This function returns the choice probabilities of an RprobitB_fit
object.
Arguments
- x
An object of class
RprobitB_fit.- data
Either
NULLor an object of classRprobitB_data. In the former case, choice probabilities are computed for the data that was used for model fitting. Alternatively, a new data set can be provided.- par_set
Specifying the parameter set for calculation and either
a function that computes a posterior point estimate (the default is
mean()),"true"to select the true parameter set,an object of class
RprobitB_parameter.
Value
A data frame of choice probabilities with choice situations in rows and
alternatives in columns. The first two columns are the decider identifier
"id" and the choice situation identifier "idc".
Examples
data <- simulate_choices(form = choice ~ covariate, N = 10, T = 10, J = 2)
x <- fit_model(data)
#> Computing sufficient statistics - 0 of 4
#> Computing sufficient statistics - 1 of 4
#> Computing sufficient statistics - 2 of 4
#> Computing sufficient statistics - 3 of 4
#> Computing sufficient statistics - 4 of 4
#> Gibbs sampler - 1 of 1000 iterations
#> Gibbs sampler - 10 of 1000 iterations
#> Gibbs sampler - 20 of 1000 iterations
#> Gibbs sampler - 30 of 1000 iterations
#> Gibbs sampler - 40 of 1000 iterations
#> Gibbs sampler - 50 of 1000 iterations
#> Gibbs sampler - 60 of 1000 iterations
#> Gibbs sampler - 70 of 1000 iterations
#> Gibbs sampler - 80 of 1000 iterations
#> Gibbs sampler - 90 of 1000 iterations
#> Gibbs sampler - 100 of 1000 iterations
#> Gibbs sampler - 110 of 1000 iterations
#> Gibbs sampler - 120 of 1000 iterations
#> Gibbs sampler - 130 of 1000 iterations
#> Gibbs sampler - 140 of 1000 iterations
#> Gibbs sampler - 150 of 1000 iterations
#> Gibbs sampler - 160 of 1000 iterations
#> Gibbs sampler - 170 of 1000 iterations
#> Gibbs sampler - 180 of 1000 iterations
#> Gibbs sampler - 190 of 1000 iterations
#> Gibbs sampler - 200 of 1000 iterations
#> Gibbs sampler - 210 of 1000 iterations
#> Gibbs sampler - 220 of 1000 iterations
#> Gibbs sampler - 230 of 1000 iterations
#> Gibbs sampler - 240 of 1000 iterations
#> Gibbs sampler - 250 of 1000 iterations
#> Gibbs sampler - 260 of 1000 iterations
#> Gibbs sampler - 270 of 1000 iterations
#> Gibbs sampler - 280 of 1000 iterations
#> Gibbs sampler - 290 of 1000 iterations
#> Gibbs sampler - 300 of 1000 iterations
#> Gibbs sampler - 310 of 1000 iterations
#> Gibbs sampler - 320 of 1000 iterations
#> Gibbs sampler - 330 of 1000 iterations
#> Gibbs sampler - 340 of 1000 iterations
#> Gibbs sampler - 350 of 1000 iterations
#> Gibbs sampler - 360 of 1000 iterations
#> Gibbs sampler - 370 of 1000 iterations
#> Gibbs sampler - 380 of 1000 iterations
#> Gibbs sampler - 390 of 1000 iterations
#> Gibbs sampler - 400 of 1000 iterations
#> Gibbs sampler - 410 of 1000 iterations
#> Gibbs sampler - 420 of 1000 iterations
#> Gibbs sampler - 430 of 1000 iterations
#> Gibbs sampler - 440 of 1000 iterations
