Conducts a full CAT simulation.

Conducts a full CAT simulation.

Usage

cat_simulation(
  select_fun,
  update_fun,
  data_loader,
  init = NULL,
  fix = "none",
  ...
)

Arguments

select_fun

A function that specifies the item selection algorithm.

update_fun

A function that specifies the parameter update algorithm.

data_loader

A function that specifies the data generating process.

init

A list of intialization values for estimated person and item parameters. Currently accepts a named list with two entities: theta and diff, for initial estimated values of ability and difficulty, respectively. Defaults to NULL, which initializes all estimated parameters to zero.

fix

Which estimated parameters to treat as fixed. Currently defaults to none, but accepts theta, diff, or both.`

...

Named arguments to be passed to data_loader, select_fun, or update_fun

Value

A list of four named entities, results is a dataframe with one row per iteration of the simulation. It contains three general columns, iter for the iteration number, a RMSE pooled across person abilities named rmse_theta, and the RMSE pooled across item difficulties named rmse_diff. Additionally there is one column per person and item, one for the associated estimated parameter (ability or difficulty) and one for the bias in that estimate. Next is a list of item-item adjacency matrices, contained in adj_mats. One matrix is provided per iteration of the simulation, and edge weights are the number of respondents who have responded to each pair of items. Finally, true ability and difficulty vectors are returned in theta_tru and diff_tru.