sim_arguments <- list(
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, 0.15),
outcome_type = 'binary'
)
)
prop_data <- simulate_propensity(sim_arguments)
# prop_data |> ggformula::gf_density(~ ses, fill = ~factor(trt))
# prop_data |> ggformula::gf_density(~ age, fill = ~factor(trt))
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
)
)
simulate_fixed(data = NULL, sim_args = sim_arguments) |>
simulate_error(sim_args = sim_arguments) |>
generate_response(sim_arguments)## X.Intercept. motivation trt age ses level1_id error
## 1 1 -20.226141182 1 3 2.954775206 1 0.750026529
## 2 1 -7.490679620 1 -3 -3.528203718 2 0.118249670
## 3 1 -16.125839573 1 6 4.933441999 3 -1.188047073
## 4 1 3.781436764 1 5 0.161795225 4 7.815044107
## 5 1 26.181660690 1 -7 -2.480217866 5 -0.088093375
## 6 1 -2.642434864 0 -7 8.366245297 6 1.712901619
## 7 1 -5.828875327 1 4 -5.256333071 7 -4.205412827
## 8 1 36.588154665 1 -1 0.140079107 8 2.338189630
## 9 1 10.338440849 0 2 2.634450455 9 -2.132025342
## 10 1 40.155655867 1 -4 6.353934600 10 2.403102384
## 11 1 46.297221425 1 4 -2.937962229 11 1.799508193
## 12 1 12.030590784 1 -2 -2.152357221 12 -0.490826976
## 13 1 -16.316030889 1 4 -0.114831313 13 -0.632547275
## 14 1 13.822094047 1 0 0.374369613 14 5.545567904
## 15 1 20.895671968 1 2 -10.449978079 15 1.542430354
## 16 1 -8.192980448 1 1 5.119485865 16 -1.737663422
## 17 1 22.034451410 1 6 3.116200658 17 5.927179127
## 18 1 33.593509262 1 -4 -6.836650042 18 -1.438759904
## 19 1 -17.018234492 0 -4 0.222947613 19 1.257481317
## 20 1 6.042164381 0 -6 1.831368995 20 -1.489699036
## 21 1 18.938440349 1 -6 -0.892348487 21 -2.324748255
## 22 1 36.585653028 1 7 10.508387967 22 -4.132211365
## 23 1 6.013838484 0 -7 -1.212048289 23 -1.953736020
## 24 1 -17.581887031 1 2 0.058586076 24 -2.482515542
## 25 1 10.080013505 1 -5 -13.892168443 25 -0.003340862
## 26 1 -3.902597980 0 -5 1.372456974 26 -4.478585028
## 27 1 16.069000547 1 -5 -3.113659639 27 0.339640501
## 28 1 -16.393086359 1 -4 -5.139158587 28 -3.051612910
## 29 1 -48.608374804 0 2 3.644950546 29 -4.584101012
## 30 1 19.826140837 1 7 -1.962307183 30 0.979951740
## 31 1 -11.277585257 0 -5 1.789227877 31 -0.222967432
## 32 1 28.794787699 1 7 -3.775949300 32 0.802932812
## 33 1 -3.275533676 1 -5 0.333448564 33 3.713799476
## 34 1 -35.596624537 1 1 1.286778252 34 -2.151358230
## 35 1 26.363637498 1 1 -0.454815282 35 0.816803500
## 36 1 9.840401825 1 0 -3.652540133 36 1.315272253
## 37 1 -7.517528891 1 -5 -1.979206232 37 -6.402510286
## 38 1 27.086908281 1 2 -7.079042577 38 -3.219038550
## 39 1 34.397574968 1 -5 -0.544694590 39 -0.496349275
## 40 1 14.589265797 1 -4 0.371499550 40 -3.652505589
## 41 1 17.411507604 1 3 2.250950726 41 1.683850319
## 42 1 21.419894504 1 3 1.936729091 42 -0.700419305
## 43 1 -17.191349622 1 2 -1.291475068 43 -1.684717135
## 44 1 55.619417201 1 -5 7.617453013 44 -0.799285155
## 