -0.99407239 0.07959148 -0.863328748
#> 49 -0.26009914 -0.37973893 0.004724072 0.22384503 0.28338913 -0.012054345
#> 50 0.47557698 0.74424525 0.513744294 1.24460620 -1.13800059 -1.018069491
#> 51 -0.26393460 -1.18318210 -0.232082803 -2.14666505 0.55833481 -1.304871448
#> 52 0.36147994 0.27641906 -0.503803381 0.52658047 1.45478371 -0.907842475
#> 53 0.07970255 -0.35621772 0.931147030 -0.13259284 0.91997026 0.045246555
#> 54 0.32843947 0.98772386 0.866682884 -0.36330720 -2.45861485 1.591761639
#> 55 0.37604723 -0.26723365 -1.615076668 -0.89040754 1.35749161 0.146493228
#> 56 0.24895657 0.48002729 -0.414854751 -0.54416473 -0.73312796 -0.463292606
#> 57 1.06896275 0.76742274 0.295879429 -0.46053733 0.38481593 0.236661956
#> 58 0.01179023 0.93944209 -0.501413917 -0.19810448 0.92773032 0.664210399
#> 59 0.24653265 -1.05321259 0.168396365 -0.05398731 -1.23938949 0.629995102
#> 60 0.02307407 1.01361777 -1.484505096 0.06713632 1.73655423 -0.219841525
#> 61 -0.25616540 -1.25417324 -0.935164736 1.37062115 0.46932808 0.001456405
#> 62 0.02411692 -0.38058363 -1.318323265 -1.71261554 0.27094446 -0.450508307
#> 63 -0.74419979 0.20908883 -0.843522645 -1.54077184 -0.66946378 -0.418097081
#> 64 0.26424977 -0.12475618 -0.180237064 1.42855685 0.54643929 0.338293573
#> 65 1.09889987 2.18637793 -1.473517194 -1.19629850 0.29537750 -0.393182816
#> 66 0.28142974 0.08202324 0.296855110 0.88030339 -1.13881229 -0.354461946
#> 67 1.66695308 2.11210205 0.483606325 -1.68208244 1.29022620 -1.206672296
#> 68 0.77291745 1.03434270 0.068466459 1.69888450 -2.22984375 -0.412220453
#> 69 -0.11320584 -0.19817321 0.288832146 -0.15496161 1.28430248 0.386628349
#> 70 -0.54707185 -1.03922958 1.314781376 -1.27400097 -1.41318801 -0.619818844
#> 71 -0.36965484 -0.13366410 -0.295572687 -0.58194548 -0.69842091 1.081208993
#> 72 -0.53441321 -0.58203619 -0.064578436 -0.43066068 0.82325010 -1.678908961
#> 73 0.03831820 -0.90301123 0.776282190 0.30444699 -1.14748923 -1.440690533
#> 74 -0.34719728 -0.80183175 -1.079610620 0.33526170 0.02986996 -1.422982215
#> 75 0.61514177 1.32082067 0.592149208 -2.28425822 -0.05187127 -1.138380472
#> 76 0.68789771 1.03917965 0.326762664 -0.87895358 -0.29070076 -1.560017465
#> 77 -0.09672290 0.55811104 -2.295856107 0.98105921 -0.06218890 -0.468259305
#> 78 -0.74027615 -1.32868715 -0.402928719 0.74271560 -1.07172843 0.467356785
#> 79 -1.16326147 -1.92455679 1.125903449 0.10056759 -1.16839102 -0.146917636
#> 80 0.52833995 0.02269541 1.063092737 0.16902021 -0.19654398 1.624582143
#> 81 -0.07904639 -0.58148841 -1.156393348 0.34330655 -1.23256601 0.424045409
#> 82 1.02657912 -0.22415163 -0.954671006 1.03191632 1.67582582 -0.560888865
#> 83 0.30907212 1.19843419 -0.642356883 1.61580763 0.14981857 -0.229831775
#> 84 -0.34046644 -1.35857871 -0.129691509 -2.05943539 1.10504698 0.994997471
#> 85 0.13103045 -0.02007139 0.295829171 0.71146123 -0.16625956 -0.669661078
#> 86 0.68051453 0.89013344 0.824899518 -0.19750984 -1.62326723 0.071204845
#> 87 0.12433401 -0.02785890 -1.148831250 -1.55998272 0.54408680 -1.183813504
#> 88 -0.87246555 -0.87933866 1.585573941 0.39203385 0.43202330 -0.976633307
#> 89 0.88858779 0.76532204 -0.132846093 -0.10443202 -1.73795791 1.869923666
