This function returns the choice probabilities of an RprobitB_fit
object.
Arguments
- x
An object of class
RprobitB_fit
.- data
Either
NULL
or 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
#> MCMC iteration - 1 of 1000
#> MCMC iteration - 10 of 1000
#> MCMC iteration - 20 of 1000
#> MCMC iteration - 30 of 1000
#> MCMC iteration - 40 of 1000
#> MCMC iteration - 50 of 1000
#> MCMC iteration - 60 of 1000
#> MCMC iteration - 70 of 1000
#> MCMC iteration - 80 of 1000
#> MCMC iteration - 90 of 1000
#> MCMC iteration - 100 of 1000
#> MCMC iteration - 110 of 1000
#> MCMC iteration - 120 of 1000
#> MCMC iteration - 130 of 1000
#> MCMC iteration - 140 of 1000
#> MCMC iteration - 150 of 1000
#> MCMC iteration - 160 of 1000
#> MCMC iteration - 170 of 1000
#> MCMC iteration - 180 of 1000
#> MCMC iteration - 190 of 1000
#> MCMC iteration - 200 of 1000
#> MCMC iteration - 210 of 1000
#> MCMC iteration - 220 of 1000
#> MCMC iteration - 230 of 1000
#> MCMC iteration - 240 of 1000
#> MCMC iteration - 250 of 1000
#> MCMC iteration - 260 of 1000
#> MCMC iteration - 270 of 1000
#> MCMC iteration - 280 of 1000
#> MCMC iteration - 290 of 1000
#> MCMC iteration - 300 of 1000
#> MCMC iteration - 310 of 1000
#> MCMC iteration - 320 of 1000
#> MCMC iteration - 330 of 1000
#> MCMC iteration - 340 of 1000
#> MCMC iteration - 350 of 1000
#> MCMC iteration - 360 of 1000
#> MCMC iteration - 370 of 1000
#> MCMC iteration - 380 of 1000
#> MCMC iteration - 390 of 1000
#> MCMC iteration - 400 of 1000
#> MCMC iteration - 410 of 1000
#> MCMC iteration - 420 of 1000
#> MCMC iteration - 430 of 1000
#> MCMC iteration - 440 of 1000
#> MCMC iteration - 450 of 1000
#> MCMC iteration - 460 of 1000
#> MCMC iteration - 470 of 1000
#> MCMC iteration - 480 of 1000
#> MCMC iteration - 490 of 1000
#> MCMC iteration - 500 of 1000
#> MCMC iteration - 510 of 1000
#> MCMC iteration - 520 of 1000
#> MCMC iteration - 530 of 1000
#> MCMC iteration - 540 of 1000
#> MCMC iteration - 550 of 1000
#> MCMC iteration - 560 of 1000
#> MCMC iteration - 570 of 1000
#> MCMC iteration - 580 of 1000
#> MCMC iteration - 590 of 1000
#> MCMC iteration - 600 of 1000
#> MCMC iteration - 610 of 1000
#> MCMC iteration - 620 of 1000
#> MCMC iteration - 630 of 1000
#> MCMC iteration - 640 of 1000
#> MCMC iteration - 650 of 1000
#> MCMC iteration - 660 of 1000
#> MCMC iteration - 670 of 1000
#> MCMC iteration - 680 of 1000
#> MCMC iteration - 690 of 1000
#> MCMC iteration - 700 of 1000
#> MCMC iteration - 710 of 1000
#> MCMC iteration - 720 of 1000
#> MCMC iteration - 730 of 1000
#> MCMC iteration - 740 of 1000
#> MCMC iteration - 750 of 1000
#> MCMC iteration - 760 of 1000
#> MCMC iteration - 770 of 1000
#> MCMC iteration - 780 of 1000
#> MCMC iteration - 790 of 1000
#> MCMC iteration - 800 of 1000
#> MCMC iteration - 810 of 1000
#> MCMC iteration - 820 of 1000
#> MCMC iteration - 830 of 1000
#> MCMC iteration - 840 of 1000
#> MCMC iteration - 850 of 1000
#> MCMC iteration - 860 of 1000
#> MCMC iteration - 870 of 1000
#> MCMC iteration - 880 of 1000
#> MCMC iteration - 890 of 1000
#> MCMC iteration - 900 of 1000
#> MCMC iteration - 910 of 1000
#> MCMC iteration - 920 of 1000
#> MCMC iteration - 930 of 1000
#> MCMC iteration - 940 of 1000
#> MCMC iteration - 950 of 1000
#> MCMC iteration - 960 of 1000
#> MCMC iteration - 970 of 1000
#> MCMC iteration - 980 of 1000
#> MCMC iteration - 990 of 1000
#> MCMC iteration - 1000 of 1000
#> Computing log-likelihood
choice_probabilities(x)
#> id idc A B
#> 1 1 1 9.670760e-01 3.292403e-02
#> 2 1 2 6.672293e-04 9.993328e-01