45 1 -15.834627711 1 1 2.756717191 45 1.675224396
## 46 1 -48.216645129 0 -7 1.649508867 46 -1.135302578
## 47 1 -5.658817814 1 -5 0.864613477 47 5.809993624
## 48 1 22.620101677 0 -5 2.133207681 48 -1.634578147
## 49 1 -12.000164599 1 -3 -4.068497924 49 1.340200373
## 50 1 -0.264383692 1 -2 3.256775470 50 -4.564393000
## 51 1 9.941663793 0 -7 4.599908980 51 -4.879307114
## 52 1 40.066375493 0 -7 1.950508939 52 -0.272184506
## 53 1 -26.336910404 0 1 4.783620490 53 -0.089251954
## 54 1 26.039632657 1 3 -0.849867817 54 -3.571454040
## 55 1 -38.793398003 1 1 -1.899889581 55 -2.372290390
## 56 1 -12.642586688 1 0 0.721039785 56 -0.973305522
## 57 1 -3.865034645 1 7 -3.591420445 57 -2.017293288
## 58 1 -38.381799814 0 -5 1.030543240 58 2.447287783
## 59 1 6.840027644 0 0 -1.836671806 59 0.363596721
## 60 1 -0.432732119 0 2 3.942214385 60 1.465797716
## 61 1 -8.025461949 1 1 -1.554763977 61 -4.209326757
## 62 1 -1.398401682 1 4 -4.295700496 62 1.811654820
## 63 1 3.029043977 1 3 5.426398314 63 0.379193330
## 64 1 -38.578895384 1 1 -3.923896620 64 0.414277708
## 65 1 -8.071686009 0 0 -6.104801168 65 -1.719475229
## 66 1 11.425770271 1 7 -5.283457160 66 1.156773435
## 67 1 17.789329786 1 1 -4.984452770 67 4.798803187
## 68 1 28.043807755 0 1 -3.363025425 68 -4.796199240
## 69 1 23.373781278 0 5 3.326341836 69 1.920440883
## 70 1 -37.867961956 0 -2 11.180388415 70 1.270611912
## 71 1 1.943141337 1 1 -3.008990846 71 -1.951057968
## 72 1 5.135308701 1 4 -1.458088710 72 -0.289790057
## 73 1 -12.135103843 1 -3 -4.204874310 73 2.215131285
## 74 1 35.012491717 1 6 10.158497576 74 2.384127877
## 75 1 -20.799395860 1 -5 2.146860626 75 -0.384662358
## 76 1 18.737823220 0 -1 4.055087606 76 2.563700756
## 77 1 -28.618008686 1 -2 -0.871499743 77 0.414606807
## 78 1 1.317347114 1 6 -1.581857416 78 1.037977841
## 79 1 2.577216044 1 6 -2.465009474 79 -0.521589095
## 80 1 18.919682842 0 0 0.953892371 80 1.285247997
## 81 1 -7.940087692 1 2 -5.518724919 81 2.444418407
## 82 1 -26.334167189 1 -4 -0.532066568 82 1.670246457
## 83 1 -0.426852705 1 -2 7.294963564 83 -0.590128179
## 84 1 28.420255411 1 1 -1.537371491 84 4.911893714
## 85 1 -3.607639143 1 1 3.785907057 85 2.358244144
## 86 1 -1.254163015 1 2 -8.554126950 86 -2.605166287
## 87 1 -12.615604696 1 7 2.976548039 87 -3.190370851
## 88 1 3.291452127 1 0 -10.339833252 88 0.681601838
## 89 1 -1.219427637 1 -7 -0.428698780 89 -2.027925786
## 90 1 5.061885082 1 7 0.161432749 90 2.333466711
## 91 1 18.523670875 1 -4 1.448431987 91 -0.629668080
## 92 1 11.257257990 1 -2 4.044690344 92 2.251839225
## 93 1 -29.451168410 1 7 1.631312357 93 4.845736683
## 94 1 24.432043582 1 3 -8.519287396 94 -4.025908558
## 95 1 16.181078860 0 2 2.627522946 95 -0.141142173
## 96 1 32.426275529 1 7 -4.015169249 96 -2.716030685
## 97 1 -6.917215345 1 -6 -1.188278604 97 0.743261281
## 98 1 6.972488065 1 -3 4.276745293 98 3.119655303
## 