#> 90 0.99109805 -0.97758855 -0.118899731 -0.30398595 1.32981739 -0.575898320
#> 91 0.17345653 0.53030495 0.119426695 -0.37146423 -0.82576253 -1.019094922
#> 92 0.23095085 0.93490598 0.193494092 1.28880895 -0.74782165 -0.649659900
#> 93 -0.10370354 0.34618828 1.469472441 0.16832416 -2.13353795 1.533933071
#> 94 -0.53466699 -0.73358085 -2.160397828 0.79351401 0.36263912 -0.107787116
#> 95 0.88380862 0.71044321 2.309691146 -1.43665381 -0.23749230 0.169535331
#> 96 -0.08109247 -0.32270804 0.984586572 -2.80920276 -0.91259245 1.008258732
#> 97 -0.06717729 -0.36063854 1.510773654 0.24555120 -0.54637269 1.177163008
#> 98 -0.34213584 0.15515330 -0.044507754 -0.42755197 0.17847832 0.837676659
#> 99 -0.51101709 -1.39090615 -1.153481015 0.32787243 -0.99193808 0.669284255
#> 100 0.35873286 1.37319401 2.294143138 0.24435018 -0.86142982 -0.243659774
#> 101 -0.45132111 -0.86514969 0.538671399 0.94024364 0.45566891 1.129335915
#> 102 -0.02134494 -1.30250885 -0.146357462 -0.13692522 -2.05915568 -0.041590377
#> 103 0.75858908 0.89342076 -0.979218618 -1.85424344 0.16398290 0.914060323
#> 104 -0.34981144 -0.88037666 -1.831172832 1.64373845 0.78839628 0.432991085
#> 105 -0.37632586 -0.41661945 1.870035998 -0.14302351 -1.09377744 -0.936613809
#> 106 -0.08913409 -0.04062916 -1.503020392 -0.52419166 -0.53807090 -0.582746486
#> 107 0.67550059 0.14358965 2.318318542 0.78931815 -0.22432943 -0.090653685
#> 108 -0.33840336 0.09751138 -1.256282212 0.96144097 1.46733421 1.957436095
#> 109 -0.39774616 0.16642969 -0.938503513 1.29350891 -1.60605679 0.843387407
#> 110 1.05740527 1.47631110 -0.526780039 -1.35604594 -0.54841970 0.115736923
#> 111 -1.44354981 -1.54600953 -1.733278053 -0.85199073 -1.12518922 0.051878985
#> 112 1.17984868 1.37092704 -1.078728380 -1.10291173 0.37742996 -0.559855595
#> 113 -0.57377293 -0.30687696 -0.369739572 0.84291478 -0.35847353 0.039211626
#> 114 -0.41396429 -0.17814964 1.418624142 -0.40487444 0.02549246 0.573795451
#> 115 0.75905338 0.84086555 -0.960515383 0.34463034 -0.83657908 -1.722671808
#> 116 -0.81228643 -0.70385466 0.444572597 0.11589273 0.34432991 1.525124902
#> 117 -0.01695148 -0.38034853 0.920231427 0.02267681 1.26276866 -0.035198961
#> 118 -0.13456486 -0.61284745 0.248415976 1.81038765 0.32824879 -0.187316677
#> 119 -1.17201462 -0.84761010 -0.258117777 0.18494050 -0.03846550 0.602229762
#> 120 1.87942283 1.45125747 2.300613151 -0.42790075 0.55285433 0.401261034
#>
#> $housekeeping
#> n mu_i theta_i
#> 1 38 -0.57220472 -0.110403011
#> 2 52 -0.86076766 -0.994550735
#> 3 34 -0.18484276 -0.479947194
#> 4 32 0.91103776 1.073673824
#> 5 50 0.23879490 0.077083550
#> 6 38 0.07027426 0.295637889
#> 7 38 -0.86301223 -0.698699491
#> 8 26 0.13826583 -0.034774866
#> 9 8 -0.68137984 -0.665516769
#> 10 32 0.41771190 0.518916955
#> 11 30 1.11281162 0.538211463
#> 12 44 0.08613348 0.111778407
#> 13 46 -0.75029639 -0.796466534
#> 14 22 -0.67330868 -0.688752397
#> 15 40 -1.20054114 -1.301720562
#> 16 30 0.39676020 0.732134824
#> 17 60 0.22194567 0.543078167
#> 18 36 0.25191305 0.434458715
#> 19 60 0.71404233 0.880860097
#> 20 36 -0.91604347 -0.759558068
#> 21 58 0.67139140 0.415969976
#> 22 40 1.21947217 1.441140252
#> 23 34 0.33846806 0.205837248