#> Gibbs sampler - 450 of 1000 iterations
#> Gibbs sampler - 460 of 1000 iterations
#> Gibbs sampler - 470 of 1000 iterations
#> Gibbs sampler - 480 of 1000 iterations
#> Gibbs sampler - 490 of 1000 iterations
#> Gibbs sampler - 500 of 1000 iterations
#> Gibbs sampler - 510 of 1000 iterations
#> Gibbs sampler - 520 of 1000 iterations
#> Gibbs sampler - 530 of 1000 iterations
#> Gibbs sampler - 540 of 1000 iterations
#> Gibbs sampler - 550 of 1000 iterations
#> Gibbs sampler - 560 of 1000 iterations
#> Gibbs sampler - 570 of 1000 iterations
#> Gibbs sampler - 580 of 1000 iterations
#> Gibbs sampler - 590 of 1000 iterations
#> Gibbs sampler - 600 of 1000 iterations
#> Gibbs sampler - 610 of 1000 iterations
#> Gibbs sampler - 620 of 1000 iterations
#> Gibbs sampler - 630 of 1000 iterations
#> Gibbs sampler - 640 of 1000 iterations
#> Gibbs sampler - 650 of 1000 iterations
#> Gibbs sampler - 660 of 1000 iterations
#> Gibbs sampler - 670 of 1000 iterations
#> Gibbs sampler - 680 of 1000 iterations
#> Gibbs sampler - 690 of 1000 iterations
#> Gibbs sampler - 700 of 1000 iterations
#> Gibbs sampler - 710 of 1000 iterations
#> Gibbs sampler - 720 of 1000 iterations
#> Gibbs sampler - 730 of 1000 iterations
#> Gibbs sampler - 740 of 1000 iterations
#> Gibbs sampler - 750 of 1000 iterations
#> Gibbs sampler - 760 of 1000 iterations
#> Gibbs sampler - 770 of 1000 iterations
#> Gibbs sampler - 780 of 1000 iterations
#> Gibbs sampler - 790 of 1000 iterations
#> Gibbs sampler - 800 of 1000 iterations
#> Gibbs sampler - 810 of 1000 iterations
#> Gibbs sampler - 820 of 1000 iterations
#> Gibbs sampler - 830 of 1000 iterations
#> Gibbs sampler - 840 of 1000 iterations
#> Gibbs sampler - 850 of 1000 iterations
#> Gibbs sampler - 860 of 1000 iterations
#> Gibbs sampler - 870 of 1000 iterations
#> Gibbs sampler - 880 of 1000 iterations
#> Gibbs sampler - 890 of 1000 iterations
#> Gibbs sampler - 900 of 1000 iterations
#> Gibbs sampler - 910 of 1000 iterations
#> Gibbs sampler - 920 of 1000 iterations
#> Gibbs sampler - 930 of 1000 iterations
#> Gibbs sampler - 940 of 1000 iterations
#> Gibbs sampler - 950 of 1000 iterations
#> Gibbs sampler - 960 of 1000 iterations
#> Gibbs sampler - 970 of 1000 iterations
#> Gibbs sampler - 980 of 1000 iterations
#> Gibbs sampler - 990 of 1000 iterations
#> Gibbs sampler - 1000 of 1000 iterations
choice_probabilities(x)
#> id idc A B
#> 1 1 1 4.932177e-01 0.506782344
#> 2 1 2 9.517899e-01 0.048210140
#> 3 1 3 1.781420e-02 0.982185802
#> 4 1 4 9.737644e-01 0.026235575
#> 5 1 5 2.593394e-01 0.740660593
#> 6 1 6 1.394951e-01 0.860504918
#> 7 1 7 3.249789e-01 0.675021089
#> 8 1 8 7.228659e-01 0.277134143
#> 9 1 9 3.513767e-04 0.999648623
#> 10 1 10 4.797067e-01 0.520293289
#> 11 2 1 3.876312e-01 0.612368800
#> 12 2 2 8.399955e-01 0.160004513
#> 13 2 3 2.713725e-04 0.999728628
#> 14 2 4 1.285106e-01 0.871489416
#> 15 2 5 3.350039e-01 0.664996076
#> 16 2 6 3.720707e-02 0.962792932