99 1 -7.370479797 1 -2 -2.347435224 99 1.627187588
## 100 1 11.090257967 0 -6 0.590063920 100 1.264474003
## 101 1 6.672095471 1 2 6.237207774 101 0.534891919
## 102 1 8.405713663 1 -3 -3.441986493 102 -1.265116413
## 103 1 -12.587260258 1 -3 -6.917847875 103 -3.328225204
## 104 1 -2.441933615 1 -4 -0.214662076 104 3.475620042
## 105 1 -11.008466021 1 -6 -1.046171149 105 1.205449635
## 106 1 9.710972763 1 -1 -7.334967272 106 2.887886682
## 107 1 26.640220616 0 -6 11.178891900 107 -2.272561896
## 108 1 1.312845398 1 1 -3.125724817 108 3.047043597
## 109 1 45.838754849 1 -7 -5.839568927 109 4.692583971
## 110 1 -15.425100671 0 -5 0.747644157 110 -4.207700202
## 111 1 18.383923492 1 5 4.306599502 111 2.261552855
## 112 1 -6.431093599 0 -4 -5.231017035 112 -1.449080464
## 113 1 13.493738784 1 -2 0.861300056 113 -8.700070330
## 114 1 -29.337919296 1 3 -1.535103755 114 -0.671175866
## 115 1 11.319996638 0 3 4.516728049 115 -0.078507145
## 116 1 6.528605880 0 7 4.926243468 116 1.744719158
## 117 1 6.932575528 0 2 8.219142418 117 -3.352145984
## 118 1 0.386333975 0 -6 4.072798622 118 -1.312174991
## 119 1 13.777893890 0 4 4.125565803 119 -1.296239769
## 120 1 -32.600633152 1 -2 -1.728869611 120 -0.304126667
## 121 1 3.268232007 1 7 2.055736674 121 -0.463065012
## 122 1 32.282683507 1 -7 -3.216071915 122 1.319474328
## 123 1 8.954615688 1 -3 4.710297384 123 3.121244453
## 124 1 9.675958641 1 2 2.347958919 124 0.556582527
## 125 1 -36.046231724 1 -5 -3.310561190 125 3.249583844
## 126 1 0.217996750 1 -1 -1.547355511 126 -4.679046580
## 127 1 -6.951356075 1 -3 3.972871318 127 -0.043414673
## 128 1 -11.420423842 1 5 -4.574254782 128 -0.315868863
## 129 1 -2.636162643 1 -7 -2.327601547 129 -1.881740134
## 130 1 38.206366222 1 -2 4.098346815 130 -0.126073105
## 131 1 4.067631967 0 2 7.605178690 131 -2.766454514
## 132 1 -14.895593752 1 5 1.326773952 132 4.366329165
## 133 1 -9.395918296 1 6 3.095334699 133 -0.495104382
## 134 1 -20.190045688 0 -2 2.355404183 134 -0.767287635
## 135 1 6.555414430 0 -7 2.315721067 135 -0.524118053
## 136 1 8.948429395 0 3 -0.800806814 136 -2.022108051
## 137 1 29.935026978 1 -5 -0.178427360 137 -1.616016541
## 138 1 5.299618581 0 -7 -2.873424079 138 2.207440188
## 139 1 -0.288802662 0 6 4.226750084 139 4.457754735
## 140 1 -31.498777022 1 -3 -2.908830613 140 -1.633647205
## 141 1 0.763019685 1 6 -2.511785528 141 -1.145237607
## 142 1 -59.443528118 0 -7 5.440381323 142 -0.680971572
## 143 1 -17.538747773 1 -4 -1.964765097 143 -2.782401365
## 144 1 12.025787332 1 6 -3.302305596 144 -0.414359985
## 145 1 20.936710342 0 -2 -0.745125634 145 -5.746871874
## 146 1 4.734085396 1 6 -4.377426488 146 2.665506883
## 147 1 21.009446201 0 3 8.495007854 147 -2.435973198
## 148 1 -14.402858498 1 -7 0.340951308 148 -1.853056098
## 149 1 -1.967190588 0 1 2.665937901 149 -1.717219964
## 150 1 5.409705212 1 0 -0.404744966 150 -1.718797047