#> 24 40 -0.98449031 -0.882694054
#> 25 64 0.04019071 0.178884132
#> 26 26 -0.35238462 -0.366333503
#> 27 26 0.29862616 0.551341277
#> 28 66 0.39788982 0.350349316
#> 29 48 -0.51541804 -0.692309354
#> 30 14 -0.20976341 0.025731841
#> 31 60 0.08004557 0.260410672
#> 32 52 -0.28224017 -0.189857428
#> 33 20 0.11375327 0.081113592
#> 34 30 0.12853944 0.354045634
#> 35 56 -0.16885040 -0.342201593
#> 36 46 0.15228623 -0.027168989
#> 37 52 0.36221854 0.309529621
#> 38 30 -0.34258665 -0.162344715
#> 39 40 0.33074295 -0.115007554
#> 40 46 0.86270981 0.507714856
#> 41 44 0.16067285 0.094743023
#> 42 32 0.28815795 -0.048047856
#> 43 44 -0.70499609 -0.627156769
#> 44 36 -0.49775103 -0.727641593
#> 45 48 0.78014947 0.585727525
#> 46 14 0.10179700 0.166972621
#> 47 32 -0.19381740 -0.390963946
#> 48 44 0.45980742 0.880290311
#> 49 44 -0.18986946 -0.367066998
#> 50 32 0.37212263 0.502838305
#> 51 54 -0.59159105 -0.726077338
#> 52 36 0.13820953 0.412740339
#> 53 48 -0.17810886 -0.364378784
#> 54 38 0.49386193 0.312374877
#> 55 52 -0.13361682 -0.013720847
#> 56 48 0.24001364 0.594751986
#> 57 30 0.38371137 0.529897650
#> 58 32 0.46972104 0.375083374
#> 59 20 -0.52660630 -0.306364087
#> 60 50 0.50680889 0.060905115
#> 61 48 -0.62708662 -0.480724423
#> 62 42 -0.19029182 -0.087017759
#> 63 16 0.10454441 0.160216409
#> 64 26 -0.06237809 0.005273069
#> 65 36 1.09318897 1.031005329
#> 66 26 0.04101162 -0.301954708
#> 67 34 1.05605102 1.592781611
#> 68 62 0.51717135 0.408599441
#> 69 46 -0.09908660 -0.277930383
#> 70 18 -0.51961479 -0.411171528
#> 71 20 -0.06683205 0.058090708
#> 72 54 -0.29101810 -0.265825394
#> 73 50 -0.45150561 -0.301892062
#> 74 34 -0.40091587 -0.468773592
#> 75 42 0.66041034 0.632834013
#> 76 48 0.51958982 0.606783942
#> 77 8 0.27905552 -0.070952444
#> 78 48 -0.66434357 -0.548251031
#> 79 72 -0.96227839 -1.341979596
#> 80 32 0.01134770 0.285301371
#> 81 44 -0.29074421 -0.192195015
#> 82 30 -0.11207581 0.009216599
#> 83 26 0.59921709 0.213300606
#> 84 68 -0.67928936 -0.546241835
#> 85 18 -0.01003569 0.195122614
#> 86 56 0.44506672 0.629947466
#> 87 38 -0.01392945 0.190647482
#> 88 28 -0.43966933 -0.355124416
#> 89 48 0.38266102 0.541580823
#> 90 8 -0.48879427 -0.539783475
#> 91 50 0.26515247 0.332732908
#> 92 60 0.46745299 0.507476081
#> 93 34 0.17309414 0.135749209
#> 94 32 -0.36679042 -1.005220564
#> 95 22 0.35522160 0.287387874
#> 96 50 -0.16135402 -0.018783716
#> 97 44 -0.18031927 -0.178758180
#> 98 40 0.07757665 -0.196031042
#> 99 32 -0.69545307 -0.664308741
#> 100 40 0.68659701 0.552990749
#> 101 44 -0.43257484 -0.524339492
#> 102 14 -0.65125442 -0.711346529
#> 103 18 0.44671038 0.262839443
#> 104 16 -0.44018833 -0.342319368
#> 105 40 -0.20830972 -0.147333039
#> 106 34 -0.02031458 -0.146168767
#> 107 48 0.07179483 0.228097356
#> 108 36 0.04875569 -0.353462512
#> 109 42 0.08321484 0.217963002
#> 110 48 0.73815555 0.551954362
#> 111 38 -0.77300477 -0.780651379
#> 112 28 0.68546352 0.990646513
#> 113 48 -0.15343848 -0.022634990
#> 114 26 -0.08907482 -0.207256657
#> 115 60 0.42043278 0.824740476
#> 116 58 -0.35192733 -0.547086944
#> 117 44 -0.19017427 -0.311305763
#> 118 60 -0.30642373 -0.014485949
#> 119 24 -0.42380505 -0.371865329
