point_estimate: Extract point estimates of parameters from a fit object

In rater: Statistical Models of Repeated Categorical Rating Data

Extract point estimates of parameters from a fit object

Description

Extract point estimates of parameters from a fit object

Usage

point_estimate(fit, pars = c("pi", "theta", "z"), ...)

Arguments

fit	A rater fit object
pars	A character vector of parameter names to return. By default c("pi", "theta", "z").
...	Extra arguments

Details

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.

Value

A named list of the parameter estimates.

See Also

class_probabilities()

Examples

# A model fit using MCMC.
mcmc_fit <- rater(anesthesia, "dawid_skene")

# 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)

rater documentation built on Sept. 12, 2023, 1:13 a.m.