## 151 1 -1.484247345 0 0 12.233907139 151 -1.171699237
## 152 1 -21.831098815 1 -7 1.919495168 152 3.531919140
## 153 1 11.530222700 1 -4 -5.260857890 153 0.031175067
## 154 1 21.020758143 1 3 -8.611169134 154 6.995930232
## 155 1 -8.396890358 0 2 -3.154096248 155 -5.902784453
## 156 1 12.118392958 1 1 -4.767979897 156 -7.213414180
## 157 1 20.721247545 0 -4 -0.399912326 157 -3.238120020
## 158 1 -16.798730745 0 -6 5.313479511 158 2.422403521
## 159 1 30.408828916 0 0 6.755284179 159 -1.221610925
## 160 1 41.021601781 1 6 1.166451102 160 -2.987443816
## 161 1 18.931410247 1 -7 1.669493957 161 -0.387968360
## 162 1 -15.669127659 1 -4 -3.823851290 162 2.142075871
## 163 1 21.955410061 1 -1 3.846922301 163 -6.504228147
## 164 1 42.598631217 0 1 -1.075064807 164 4.451078060
## 165 1 -9.914791683 1 7 1.916902926 165 1.965800010
## 166 1 2.675691584 1 4 -2.712476806 166 4.905213389
## 167 1 -45.048913760 1 1 -10.472891099 167 2.152847637
## 168 1 6.595583352 1 4 1.890250293 168 1.903840981
## 169 1 12.340285768 1 7 -3.529671141 169 2.251984226
## 170 1 -10.840188764 0 -3 -2.935522649 170 -1.516425603
## 171 1 5.998924567 1 -6 0.500431796 171 3.818610940
## 172 1 20.096889641 0 -7 6.010331794 172 -3.863115241
## 173 1 6.790873093 1 1 3.299219980 173 -2.128857312
## 174 1 -22.567561406 1 5 -3.133726778 174 5.741514480
## 175 1 9.222023913 1 0 -2.648880530 175 -0.721643563
## 176 1 15.972577812 1 -3 4.382515935 176 3.369247912
## 177 1 32.459264383 1 -5 -0.250691059 177 -1.916768421
## 178 1 5.657295692 1 7 11.401951764 178 -3.223726828
## 179 1 -29.245752673 1 6 6.899052819 179 0.154324852
## 180 1 -24.353935132 1 -1 -2.868180171 180 -4.068790320
## 181 1 5.036823087 1 -2 -7.084187140 181 -6.041616726
## 182 1 8.265203477 1 -6 -6.224537295 182 0.275729049
## 183 1 6.257315062 0 0 4.500412400 183 -5.458582393
## 184 1 -22.773427764 0 -7 9.947151283 184 0.189414271
## 185 1 -5.373255833 1 3 -13.934876432 185 -0.064392736
## 186 1 40.293651507 1 -2 -0.115676521 186 0.697334114
## 187 1 68.189097548 1 -2 1.020804197 187 -5.648188460
## 188 1 14.517978582 1 -5 0.221142623 188 -3.162949686
## 189 1 21.681233875 1 -2 3.135487439 189 0.515239375
## 190 1 16.238995681 1 -7 -4.736247771 190 7.296652636
## 191 1 2.774816670 1 4 -5.614134719 191 2.435552848
## 192 1 -2.029976563 1 2 -0.101618247 192 2.064486726
## 193 1 3.931604907 1 6 6.125127717 193 -2.402031190
## 194 1 7.209003154 1 0 0.415294923 194 0.604231325
## 195 1 14.222196019 1 -1 -9.429575852 195 0.061181973
## 196 1 -17.739163122 1 1 5.284635638 196 -3.431852160
## 197 1 -35.745067837 1 -4 3.064560943 197 1.431907654
## 198 1 -6.437625491 0 -7 2.948250454 198 -2.187540451
## 199 1 5.285612326 0 -7 -0.332256705 199 0.734808851
## 200 1 -11.358184929 1 5 -3.040539404 200 -1.745398643
## 201 1 22.024710162 0 -4 4.736522872 201 0.856600964
## 202 1 21.394188233 1 -2 1.965849788 202 2.550710056
## 