#> 3 1 3 3.508590e-01 6.491410e-01
#> 4 1 4 1.493248e-01 8.506752e-01
#> 5 1 5 2.364259e-02 9.763574e-01
#> 6 1 6 9.999776e-01 2.243195e-05
#> 7 1 7 6.032121e-02 9.396788e-01
#> 8 1 8 1.390191e-01 8.609809e-01
#> 9 1 9 8.213518e-01 1.786482e-01
#> 10 1 10 1.680059e-01 8.319941e-01
#> 11 2 1 9.989319e-01 1.068127e-03
#> 12 2 2 1.904414e-10 1.000000e+00
#> 13 2 3 9.999864e-01 1.360708e-05
#> 14 2 4 6.572195e-01 3.427805e-01
#> 15 2 5 5.928364e-01 4.071636e-01
#> 16 2 6 9.555307e-01 4.446933e-02
#> 17 2 7 9.956547e-01 4.345322e-03
#> 18 2 8 7.258709e-01 2.741291e-01
#> 19 2 9 4.459100e-01 5.540900e-01
#> 20 2 10 6.813287e-01 3.186713e-01
#> 21 3 1 4.533256e-01 5.466744e-01
#> 22 3 2 2.375088e-03 9.976249e-01
#> 23 3 3 1.380253e-01 8.619747e-01
#> 24 3 4 1.651974e-03 9.983480e-01
#> 25 3 5 1.444712e-01 8.555288e-01
#> 26 3 6 2.571122e-02 9.742888e-01
#> 27 3 7 7.763636e-01 2.236364e-01
#> 28 3 8 1.975238e-03 9.980248e-01
#> 29 3 9 9.980424e-01 1.957603e-03
#> 30 3 10 1.315133e-01 8.684867e-01
#> 31 4 1 4.756939e-01 5.243061e-01
#> 32 4 2 9.999999e-01 1.339023e-07
#> 33 4 3 3.778355e-09 1.000000e+00
#> 34 4 4 7.335167e-03 9.926648e-01
#> 35 4 5 3.262805e-01 6.737195e-01
#> 36 4 6 9.996842e-01 3.157546e-04
#> 37 4 7 9.325778e-01 6.742216e-02
#> 38 4 8 5.953135e-03 9.940469e-01
#> 39 4 9 9.999991e-01 8.534409e-07
#> 40 4 10 9.752354e-01 2.476465e-02
#> 41 5 1 9.955204e-01 4.479570e-03
#> 42 5 2 9.795583e-01 2.044169e-02
#> 43 5 3 9.998292e-01 1.707689e-04
#> 44 5 4 9.999284e-01 7.161856e-05
#> 45 5 5 6.521494e-01 3.478506e-01
#> 46 5 6 3.737731e-05 9.999626e-01
#> 47 5 7 8.890757e-01 1.109243e-01
#> 48 5 8 2.601325e-05 9.999740e-01
#> 49 5 9 1.721046e-04 9.998279e-01
#> 50 5 10 9.999762e-01 2.380196e-05
#> 51 6 1 2.281409e-02 9.771859e-01
#> 52 6 2 1.243898e-01 8.756102e-01
#> 53 6 3 9.999786e-01 2.141926e-05
#> 54 6 4 5.444757e-01 4.555243e-01
#> 55 6 5 3.178755e-01 6.821245e-01
#> 56 6 6 4.499468e-01 5.500532e-01
#> 57 6 7 7.323155e-01 2.676845e-01
#> 58 6 8 1.111661e-03 9.988883e-01
#> 59 6 9 7.386672e-01 2.613328e-01
#> 60 6 10 5.707067e-01 4.292933e-01
#> 61 7 1 9.760953e-01 2.390473e-02
#> 62 7 2 4.672583e-01 5.327417e-01
#> 63 7 3 9.984960e-01 1.503959e-03
#> 64 7 4 5.573218e-01 4.426782e-01
#> 65 7 5 2.569626e-01 7.430374e-01
#> 66 7 6 6.898174e-01 3.101826e-01
#> 67 7 7 1.771904e-01 8.228096e-01
#> 68 7 8 4.769692e-02 9.523031e-01
#> 69 7 9 1.289114e-01 8.710886e-01
#> 70 7 10 8.813543e-01 1.186457e-01
#> 71 8 1 1.770994e-01 8.229006e-01
#> 72 8 2 2.454725e-11 1.000000e+00
#> 73 8 3 2.546953e-07 9.999997e-01
#> 74 8 4 4.584209e-01 5.415791e-01
#> 75 8 5 7.352477e-01 2.647523e-01
#> 76 8 6 8.857300e-01 1.142700e-01
#> 77 8 7 4.597373e-02 9.540263e-01
#> 78 8 8 9.999952e-01 4.786603e-06
#> 79 8 9 5.313812e-03 9.946862e-01
#> 80 8 10 4.557977e-01 5.442023e-01
#> 81 9 1 9.960162e-01 3.983769e-03
#> 82 9 2 3.633792e-01 6.366208e-01
#> 83 9 3 9.999649e-01 3.514855e-05
#> 84 9 4 9.982788e-01 1.721166e-03
#> 85 9 5 2.200519e-02 9.779948e-01
#> 86 9 6 2.379320e-01 7.620680e-01
#> 87 9 7 4.397738e-03 9.956023e-01
#> 88 9 8 8.409220e-01 1.590780e-01
#> 89 9 9 5.558340e-01 4.441660e-01
#> 90 9 10 9.968000e-01 3.200024e-03
#> 91 10 1 9.236688e-01 7.633124e-02
#> 92 10 2 7.368442e-01 2.631558e-01
#> 93 10 3 9.440315e-01 5.596846e-02
#> 94 10 4 1.125563e-10 1.000000e+00
#> 95 10 5 1.142857e-01 8.857143e-01
#> 96 10 6 9.731630e-07 9.999990e-01
#> 97 10 7 2.498342e-01 7.501658e-01
#> 98 10 8 7.082929e-07 9.999993e-01
#> 99 10 9 5.282008e-03 9.947180e-01
#> 100 10 10 3.940372e-01 6.059628e-01