R/point_estimate.R
point_estimate.RdExtract point estimates of parameters from a fit object
point_estimate(fit, pars = c("pi", "theta", "z"), ...)A rater fit object
A character vector of parameter names to return. By default
c("pi", "theta", "z").
Extra arguments
A named list of the parameter estimates.
If the passed fit object was fit using MCMC then the posterior
means are returned. If it was fit through optimisation the maximum a
priori (MAP) estimates are returned. The z parameter returned is the
value of class probabilities which is largest. To return the full
posterior distributions of the latent class use class_probabilities().
For the class conditional model the 'full' theta parameterisation (i.e. appearing to have the same number of parameters as the standard Dawid-Skene model) is calculated and returned. This is designed to allow easier comparison with the full Dawid-Skene model.
# \donttest{
# A model fit using MCMC.
mcmc_fit <- rater(anesthesia, "dawid_skene")
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000194 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.94 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 2.701 seconds (Warm-up)
#> Chain 1: 2.883 seconds (Sampling)
#> Chain 1: 5.584 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000184 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.84 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 2.69 seconds (Warm-up)
#> Chain 2: 2.697 seconds (Sampling)
#> Chain 2: 5.387 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000179 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.79 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)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 2.606 seconds (Warm-up)
#> Chain 3: 2.919 seconds (Sampling)
#> Chain 3: 5.525 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000182 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.82 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 2.586 seconds (Warm-up)
#> Chain 4: 2.398 seconds (Sampling)
#> Chain 4: 4.984 seconds (Total)
#> Chain 4:
# This will return the posterior mean (except for z)
post_mean_estimate <- point_estimate(mcmc_fit)
# A model fit using optimisation.
optim_fit <- rater(anesthesia, dawid_skene(), method = "optim")
# This will output MAP estimates of the parameters.
map_estimate <- point_estimate(optim_fit)
# }