This 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 (Hedges, 1981; Li, Dusseldorp, & Meulman, 2017). The functional form of the model can be specified, and moderators can be either normally distributed or Bernoulli-distributed. See Van Lissa, in preparation, 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]
)

Arguments

k_train

Atomic integer. The number of studies in the training dataset. Defaults to 20.

k_test

Atomic integer. The number of studies in the testing dataset. Defaults to 100.

mean_n

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.

es

Atomic numeric vector. The effect size, also known as beta, used in the model statement. Defaults to .5.

tau2

Atomic numeric vector. The residual heterogeneity. For a range of realistic values encountered in psychological research, see Van Erp, Verhagen, Grasman, & Wagenmakers, 2017. Defaults to 0.04.

moderators

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.

distribution

Atomic character. The distribution of the moderators. Can be set to either "normal" or "bernoulli". Defaults to "normal".

model

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].

Value

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.

Examples

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 #>