203 1 22.351215906 1 5 -1.926029656 203 3.282163412
## 204 1 -17.364638632 1 4 -3.046315609 204 0.823165117
## 205 1 -42.158112554 1 -3 -1.921925730 205 -2.376136458
## 206 1 42.624943626 1 -3 -4.507118346 206 -0.278558660
## 207 1 39.824029100 0 5 4.364729467 207 0.188527828
## 208 1 11.933221511 0 -5 2.809106293 208 2.385801945
## 209 1 -15.019766022 1 -2 -8.786392610 209 -0.925385458
## 210 1 -21.459190273 1 -7 -4.060580475 210 -3.631694173
## 211 1 16.854441797 1 3 1.193452937 211 1.876633370
## 212 1 41.437551252 0 -2 12.530008597 212 -0.772028141
## 213 1 -6.029000048 1 -5 -4.902935825 213 -3.976985042
## 214 1 -38.770015903 0 0 7.830625277 214 -2.123854207
## 215 1 -7.257890778 1 7 -3.794337177 215 1.026139173
## 216 1 20.861345960 0 -7 2.469225522 216 8.052832513
## 217 1 16.704770142 1 1 -1.635752260 217 -0.693185691
## 218 1 9.606549727 1 3 -2.174395199 218 5.843353001
## 219 1 -15.307745442 0 -2 7.598615530 219 -1.673763388
## 220 1 -5.588543422 1 3 0.018389016 220 5.257484506
## 221 1 -32.353904718 1 -2 -2.011699751 221 -3.182951062
## 222 1 4.196134177 1 3 -7.477332166 222 2.650953673
## 223 1 30.539633906 1 -7 -4.729927566 223 0.017722118
## 224 1 -12.293739090 0 2 4.532147541 224 6.777331454
## 225 1 -34.343081659 1 6 -9.069338114 225 -2.374180943
## 226 1 27.225299905 1 -5 -4.758877158 226 3.569956858
## 227 1 -9.447590037 1 -6 5.634260496 227 2.434484630
## 228 1 -12.058335946 1 7 0.699445324 228 -1.471415870
## 229 1 20.007053130 1 -1 0.501699884 229 -5.510127988
## 230 1 -21.997251451 1 -3 -6.022643147 230 4.719019024
## 231 1 -36.029087227 1 -2 -0.417291482 231 -0.038780139
## 232 1 -9.248830669 1 -3 -3.985411234 232 -2.367867082
## 233 1 -25.011893797 0 1 5.507622893 233 -1.339574310
## 234 1 25.680548634 1 -4 0.542052031 234 -2.378307586
## 235 1 30.740715839 1 -5 -5.952533995 235 -2.227937976
## 236 1 -14.177203017 0 -2 1.399606380 236 0.505982214
## 237 1 -7.676631217 0 -7 3.887038866 237 -1.618652208
## 238 1 -2.024515483 1 -5 -1.105448620 238 -3.214252134
## 239 1 -13.275602319 0 -3 -0.688384177 239 5.346589087
## 240 1 48.155975966 1 4 0.576150216 240 -1.564433108
## 241 1 12.044364423 0 -6 7.666347864 241 0.879008543
## 242 1 33.693692191 0 -4 -3.278872301 242 -1.614392968
## 243 1 -5.949181992 0 1 4.291893504 243 -0.072129274
## 244 1 6.043216789 1 2 -5.938352759 244 -4.847003897
## 245 1 -6.441468531 1 0 2.697701902 245 1.361108880
## 246 1 10.329432062 1 4 -1.331810814 246 -1.993633820
## 247 1 -16.960109363 0 -4 3.790861173 247 1.043990535
## 248 1 -4.686035555 1 0 -5.157010146 248 -0.841655505
## 249 1 -9.972295613 1 1 5.124657552 249 -2.245526705
## 250 1 25.053802261 1 -2 -0.726307961 250 3.321355196
## 251 1 8.229287217 1 -4 0.546119151 251 -2.072303724
## 252 1 -20.678834146 1 -5 -7.392993973 252 -2.627937523
## 253 1 -1.772706716 1 0 -11.395956112 253 -0.356005835
## 254 1 -19.426045687 1 -2 