Network-based item selection with configurable edge weights.

Extends select_max_dist() with a flexible edge weight calculation.

Usage

select_max_dist_enhanced(
  pers,
  item,
  R,
  admin,
  adj_mat = NULL,
  n_candidates = 1,
  edge_weight_fun = edge_weight_inverse,
  edge_weight_args = list()
)

Arguments

pers: A data frame of current respondent ability estimates.
item: A data frame of current item parameter estimates.
R: A respondent-by-item matrix of potential responses.
admin: An integer administration matrix; 0 indicates an item has not been administered to a respondent. See meow() for details.
adj_mat: An item-item adjacency matrix. See construct_adj_mat().
n_candidates: The number of farthest items to assemble into a candidate pool before selecting the next item by maximum information. Allows users to trade off network density against estimation efficiency.
edge_weight_fun: A function that computes edge weights from the adjacency matrix. See edge_weight_inverse().
edge_weight_args: A named list of additional arguments for edge_weight_fun.

Value

An updated administration matrix with the selected item marked for each respondent.

Examples

sim <- meow(select_max_dist_enhanced, update_theta_mle, data_simple_1pl,
            data_args = list(N_persons = 10, N_items = 10), fix = "item",
            select_args = list(edge_weight_fun = edge_weight_power))
nrow(sim$results)
#> [1] 6