#> 120 36 0.72562874 0.815556112
#>
#> $tau2_est
#> [1] 0.6674238
#>
SimulateSMD(k_train = 50, distribution = "bernoulli")
#> $training
#> vi yi X1 X2 X3 X4 X5
#> 1 0.18601400 0.319895727 0 0 1 0 0
#> 2 0.12908127 -0.812082670 0 1 1 0 1
#> 3 0.09927040 -0.798594168 0 1 1 0 0
#> 4 0.11990888 0.726149321 1 1 0 0 1
#> 5 0.18917092 0.478131978 0 1 1 1 1
#> 6 0.17066258 0.324363499 0 1 1 0 0
#> 7 0.08094759 0.176391629 1 1 0 0 0
#> 8 0.07566772 0.634285362 1 0 1 1 1
#> 9 0.07462181 0.007458783 0 0 0 0 1
#> 10 0.11292781 0.229303650 0 0 0 1 0
#> 11 0.17017988 1.160447761 1 0 0 0 0
#> 12 0.08472345 -0.257861306 1 0 0 1 1
#> 13 0.15017861 0.553690447 1 0 1 0 1
#> 14 0.09806810 -0.400214164 1 0 0 0 0
#> 15 0.09356224 0.397827304 0 1 0 0 0
#> 16 0.05172868 -0.156982870 0 0 1 0 1
#> 17 0.22846438 0.399732086 1 1 1 1 1
#> 18 0.08054809 -0.551492525 0 0 0 0 1
#> 19 0.13465681 -0.003788886 0 0 0 0 1
#> 20 0.09712561 0.291156233 0 0 0 0 0
#> 21 0.12137039 0.407408446 0 1 0 0 0
#> 22 0.11344873 0.296651657 0 0 1 0 0
#> 23 0.09458856 -0.494447331 0 1 1 0 0
#> 24 0.08105376 -0.203238217 0 1 0 1 1
#> 25 0.07528058 -0.261855272 0 0 0 1 0
#> 26 0.07810811 0.602189056 0 0 1 0 0
#> 27 0.06303621 0.126754249 1 0 0 0 1
#> 28 0.08284619 0.924873736 1 0 1 0 1
#> 29 0.15626989 -0.207765217 0 0 1 0 1
#> 30 0.14454850 0.117504965 1 1 1 1 0
#> 31 0.06974934 -0.183370252 0 0 0 0 0
#> 32 0.08062792 0.020620029 0 0 0 0 1
#> 33 0.09053356 -0.501793791 0 0 1 1 0
#> 34 0.08096069 0.179921167 0 1 1 1 1
#> 35 0.09033763 0.965690186 1 1 1 0 0
#> 36 0.08576725 0.403139418 1 1 0 1 1
#> 37 0.10022729 0.576980052 1 0 1 1 1
#> 38 0.08529411 -0.344953822 0 0 0 1 0
#> 39 0.11947109 0.210777090 1 1 1 1 0
#> 40 0.10032725 0.583868790 1 0 1 1 0
#> 41 0.16025542 -0.484222913 0 0 0 1 1
#> 42 0.08852121 0.644892130 1 0 1 1 0
#> 43 0.08509067 -0.316664515 0 1 1 0 0
#> 44 0.12186025 0.812395246 1 0 0 0 0
#> 45 0.07298096 0.334883504 1 1 0 1 0
#> 46 0.10632390 0.083222187 0 0 0 0 1
#> 47 0.07853519 0.320709464 1 1 1 0 1
#> 48 0.07221412 0.171255885 0 0 1 1 0
#> 49 0.19206449 1.987575997 1 0 1 1 0
#> 50 0.09269076 0.291655586 1 1 1 0 0
#>
#> $testing
#> yi X1 X2 X3 X4 X5
#> 51 1.012904966 1 1 0 0 0
#> 52 0.374774057 1 1 0 1 1
#> 53 -0.455105328 0 1 1 0 0
#> 54 -0.669119888 0 1 1 0 1
#> 55 -0.145538268 0 1 1 1 0
#> 56 0.552748170 0 1 0 0 1
#> 57 -0.458606161 0 1 0 0 1
#> 58 -0.009677788 1 0 1 0 1
#> 59 0.625725427 0 0 0 1 0
#> 60 0.361134730 1 0 1 0 1
#> 61 0.542193010 1 1 0 0 1
#> 62 -0.297937323 0 0 0 0 0
#> 63 0.259755323 1 1 0 1 1
#> 64 0.706061426 1 1 0 1 0
#> 65 0.803470423 1 0 1 1 1
#> 66 0.435566593 0 1 1 0 1
#> 67 0.174003618 1 0 0 1 1
#> 68 -0.261874325 0 0 0 1 1
#> 69 -0.657704227 0 0 0 0 1
#> 70 1.348900101 1 1 0 1 0
#> 71 0.491722164 1 1 1 0 0
#> 72 0.459387290 0 1 1 0 0
#> 73 -0.115919994 0 1 1 0 0
#> 74 -0.692632618 1 1 1 1 0
#> 75 0.626283494 1 0 1 1 1
#> 76 -0.145243748 0 1 1 0 1
#> 77 -0.406112777 1 0 0 0 0
#> 78 0.682455822 0 1 0 0 0
#> 79 -0.406093497 0 1 0 1 0
#> 80 -0.925157959 0 0 0 0 0
#> 81 0.793949861 1 1 0 1 0
#> 82 -0.154417503 0 0 1 0 0
#> 83 0.883598016 1 1 0 0 0
#> 84 0.511118156 1 0 0 1 0
#> 85 0.199453022 1 1 1 1 0
#> 86 -0.186660791 0 1 1 0 1
#> 87 -0.050823065 0 1 1 1 1