3.000384452 254 5.736613397
## 255 1 2.271477119 1 -3 -1.732890997 255 -7.344838242
## 256 1 37.820669320 1 7 -1.425768360 256 -2.134152601
## 257 1 23.356054306 1 0 -3.238260790 257 1.464915310
## 258 1 -8.594627863 0 -6 -6.361073502 258 -3.368787277
## 259 1 -8.301383195 0 -1 -0.875865904 259 1.371476012
## 260 1 -19.017212057 1 7 -11.006597887 260 -0.560305384
## 261 1 26.707859456 1 -4 2.899379897 261 0.839505012
## 262 1 -18.172505532 1 -7 0.313982355 262 1.142168262
## 263 1 -36.540226561 0 2 8.875622590 263 -0.008316246
## 264 1 -2.086635108 0 -4 6.918473499 264 -4.640163875
## 265 1 41.274048669 1 4 8.154801432 265 -4.606649547
## 266 1 29.133390088 1 7 -4.041956491 266 -6.059305239
## 267 1 4.886892636 1 -2 0.477827257 267 -3.702518056
## 268 1 8.584484286 0 7 4.584040951 268 -2.147386488
## 269 1 27.826047885 1 2 2.369217715 269 -0.562280150
## 270 1 -4.407351609 1 7 -1.976228580 270 2.898838580
## 271 1 41.128800126 1 -1 2.146992719 271 3.471474130
## 272 1 -0.798850626 0 -7 4.254653954 272 -0.995402782
## 273 1 13.600903212 1 0 -0.851973089 273 -0.616233325
## 274 1 -6.207545322 1 -7 -0.003957019 274 0.190536113
## 275 1 -5.389936473 0 -3 4.303207477 275 -3.124158100
## 276 1 -16.992499951 1 4 3.201711901 276 0.192717374
## 277 1 -5.719788659 1 -6 0.534381448 277 -3.604381671
## 278 1 -23.284664930 1 -3 -7.302388822 278 -4.367308695
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## 921 1 -18.172168634 0 -2 4.703889962 921 1.577027198
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## 925 1 3.056214655 1 0 -2.394661437 925 -3.904249257
## 926 1 6.538417148 1 7 -6.571013705 926 5.501517400
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## 929 1 7.353031670 1 5 1.872998291 929 2.739355968
## 930 1 -15.723741080 1 4 4.048922108 930 7.433059276
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## 935 1 -42.514659070 1 1 -7.113995746 935 0.304085622
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## 946 1 -26.882439943 0 0 2.482534665 946 0.501561864
## 947 1 -5.027070486 1 3 6.765158174 947 2.793517851
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## 981 1 -18.177957900 1 5 1.012272223 981 -1.385610932
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## 984 1 -9.208231229 0 -3 10.554792720 984 3.963980693
## 985 1 4.930128018 0 -7 0.531595903 985 -2.606651903
## 986 1 -19.994985872 0 -5 1.443191410 986 -7.119843586
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## 988 1 -11.031243344 1 4 1.296230942 988 3.243390007
## 989 1 22.469278159 1 6 -2.476153692 989 3.268034108
## 990 1 23.275513249 1 -1 -1.447508337 990 3.008368508
## 991 1 3.606519682 0 -7 3.203419766 991 0.475489713
## 992 1 -13.051738850 1 -3 -0.707314307 992 0.190142994
## 993 1 11.007686809 1 -3 -4.399236930 993 -5.839361097
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## 998 1 23.885768774 0 4 4.045209623 998 0.439767025
