Skip to contents

Simulate a continuously administered item bank with known drift

Source: R/simulate.R
dw_simulate.Rd

Each window, every item is answered by a Poisson number of examinees whose abilities are known from operational scoring (the population mean may trend over time; this is not drift). Items are stable, drift gradually (linear from an onset window) or jump abruptly at an onset window.

Usage

dw_simulate(
  n_items = 300,
  n_windows = 40,
  mean_n = 80,
  p_gradual = 0.1,
  p_abrupt = 0.05,
  slope_range = c(0.02, 0.06),
  jump_range = c(0.4, 1),
  onset_range = c(5, 30),
  theta_trend = 0.01,
  ref_se = 0.05,
  seed = NULL
)

Arguments

n_items, n_windows

Bank size and number of windows.

mean_n

Mean responses per item per window.

p_gradual, p_abrupt

Share of items with each drift type.

slope_range

Absolute gradual slope per window (logits).

jump_range

Absolute abrupt jump (logits).

onset_range

Windows in which drift can begin.

theta_trend

Change in examinee mean ability per window.

ref_se

Standard error of the banked (reference) difficulties.

seed

Optional seed.

Value

A `dw_sim`: `$responses` (`window`, `item`, `theta`, `x`), `$bank` (`item`, `b`, `se`), `$truth` (`item`, `type`, `onset`, `size`, `b_true_final`) and `$b_path` (items x windows matrix of true difficulty).

Examples

sim <- dw_simulate(n_items = 60, n_windows = 20, mean_n = 60,
                   onset_range = c(5, 12), seed = 1)
table(sim$truth$type)
#> 
#>  abrupt gradual  stable 
#>       2       7      51