Functions to set up models and change their prior
parameters for use in rater().
dawid_skene(alpha = NULL, beta = NULL)
hier_dawid_skene(alpha = NULL)
class_conditional_dawid_skene(alpha = NULL, beta_1 = NULL, beta_2 = NULL)prior parameter for pi
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.)
First on diagonal prior probability parameter
Second on diagonal prior probability parameter for theta
a rater model object that can be passed to rater().
# 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()
#> Bayesian Hierarchical Dawid and Skene Model
#>
#> Prior parameters:
#>
#> alpha: default
# Changing alpha
hier_dawid_skene(alpha = c(2, 2))
#> Bayesian Hierarchical Dawid and Skene Model
#>
#> Prior parameters:
#>
#> alpha:
#>
#> [1] 2 2
#>
# Default:
class_conditional_dawid_skene()
#> Bayesian Class conditional Dawid and Skene Model
#>
#> Prior parameters:
#>
#> alpha: default
#> beta_1: default
#> beta_2: default
# Not default:
class_conditional_dawid_skene(
alpha = c(2, 2),
beta_1 = c(4, 4),
beta_2 = c(2, 2)
)
#> Bayesian Class conditional Dawid and Skene Model
#>
#> Prior parameters:
#>
#> alpha:
#>
#> [1] 2 2
#>
#> beta_1:
#>
#> [1] 4 4
#>
#> beta_2:
#>
#> [1] 2 2
#>