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
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#> 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