#> 88 0.093765476 0 0 1 0 0
#> 89 0.185497677 1 1 1 1 1
#> 90 0.703378447 0 1 1 1 1
#> 91 0.613453650 1 0 1 0 1
#> 92 1.436737422 1 1 1 1 0
#> 93 0.338012947 0 1 0 1 1
#> 94 0.027831583 1 0 0 1 0
#> 95 -0.502460116 0 1 1 0 0
#> 96 0.572883364 1 0 1 1 0
#> 97 0.115567203 0 0 0 1 1
#> 98 -0.075235249 0 1 1 0 0
#> 99 0.759880868 1 0 0 1 1
#> 100 0.272740571 1 0 0 1 0
#> 101 0.841180697 1 1 0 1 1
#> 102 -0.166823745 0 1 1 0 0
#> 103 0.166991396 1 1 1 0 0
#> 104 0.305854368 1 0 0 0 1
#> 105 -0.571938299 0 1 1 0 0
#> 106 0.465967753 1 1 1 0 0
#> 107 0.378830454 1 1 1 1 1
#> 108 0.532428080 1 0 0 1 0
#> 109 0.459242178 1 1 1 1 0
#> 110 -0.363940773 0 1 1 0 1
#> 111 0.122211241 1 1 1 1 0
#> 112 -0.139589124 0 1 1 0 0
#> 113 -0.290900568 0 1 0 1 1
#> 114 -0.140285503 0 0 1 1 0
#> 115 -0.374212002 0 0 1 1 0
#> 116 1.003239234 1 0 0 0 1
#> 117 -0.828637399 0 0 0 0 1
#> 118 0.079694661 1 1 1 1 0
#> 119 0.029921814 0 1 0 1 0
#> 120 0.278153302 1 0 1 1 0
#> 121 0.803054657 1 1 1 1 1
#> 122 0.359287189 0 0 1 0 0
#> 123 -0.482613334 0 1 0 1 0
#> 124 -0.084650908 1 0 0 0 1
#> 125 -0.223874503 0 0 1 1 1
#> 126 0.324810103 1 0 1 1 0
#> 127 0.993530246 1 1 0 1 1
#> 128 -0.003850118 1 0 0 0 0
#> 129 0.152511001 0 1 0 1 0
#> 130 -0.002716635 0 1 1 1 1
#> 131 0.455796944 0 0 1 0 0
#> 132 0.875830915 0 0 1 0 1
#> 133 0.246528549 1 0 0 0 0
#> 134 0.349252780 1 1 1 0 1
#> 135 0.445560610 0 1 1 0 0
#> 136 0.152314692 1 0 1 1 0
#> 137 -0.268616187 1 0 1 0 0
#> 138 -0.787108297 0 0 1 1 0
#> 139 0.051764455 1 1 1 1 1
#> 140 0.044816251 1 1 1 0 1
#> 141 -0.320510163 0 1 1 1 1
#> 142 0.490135511 1 0 1 0 0
#> 143 0.439386341 1 1 0 0 0
#> 144 0.874346062 1 1 0 1 0
#> 145 -0.414164722 1 0 1 1 0
#> 146 1.138882803 1 0 1 1 1
#> 147 0.721229647 1 0 0 1 1
#> 148 0.458024016 1 1 1 0 0
#> 149 0.331885547 0 1 1 1 1
#> 150 0.039939278 0 1 1 1 0
#>
#> $housekeeping
#> n mu_i theta_i
#> 1 20 0.0 0.236457864
#> 2 32 0.0 -0.141724215
#> 3 42 0.0 0.096427145
#> 4 34 0.5 0.694802939
#> 5 20 0.0 -0.239674004
#> 6 22 0.0 0.182219776
#> 7 48 0.5 0.327600385
#> 8 54 0.5 0.424319040
#> 9 52 0.0 0.075423476
#> 10 34 0.0 0.450004325
#> 11 26 0.5 0.683344207
#> 12 46 0.5 0.259912083
#> 13 26 0.5 0.499302746
#> 14 40 0.5 0.285212568
#> 15 42 0.0 0.250052333
#> 16 76 0.0 -0.055583462
#> 17 16 0.5 0.351732483
#> 18 50 0.0 -0.023919158
#> 19 28 0.0 0.249561999
#> 20 40 0.0 0.081809895
#> 21 32 0.0 0.107553678
#> 22 34 0.0 0.445909030
#> 23 42 0.0 -0.290566931
#> 24 48 0.0 0.064367379
#> 25 52 0.0 -0.163375817
#> 26 52 0.0 0.063575485
#> 27 62 0.5 0.473614683
#> 28 52 0.5 0.575362963
#> 29 24 0.0 0.124391768
#> 30 26 0.5 0.240334098
#> 31 56 0.0 -0.209489275
#> 32 48 0.0 0.317906906
#> 33 44 0.0 -0.388176769
#> 34 48 0.0 -0.064779051
#> 35 48 0.5 0.620411220
#> 36 46 0.5 0.414041950
#> 37 40 0.5 0.623995860
#> 38 46 0.0 0.056201448
#> 39 32 0.5 0.319016567
#> 40 40 0.5 0.380514481
#> 41 24 0.0 -0.278942986
#> 42 46 0.5 0.360546875
#> 43 46 0.0 -0.154107231
#> 44 34 0.5 0.758003747
#> 45 54 0.5 0.420998449
#> 46 36 0.0 -0.042406223
#> 47 50 0.5 0.356445304
#> 48 54 0.0 -0.143298699
#> 49 30 0.5 0.621913771
#> 50 42 0.5 0.470757003
#> 51 46 0.5 0.667803624
#> 52 32 0.5 0.275786748