#> 17 2 7 5.808553e-02 0.941914469
#> 18 2 8 8.505197e-03 0.991494803
#> 19 2 9 7.033465e-01 0.296653502
#> 20 2 10 1.239741e-01 0.876025855
#> 21 3 1 2.291752e-01 0.770824838
#> 22 3 2 5.344472e-02 0.946555279
#> 23 3 3 1.011996e-02 0.989880042
#> 24 3 4 7.280773e-01 0.271922685
#> 25 3 5 5.646351e-02 0.943536493
#> 26 3 6 5.180008e-02 0.948199915
#> 27 3 7 7.417511e-01 0.258248872
#> 28 3 8 1.382350e-02 0.986176505
#> 29 3 9 5.914428e-02 0.940855724
#> 30 3 10 8.361066e-01 0.163893396
#> 31 4 1 6.026618e-01 0.397338239
#> 32 4 2 3.493744e-02 0.965062562
#> 33 4 3 7.679173e-01 0.232082743
#> 34 4 4 2.145815e-01 0.785418498
#> 35 4 5 1.053219e-02 0.989467806
#> 36 4 6 2.062785e-01 0.793721471
#> 37 4 7 7.120449e-02 0.928795514
#> 38 4 8 2.253851e-01 0.774614880
#> 39 4 9 5.223427e-01 0.477657289
#> 40 4 10 9.134286e-01 0.086571441
#> 41 5 1 5.473963e-02 0.945260372
#> 42 5 2 3.272214e-01 0.672778585
#> 43 5 3 1.218131e-01 0.878186878
#> 44 5 4 8.789859e-02 0.912101413
#> 45 5 5 4.140010e-02 0.958599896
#> 46 5 6 1.876479e-01 0.812352092
#> 47 5 7 1.700860e-01 0.829914023
#> 48 5 8 2.401754e-01 0.759824603
#> 49 5 9 1.093627e-01 0.890637305
#> 50 5 10 1.038872e-01 0.896112795
#> 51 6 1 5.492245e-05 0.999945078
#> 52 6 2 2.333860e-01 0.766613954
#> 53 6 3 5.288959e-01 0.471104114
#> 54 6 4 6.309383e-02 0.936906175
#> 55 6 5 6.007168e-01 0.399283190
#> 56 6 6 8.657904e-01 0.134209572
#> 57 6 7 6.183260e-01 0.381674022
#> 58 6 8 8.375500e-01 0.162450007
#> 59 6 9 2.169853e-01 0.783014714
#> 60 6 10 4.873812e-05 0.999951262
#> 61 7 1 5.966253e-01 0.403374719
#> 62 7 2 1.501737e-04 0.999849826
#> 63 7 3 1.909438e-01 0.809056167
#> 64 7 4 3.998612e-01 0.600138798
#> 65 7 5 1.786103e-02 0.982138969
#> 66 7 6 3.365395e-03 0.996634605
#> 67 7 7 2.041831e-02 0.979581687
#> 68 7 8 3.357516e-01 0.664248405
#> 69 7 9 2.239267e-01 0.776073348
#> 70 7 10 9.976883e-01 0.002311711
#> 71 8 1 1.386012e-01 0.861398788
#> 72 8 2 4.841025e-01 0.515897468
#> 73 8 3 4.039655e-01 0.596034538
#> 74 8 4 1.532953e-01 0.846704731
#> 75 8 5 4.952403e-01 0.504759704
#> 76 8 6 9.841896e-01 0.015810364
#> 77 8 7 3.001694e-01 0.699830618
#> 78 8 8 4.280600e-04 0.999571940
#> 79 8 9 6.825799e-02 0.931742012
#> 80 8 10 5.062417e-01 0.493758274
#> 81 9 1 1.184241e-01 0.881575907
#> 82 9 2 3.123446e-01 0.687655373
#> 83 9 3 8.657735e-01 0.134226520
#> 84 9 4 3.558924e-01 0.644107607
#> 85 9 5 9.478017e-01 0.052198302
#> 86 9 6 8.202939e-01 0.179706098
#> 87 9 7 2.681820e-01 0.731817976
#> 88 9 8 3.595393e-02 0.964046070
#> 89 9 9 9.865736e-01 0.013426377
#> 90 9 10 3.428442e-01 0.657155751
#> 91 10 1 3.049607e-03 0.996950393
#> 92 10 2 8.544151e-02 0.914558493
#> 93 10 3 1.698517e-03 0.998301483
#> 94 10 4 9.860539e-01 0.013946058
#> 95 10 5 8.230816e-01 0.176918428
#> 96 10 6 7.981163e-01 0.201883666
#> 97 10 7 9.212081e-01 0.078791918
#> 98 10 8 2.366363e-05 0.999976336
#> 99 10 9 5.486260e-01 0.451374029
#> 100 10 10 6.423519e-01 0.357648061