R/point_estimate.R
point_estimate.RdExtract point estimates of parameters from a fit object
point_estimate(fit, pars = c("pi", "theta", "z"), ...)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 9.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.92 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: Elapsed Time: 1.296 seconds (Warm-up)
#> Chain 1: 1.337 seconds (Sampling)
#> Chain 1: 2.633 seconds (Total)
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#>
#> 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!
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#> Chain 2: Elapsed Time: 1.258 seconds (Warm-up)
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#> Chain 2: 2.581 seconds (Total)
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#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000112 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.12 seconds.
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#> Chain 3: Elapsed Time: 1.316 seconds (Warm-up)
#> Chain 3: 1.18 seconds (Sampling)
#> Chain 3: 2.496 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'dawid_skene' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 8.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.87 seconds.
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#> Chain 4: Elapsed Time: 1.228 seconds (Warm-up)
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#> Chain 4: 2.593 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)
# }