#> 53 66 0.0 -0.032610490
#> 54 28 0.0 -0.261275484
#> 55 20 0.0 -0.001423038
#> 56 42 0.0 0.233271325
#> 57 18 0.0 -0.162185915
#> 58 36 0.5 0.313974053
#> 59 28 0.0 -0.028776824
#> 60 24 0.5 0.881426443
#> 61 54 0.5 0.548352510
#> 62 30 0.0 0.206138982
#> 63 44 0.5 0.256361706
#> 64 44 0.5 0.384669678
#> 65 46 0.5 0.710480564
#> 66 28 0.0 -0.145602348
#> 67 34 0.5 0.394022960
#> 68 40 0.0 -0.139210812
#> 69 50 0.0 0.303460137
#> 70 42 0.5 0.835366692
#> 71 38 0.5 0.803723290
#> 72 34 0.0 -0.007447504
#> 73 44 0.0 -0.198260982
#> 74 8 0.5 0.274965809
#> 75 38 0.5 0.646899407
#> 76 30 0.0 0.049055930
#> 77 46 0.5 0.400398777
#> 78 42 0.0 -0.086547465
#> 79 16 0.0 -0.171420170
#> 80 32 0.0 0.103726034
#> 81 64 0.5 0.965507467
#> 82 36 0.0 -0.024407347
#> 83 40 0.5 0.681232917
#> 84 28 0.5 0.456687037
#> 85 50 0.5 0.113845557
#> 86 44 0.0 0.170289562
#> 87 32 0.0 -0.018967038
#> 88 60 0.0 -0.060745530
#> 89 38 0.5 0.781560507
#> 90 8 0.0 -0.096564537
#> 91 44 0.5 0.652421963
#> 92 26 0.5 0.723186228
#> 93 24 0.0 0.011164221
#> 94 50 0.5 0.044155169
#> 95 44 0.0 -0.001217579
#> 96 30 0.5 0.690616637
#> 97 64 0.0 -0.194712087
#> 98 68 0.0 -0.109612307
#> 99 42 0.5 0.594715999
#> 100 50 0.5 0.472352492
#> 101 32 0.5 0.642653882
#> 102 42 0.0 -0.122421868
#> 103 52 0.5 0.222533065
#> 104 30 0.5 0.406813361
#> 105 42 0.0 -0.001870094
#> 106 58 0.5 0.173989969
#> 107 36 0.5 0.632137309
#> 108 38 0.5 0.849246683
#> 109 64 0.5 0.453708673
#> 110 62 0.0 0.101279461
#> 111 26 0.5 0.403328813
#> 112 48 0.0 -0.149606156
#> 113 22 0.0 -0.382438709
#> 114 56 0.0 -0.227191976
#> 115 44 0.0 -0.135577057
#> 116 30 0.5 0.751593116
#> 117 36 0.0 -0.507693606
#> 118 42 0.5 0.166680013
#> 119 8 0.0 0.040361747
#> 120 48 0.5 0.259946504
#> 121 50 0.5 0.804348627
#> 122 46 0.0 0.203900854
#> 123 52 0.0 0.135880521
#> 124 66 0.5 0.333272677
#> 125 50 0.0 -0.028153004
#> 126 42 0.5 0.398409937
#> 127 42 0.5 0.493365874
#> 128 36 0.5 0.500086117
#> 129 24 0.0 0.149474223
#> 130 30 0.0 0.075034492
#> 131 42 0.0 0.064684728
#> 132 16 0.0 0.023972886
#> 133 60 0.5 0.303310332
#> 134 44 0.5 0.254670557
#> 135 44 0.0 0.395850703
#> 136 40 0.5 0.192303599
#> 137 18 0.5 0.278825155
#> 138 34 0.0 -0.194509557
#> 139 56 0.5 0.623430966
#> 140 32 0.5 0.475578367
#> 141 54 0.0 0.002538519
#> 142 52 0.5 0.549976149
#> 143 50 0.5 0.552210075
#> 144 56 0.5 0.431615110
#> 145 42 0.5 0.508575722
#> 146 18 0.5 0.247560578
#> 147 44 0.5 0.529930265
#> 148 18 0.5 0.479428248
#> 149 40 0.0 0.054100153
#> 150 50 0.0 0.002462566
#>
#> $tau2_est
#> [1] 0.1328458
#>
SimulateSMD(distribution = "bernoulli", model = es * x[ ,1] * x[ ,2])
#> $training
#> vi yi X1 X2 X3 X4 X5
#> 1 0.08899914 -0.341713574 0 1 0 0 1
#> 2 0.08483423 -0.276918002 1 0 1 0 0
#> 3 0.09905862 0.787377024 0 0 0 1 1
#> 4 0.18638173 0.342114936 0 0 1 0 0
#> 5 0.12243253 0.483692981 1 0 0 1 0
#> 6 0.14826028 0.454774626 0 0 1 1 1
#> 7 0.10629205 0.068062122 0 0 1 0 0
#> 8 0.09861175 0.451290616 1 1 0 0 0
#> 9 0.13792967 0.428128458 0 0 1 1 0
#> 10 0.13697367 0.360219904 1 0 0 0 1
#> 11 0.14428300 0.001076264 1 0 1 0 0
#> 12 0.05932725 -0.135368361 1 0 1 1 1
#> 13 0.07737572 0.535221797 1 1 1 1 0
#> 14 0.12288741 0.854302613 1 0 1 1 0