## 999 1 21.301854304 0 -6 5.260219579 999 2.449137045
## 1000 1 -32.249578725 0 5 7.883550864 1000 1.930632169
## fixed_outcome random_effects achievement
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## 2 47.02168 0 47.13993
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## 24 44.38189 0 41.89938
## 25 51.25896 0 51.25562
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## 215 48.04826 0 49.07440
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## 887 48.41782 0 42.34791
## 888 45.44038 0 44.07324
## 889 43.82846 0 40.47985
## 890 52.84252 0 53.37788
## 891 44.01766 0 38.37852
## 892 52.88877 0 53.00168
## 893 47.01403 0 43.91679
## 894 44.22611 0 48.19225
## 895 49.45921 0 45.88106
## 896 61.13268 0 67.79815
## 897 52.11333 0 49.29489
## 898 48.89686 0 52.94650
## 899 61.51898 0 60.16230
## 900 48.87504 0 45.62733
## 901 45.50939 0 47.99000
## 902 42.22804 0 42.62871
## 903 52.64778 0 52.85094
## 904 39.26965 0 41.30120
## 905 56.99808 0 57.28878
## 906 61.04475 0 64.42263
## 907 35.56650 0 36.44113
## 908 57.54542 0 58.53500
## 909 47.76335 0 51.24016
## 910 52.13803 0 55.59823
## 911 47.82870 0 54.74413
## 912 44.80369 0 40.85727
## 913 51.07270 0 51.22818
## 914 52.54214 0 54.13765
## 915 54.02123 0 59.32670
## 916 43.85128 0 43.20230
## 917 52.46487 0 51.08479
## 918 52.60895 0 53.01861
## 919 41.53947 0 42.23343
## 920 47.60247 0 51.60873
## 921 43.70711 0 45.28413
## 922 41.25241 0 40.45863
## 923 60.67195 0 58.88487
## 924 58.00402 0 55.67107
## 925 51.82382 0 47.91957
## 926 52.87261 0 58.37413
## 927 46.40433 0 44.94995
## 928 47.55655 0 51.16991
## 929 55.10946 0 57.84882
## 930 46.32273 0 53.75579
## 931 39.03783 0 35.02495
## 932 59.22115 0 56.11692
## 933 50.03936 0 47.74204
## 934 41.43690 0 38.24866
## 935 32.51564 0 32.81972
## 936 40.82450 0 41.55732
## 937 47.89547 0 53.10714
## 938 53.30081 0 49.29076
## 939 45.75698 0 48.60274
## 940 55.55330 0 55.85748
## 941 44.12653 0 42.61655
## 942 39.32604 0 41.45575
## 943 52.39759 0 51.64305
## 944 38.67085 0 37.91099
## 945 48.35650 0 49.67146
## 946 39.86766 0 40.36922
## 947 51.18046 0 53.97398
## 948 73.72453 0 76.85426
## 949 45.64841 0 46.53359
## 950 47.27924 0 50.23731
## 951 55.25810 0 50.74062
## 952 49.92331 0 55.54584
## 953 39.90305 0 39.14142
## 954 56.82937 0 56.37826
## 955 45.94474 0 51.29470
## 956 34.66390 0 39.16148
## 957 31.22911 0 35.87381
## 958 50.32273 0 50.37421
## 959 45.46235 0 48.36320
## 960 50.69558 0 53.31752
## 961 50.22254 0 47.68565
## 962 52.67093 0 54.09385
## 963 48.13452 0 49.09936
## 964 49.64543 0 53.60754
## 965 55.02668 0 47.62213
## 966 48.65367 0 49.94457
## 967 48.03326 0 47.26785
## 968 41.14404 0 42.82980
## 969 42.89363 0 46.65373
## 970 60.42502 0 60.30425
## 971 53.37088 0 56.21343
## 972 65.76433 0 64.11125
## 973 53.29328 0 50.93665
## 974 42.88440 0 41.81695
## 975 46.44792 0 50.08647
## 976 56.08153 0 58.09976
## 977 68.95509 0 65.96120
## 978 52.28559 0 51.84696
## 979 58.42156 0 55.82570
## 980 47.48758 0 53.13310
## 981 44.68188 0 43.29627
## 982 46.88789 0 44.73045
## 983 64.03012 0 62.03417
## 984 48.65541 0 52.61939
## 985 51.40495 0 48.79830
## 986 41.86280 0 34.74296
## 987 50.57697 0 51.12162
## 988 47.51156 0 50.75495
## 989 60.16867 0 63.43671
## 990 60.04833 0 63.05670
## 991 51.54346 0 52.01895
## 992 45.50248 0 45.69262
## 993 54.20327 0 48.36390
## 994 28.04200 0 28.11850
## 995 53.59475 0 56.51452
## 996 39.64990 0 40.08806
## 997 62.92202 0 59.76006
## 998 60.96561 0 61.40538
## 999 59.23580 0 61.68493
## 1000 39.57106 0 41.50169This is a two-step process, estimate propensity scores, then estimate final model.
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
model_fit = list(formula = achievement ~ 1 + motivation + trt + age + ses,
model_function = 'lm'),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'covariate'
)
)
simulate_fixed(data = NULL, sim_args = sim_arguments) |>
simulate_error(sim_args = sim_arguments) |>
generate_response(sim_arguments)|>
fit_propensity(sim_arguments) |>
model_fit(sim_arguments) |>
