models: Probabilistic models of repeated categorical rating

In rater: Statistical Models of Repeated Categorical Rating Data

Probabilistic models of repeated categorical rating

Description

Functions to set up models and change their prior parameters for use in rater().

Usage

dawid_skene(alpha = NULL, beta = NULL)

hier_dawid_skene(alpha = NULL)

class_conditional_dawid_skene(alpha = NULL, beta_1 = NULL, beta_2 = NULL)

Arguments

alpha	prior parameter for pi
beta	prior parameter for theta. This can either be a K * K matrix, in which case it is interpreted as the prior parameter of all of the J raters, or a J by K by K array in which case it is the fully specified prior parameter for all raters. (Here K is the number of categories in the data and J is the number of raters in the data.)
beta_1	First on diagonal prior probability parameter
beta_2	Second on diagonal prior probability parameter for theta

Value

a rater model object that can be passed to rater().

Examples

# Model with default prior parameters:
default_m <- dawid_skene()

# Changing alpha:
set_alpha_m <- dawid_skene(alpha = c(2, 2, 2))

# Changing beta, single matrix:
# (See details for how this is interpreted.)
beta_mat <- matrix(1, nrow = 4, ncol = 4)
diag(beta_mat) <- 4
beta_mat_m <- dawid_skene()

# The above is equivalent (when the model is fit - see details) to:
beta_array <- array(NA, dim = c(2, 4, 4))
for (i in 1:2) {
  beta_array[i, , ] <- beta_mat
}
beta_array_m <- dawid_skene(beta = beta_array)

# But you can also specify an array where each slice is different.
# (Again, see details for how this is interpreted.)
beta_array[1, , ] <- matrix(1, nrow = 4, ncol = 4)
beta_array_m <- dawid_skene(beta = beta_array)

# Default:
hier_dawid_skene()

# Changing alpha
hier_dawid_skene(alpha = c(2, 2))

# Default:
class_conditional_dawid_skene()

# Not default:
class_conditional_dawid_skene(
  alpha = c(2, 2),
  beta_1 = c(4, 4),
  beta_2 = c(2, 2)
)

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