#> 15 0.09209183 -0.186419459 0 0 1 1 0
#> 16 0.08702929 -0.527853248 0 1 1 1 1
#> 17 0.20435476 0.914310676 1 0 1 1 0
#> 18 0.11230694 0.101786688 1 1 1 0 1
#> 19 0.06989136 0.222555205 1 1 0 0 0
#> 20 0.41513964 0.770120048 1 1 1 0 0
#>
#> $testing
#> yi X1 X2 X3 X4 X5
#> 21 1.395378864 1 1 1 1 0
#> 22 -0.067869107 1 1 0 1 1
#> 23 -0.302146932 1 0 1 1 0
#> 24 0.410507262 1 0 0 0 0
#> 25 0.231038720 1 1 1 1 1
#> 26 0.025904361 1 1 1 0 0
#> 27 0.166662748 1 1 1 1 0
#> 28 0.250770488 0 1 1 0 0
#> 29 0.258631507 1 0 1 0 1
#> 30 0.140105631 0 1 0 0 0
#> 31 -0.057501765 0 1 1 1 1
#> 32 0.173243132 0 0 1 1 1
#> 33 -0.290209083 0 0 1 0 1
#> 34 -0.431566128 0 0 1 1 0
#> 35 0.021351607 1 0 0 1 0
#> 36 0.206182083 1 0 0 1 0
#> 37 -0.232677396 1 0 1 0 0
#> 38 0.048029773 0 0 1 1 1
#> 39 0.484059472 0 1 1 1 1
#> 40 0.125639708 1 1 0 1 0
#> 41 0.675121020 0 1 1 1 1
#> 42 0.287237362 0 1 1 1 0
#> 43 -0.338369412 1 0 1 0 0
#> 44 0.074822076 0 0 0 0 1
#> 45 -0.085615981 0 0 1 1 1
#> 46 0.528662230 1 1 1 1 0
#> 47 -0.104644232 0 0 0 1 0
#> 48 0.309969722 1 1 1 0 0
#> 49 -0.202975543 0 1 1 1 0
#> 50 0.221639650 0 1 0 0 0
#> 51 -0.135852171 0 1 1 0 0
#> 52 0.028421144 0 0 0 0 0
#> 53 0.397051905 0 0 1 1 1
#> 54 0.202422884 1 1 0 1 1
#> 55 0.124097113 0 1 1 0 1
#> 56 0.290230232 1 0 0 1 1
#> 57 0.405228250 1 0 1 0 1
#> 58 0.300594407 1 1 0 1 1
#> 59 0.517528986 0 1 1 0 1
#> 60 1.465508331 1 1 1 0 1
#> 61 -0.482790464 1 0 1 0 1
#> 62 -0.377937818 1 0 1 1 0
#> 63 -0.006511864 1 1 0 1 0
#> 64 0.288785830 0 1 1 1 1
#> 65 -0.229726886 0 0 1 0 1
#> 66 0.253960762 0 0 1 1 0
#> 67 0.045086972 1 1 0 0 0
#> 68 1.629414638 1 1 1 0 0
#> 69 0.310626535 0 0 0 0 1
#> 70 -0.524530601 0 1 0 0 1
#> 71 -0.837544243 0 1 0 1 0
#> 72 0.001063047 1 1 1 0 0
#> 73 -0.512749759 1 0 1 1 0
#> 74 0.492691943 1 0 1 0 0
#> 75 0.338803904 0 1 1 1 1
#> 76 -0.341957673 0 0 1 0 1
#> 77 -0.366096043 0 0 0 1 1
#> 78 0.115912622 0 0 0 1 1
#> 79 0.275936640 1 1 1 0 0
#> 80 -0.013144811 1 0 0 1 0
#> 81 0.379774474 0 0 1 1 1
#> 82 0.287611210 0 1 1 1 1
#> 83 -0.248120632 1 0 0 1 1
#> 84 0.324261855 1 1 0 0 1
#> 85 0.321074450 0 0 1 1 0
#> 86 -0.110752661 0 0 1 0 1
#> 87 0.603823919 1 1 1 0 0
#> 88 0.803019196 1 0 0 0 0
#> 89 0.316675897 0 0 1 1 1
#> 90 -0.470775777 0 1 1 0 0
#> 91 0.090001988 1 1 0 0 0
#> 92 0.124601360 0 0 0 0 0
#> 93 0.677368091 1 1 0 0 0
#> 94 0.325373415 0 0 1 1 0
#> 95 -0.342912459 1 0 1 0 1
#> 96 -0.373029080 0 1 0 1 0
#> 97 0.602362964 1 0 1 0 1
#> 98 -0.083063136 0 0 1 0 1
#> 99 -0.180973084 0 1 0 0 1
#> 100 1.052343474 1 1 1 1 1
#> 101 0.424489594 1 1 0 0 0
#> 102 0.419941381 0 0 1 0 0
#> 103 0.097763076 0 0 1 0 1
#> 104 -0.273573040 0 0 0 0 1
#> 105 0.222844134 0 1 1 0 0
#> 106 0.219034185 0 0 0 1 1
#> 107 1.132880147 1 1 0 0 0
#> 108 0.063847614 0 1 1 1 0
#> 109 0.376217438 1 1 1 1 0
#> 110 -0.118153394 1 0 0 0 0
#> 111 0.380284516 1 1 1 1 0
#> 112 -0.247431475 1 0 1 1 1
#> 113 -0.015349927 1 0 1 1 1
#> 114 0.200829361 0 0 0 0 0
#> 115 -0.736967788 1 0 0 1 0
#> 116 0.560959434 0 1 0 0 1
#> 117 -0.044711686 0 0 0 1 0
#> 118 0.313728749 1 0 0 1 0
#> 119 0.614234855 1 0 1 0 1
#> 120 0.286811516 0 0 0 1 0
#>
#> $housekeeping
#> n mu_i theta_i
#> 1 44 0.0 0.0218125526