extract_coefficients()## # A tibble: 6 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 51.1 0.848 60.2 0
## 2 motivation 0.404 0.00523 77.2 0
## 3 trt 1.12 0.270 4.15 0.0000359
## 4 age 0.158 0.0427 3.71 0.000222
## 5 ses 0.159 0.0602 2.63 0.00860
## 6 propensity -1.83 1.27 -1.44 0.151
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'ipw'
)
)
simulate_fixed(data = NULL, sim_args = sim_arguments) |>
simulate_error(sim_args = sim_arguments) |>
generate_response(sim_arguments)|>
fit_propensity(sim_arguments) |>
model_fit(sim_arguments) |>
extract_coefficients()## # A tibble: 5 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 49.8 0.204 245. 0
## 2 motivation 0.396 0.00498 79.5 0
## 3 trt 1.49 0.260 5.74 1.24e- 8
## 4 age 0.109 0.0239 4.55 6.11e- 6
## 5 ses 0.245 0.0226 10.8 7.57e-26
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'sbw'
)
)
simulate_fixed(data = NULL, sim_args = sim_arguments) |>
simulate_error(sim_args = sim_arguments) |>
generate_response(sim_arguments)|>
fit_propensity(sim_arguments) |>
model_fit(sim_arguments) |>
extract_coefficients()## # A tibble: 5 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 49.8 0.207 241. 0
## 2 motivation 0.396 0.00509 77.9 0
## 3 trt 1.25 0.262 4.77 2.08e- 6
## 4 age 0.106 0.0239 4.44 1.00e- 5
## 5 ses 0.244 0.0237 10.3 1.24e-23
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
model_fit = list(formula = achievement ~ 1 + motivation + trt + age + ses,
model_function = 'lm'),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'covariate'
),
replications = 20,
extract_coefficients = TRUE
)
replicate_simulation(sim_arguments) |>
compute_statistics(sim_arguments, alternative_power = FALSE,
type_1_error = FALSE)## # A tibble: 6 × 7
## term avg_estimate power param_estimate_sd avg_standard_error precision_ratio
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Inte… 50.0 1 0.883 0.837 1.06
## 2 age 0.0961 0.5 0.0461 0.0396 1.16
## 3 motiv… 0.400 1 0.00515 0.00502 1.03
## 4 prope… 0.00924 0 1.40 1.23 1.14
## 5 ses 0.256 0.95 0.0592 0.0568 1.04
## 6 trt 1.28 1 0.217 0.263 0.824
## # ℹ 1 more variable: replications <dbl>
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
model_fit = list(formula = achievement ~ 1 + motivation + trt + age + ses,
model_function = 'lm'),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'ipw'
),
replications = 500,
extract_coefficients = TRUE
)
replicate_simulation(sim_arguments) |>
compute_statistics(sim_arguments, alternative_power = FALSE,
type_1_error = FALSE)## # A tibble: 5 × 7
## term avg_estimate power param_estimate_sd avg_standard_error precision_ratio
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Inte… 50.0 1 0.307 0.216 1.42
## 2 age 0.101 0.88 0.0420 0.0252 1.66
## 3 motiv… 0.400 1 0.00975 0.00502 1.94
## 4 ses 0.245 0.996 0.0544 0.0205 2.65
## 5 trt 1.17 0.938 0.374 0.263 1.42
## # ℹ 1 more variable: replications <dbl>
sim_arguments <- list(
formula = achievement ~ 1 + motivation + trt + age + ses,
fixed = list(
motivation = list(var_type = 'continuous',
mean = 0, sd = 20)
),
sample_size = 1000,
error = list(variance = 10),
reg_weights = c(50, 0.4, 1.2, 0.1, 0.25),
propensity = list(
formula = trt ~ 1 + age + ses,
fixed = list(age = list(var_type = 'ordinal',
levels = -7:7),
ses = list(var_type = 'continuous',
mean = 0, sd = 5)),
sample_size = 1000,
error = list(variance = 5),
reg_weights = c(2, 0.3, -0.5),
outcome_type = 'binary'
),
model_fit = list(formula = achievement ~ 1 + motivation + trt + age + ses,
model_function = 'lm'),
propensity_model = list(
formula = trt ~ 1 + age + ses,
propensity_type = 'sbw'
),
replications = 500,
extract_coefficients = TRUE
)
replicate_simulation(sim_arguments) |>
compute_statistics(sim_arguments, alternative_power = FALSE,
type_1_error = FALSE)## # A tibble: 5 × 7
## term avg_estimate power param_estimate_sd avg_standard_error precision_ratio
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Inte… 50.0 1 0.301 0.216 1.39
## 2 age 0.100 0.864 0.0401 0.0252 1.59
## 3 motiv… 0.400 1 0.00927 0.00502 1.85
## 4 ses 0.250 1 0.0524 0.0205 2.55
## 5 trt 1.19 0.936 0.381 0.262 1.45
## # ℹ 1 more variable: replications <dbl>