R/point_estimate.R
class_probabilities.RdExtract latent class probabilities from a rater fit object
class_probabilities(fit, ...)
# S3 method for class 'mcmc_fit'
class_probabilities(fit, ...)
# S3 method for class 'optim_fit'
class_probabilities(fit, ...)A I * K matrix where each element is the probably of item i being of class k. (I is the number of items and K the number of classes).
The latent class probabilities are obtained by marginalising out the latent class and then calculating, for each draw of pi and theta, the conditional probability of the latent class given the other parameters and the data. Averaging these conditional probabilities gives the (unconditional) latent class probabilities retuned by this function.
# \donttest{
fit <- rater(anesthesia, "dawid_skene")
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.9 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 1.277 seconds (Warm-up)
#> Chain 1: 1.402 seconds (Sampling)
#> Chain 1: 2.679 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 8.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 1.316 seconds (Warm-up)
#> Chain 2: 1.311 seconds (Sampling)
#> Chain 2: 2.627 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 8.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.86 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
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#> Chain 3:
#> Chain 3: Elapsed Time: 1.296 seconds (Warm-up)
#> Chain 3: 0.97 seconds (Sampling)
#> Chain 3: 2.266 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 8.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.85 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 4:
#> Chain 4: Elapsed Time: 1.353 seconds (Warm-up)
#> Chain 4: 1.18 seconds (Sampling)
#> Chain 4: 2.533 seconds (Total)
#> Chain 4:
class_probabilities(fit)
#>
#> [,1] [,2] [,3] [,4]
#> 1 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 2 8.808080e-08 5.094311e-05 9.737657e-01 2.618323e-02
#> 3 3.922459e-01 6.071473e-01 9.806120e-05 5.087208e-04
#> 4 5.670012e-03 9.937716e-01 4.258989e-04 1.324657e-04
#> 5 2.539356e-07 9.999620e-01 3.508430e-05 2.705460e-06
#> 6 1.771539e-06 9.992231e-01 7.510137e-04 2.409728e-05
#> 7 9.994226e-01 5.687676e-04 8.460657e-07 7.769488e-06
#> 8 1.525997e-08 3.313524e-05 9.993701e-01 5.967634e-04
#> 9 1.828028e-06 9.999284e-01 6.324071e-05 6.523137e-06
#> 10 3.158375e-06 9.975497e-01 2.400558e-03 4.655979e-05
#> 11 1.778609e-08 1.847128e-08 1.919330e-04 9.998080e-01
#> 12 3.595103e-05 8.713939e-01 1.203974e-01 8.172816e-03
#> 13 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 14 3.739878e-05 9.993373e-01 5.340940e-04 9.117256e-05
#> 15 9.999858e-01 1.212226e-05 1.589141e-07 1.956872e-06
#> 16 9.999910e-01 6.529654e-06 2.957329e-07 2.169103e-06
#> 17 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 18 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 19 4.339654e-05 9.999431e-01 6.436204e-06 7.093539e-06
#> 20 2.672403e-04 9.990811e-01 5.019203e-04 1.497007e-04
#> 21 5.710388e-07 9.999965e-01 1.924017e-06 9.871702e-07
#> 22 4.339654e-05 9.999431e-01 6.436204e-06 7.093539e-06
#> 23 2.539356e-07 9.999620e-01 3.508430e-05 2.705460e-06
#> 24 9.859206e-05 9.998895e-01 5.076641e-06 6.783351e-06
#> 25 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 26 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 27 9.190783e-07 9.999043e-01 8.829466e-05 6.504723e-06
#> 28 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 29 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 30 9.978499e-01 2.115513e-03 1.813211e-06 3.277541e-05
#> 31 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 32 1.594406e-08 9.893938e-04 9.988544e-01 1.562368e-04
#> 33 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 34 5.710388e-07 9.999965e-01 1.924017e-06 9.871702e-07
#> 35 6.654876e-07 9.974205e-01 2.561107e-03 1.777565e-05
#> 36 3.795166e-07 9.787441e-05 7.786894e-01 2.212124e-01
#> 37 2.825898e-04 9.992628e-01 3.973180e-04 5.727234e-05
#> 38 2.256776e-06 7.042278e-01 2.954430e-01 3.269898e-04
#> 39 1.429023e-07 7.538427e-04 9.974306e-01 1.815388e-03
#> 40 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 41 9.999993e-01 1.377410e-07 3.087161e-08 5.231900e-07
#> 42 9.994226e-01 5.687676e-04 8.460657e-07 7.769488e-06
#> 43 9.190783e-07 9.999043e-01 8.829466e-05 6.504723e-06
#> 44 9.999858e-01 1.212226e-05 1.589141e-07 1.956872e-06
#> 45 5.710388e-07 9.999965e-01 1.924017e-06 9.871702e-07
# }