#> 2 46 0.0 -0.1400336659
#> 3 42 0.0 0.2903092867
#> 4 20 0.0 -0.1885532832
#> 5 32 0.0 -0.2237167036
#> 6 26 0.0 -0.0318303262
#> 7 36 0.0 -0.1436029033
#> 8 40 0.5 0.5855103000
#> 9 28 0.0 0.2907177529
#> 10 28 0.0 0.0125872729
#> 11 26 0.0 -0.1842569671
#> 12 66 0.0 0.1125469471
#> 13 52 0.5 0.4169749577
#> 14 34 0.0 0.2312223158
#> 15 42 0.0 -0.2127099472
#> 16 46 0.0 -0.3979757558
#> 17 20 0.0 -0.0105729020
#> 18 34 0.5 0.4848077501
#> 19 56 0.5 0.1892874080
#> 20 8 0.5 0.5664453453
#> 21 38 0.5 0.6394519471
#> 22 30 0.5 0.3213496882
#> 23 26 0.0 0.0267255964
#> 24 24 0.0 0.2174360110
#> 25 42 0.5 0.3792154885
#> 26 48 0.5 0.4254662442
#> 27 60 0.5 0.7224911756
#> 28 44 0.0 0.0557765292
#> 29 28 0.0 0.0774486740
#> 30 52 0.0 -0.1829443964
#> 31 46 0.0 0.0464188440
#> 32 56 0.0 0.0495502162
#> 33 14 0.0 -0.0539594794
#> 34 46 0.0 -0.4151037803
#> 35 48 0.0 0.0558204054
#> 36 32 0.0 -0.2076633112
#> 37 54 0.0 -0.0534839040
#> 38 24 0.0 -0.0838029318
#> 39 34 0.0 0.2865577193
#> 40 20 0.5 0.2808178562
#> 41 54 0.0 0.3559495877
#> 42 32 0.0 0.2825776675
#> 43 42 0.0 -0.2057400734
#> 44 54 0.0 -0.0148102239
#> 45 24 0.0 -0.3895915563
#> 46 36 0.5 0.4223380417
#> 47 56 0.0 0.0664195707
#> 48 18 0.5 0.2533554459
#> 49 74 0.0 -0.1311496470
#> 50 82 0.0 0.1352062513
#> 51 54 0.0 0.1108675363
#> 52 46 0.0 0.0361174307
#> 53 32 0.0 0.3182052380
#> 54 16 0.5 0.3002933080
#> 55 44 0.0 -0.2509113668
#> 56 30 0.0 0.5795005516
#> 57 14 0.0 0.2096930659
#> 58 50 0.5 0.1968983139
#> 59 48 0.0 0.3070117044
#> 60 36 0.5 0.6359222265
#> 61 44 0.0 -0.3557027533
#> 62 38 0.0 -0.1609213218
#> 63 50 0.5 0.1303641437
#> 64 32 0.0 0.3234341495
#> 65 40 0.0 -0.0117268317
#> 66 24 0.0 0.0130395502
#> 67 48 0.5 0.4490052228
#> 68 42 0.5 0.9222438706
#> 69 48 0.0 -0.0623635512
#> 70 24 0.0 -0.1890979353
#> 71 26 0.0 -0.0499581918
#> 72 32 0.5 0.0291782348
#> 73 26 0.0 -0.1476377860
#> 74 26 0.0 0.3384559092
#> 75 56 0.0 -0.0496715355
#> 76 16 0.0 0.0446072712
#> 77 26 0.0 -0.4102490769
#> 78 30 0.0 0.1648840286
#> 79 46 0.5 0.5495568846
#> 80 30 0.0 -0.2380674633
#> 81 26 0.0 0.0618427053
#> 82 56 0.0 0.4102666857
#> 83 36 0.0 -0.1108318810
#> 84 38 0.5 0.2173489832
#> 85 48 0.0 -0.1174462182
#> 86 54 0.0 0.1393623175
#> 87 26 0.5 0.3670222764
#> 88 36 0.0 -0.0819854190
#> 89 40 0.0 -0.0240307758
#> 90 46 0.0 0.1293133482
#> 91 46 0.5 0.3058202055
#> 92 46 0.0 0.3124133559
#> 93 40 0.5 0.4429591925
#> 94 42 0.0 0.0758241443
#> 95 58 0.0 -0.1479747575
#> 96 46 0.0 0.0281714361
#> 97 30 0.0 0.2961704360
#> 98 56 0.0 -0.3905419081
#> 99 44 0.0 0.0832442396
#> 100 44 0.5 0.8315698641
#> 101 38 0.5 0.6580547967
#> 102 54 0.0 -0.1371291310
#> 103 56 0.0 -0.2003559292
#> 104 40 0.0 0.0114756443
#> 105 20 0.0 0.1161416058
#> 106 42 0.0 0.3028432146
#> 107 36 0.5 0.6304958577
#> 108 34 0.0 -0.0006682723
#> 109 34 0.5 0.7146986789
#> 110 24 0.0 -0.1710327677
#> 111 22 0.5 0.4508727549
#> 112 48 0.0 -0.2833941434
#> 113 38 0.0 -0.0457327018
#> 114 48 0.0 0.3815720293
#> 115 26 0.0 -0.1168532321
#> 116 46 0.0 0.4633429161
#> 117 52 0.0 0.2012577320
#> 118 52 0.0 0.1859872420
#> 119 56 0.0 0.3185620694
#> 120 56 0.0 0.4965323615
#>
#> $tau2_est
#> [1] 0.06402135
#>