## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width  = 7,
  fig.height = 4.5
)

## ----load---------------------------------------------------------------------
library(irtsim)
library(ggplot2)

## ----design-byhand------------------------------------------------------------
design_byhand <- irt_design(
  model     = "2PL",
  n_items   = 10,
  item_params = list(
    a = c(0.8, 1.0, 1.1, 1.2, 1.3, 0.9, 1.4, 1.0, 1.2, 1.1),
    b = seq(-2, 2, length.out = 10)
  )
)
design_byhand

## ----design-helper------------------------------------------------------------
set.seed(2026)
ip <- irt_params_2pl(
  n_items = 10,
  a_mean  = 0,    a_sd  = 0.25,   # log-normal: median a = 1
  b_mean  = 0,    b_sd  = 1,
  b_range = c(-2, 2)
)
design_helper <- irt_design(
  model       = "2PL",
  n_items     = 10,
  item_params = ip
)

## ----design-priorfit----------------------------------------------------------
prior_data <- mirt::expand.table(mirt::LSAT7)
prior_fit  <- mirt::mirt(prior_data, 1, "2PL", verbose = FALSE)
co <- mirt::coef(prior_fit, IRTpars = TRUE, simplify = TRUE)$items

design_prior <- irt_design(
  model       = "2PL",
  n_items     = nrow(co),
  item_params = list(a = co[, "a"], b = co[, "b"])
)
co

## ----study--------------------------------------------------------------------
study <- irt_study(
  design       = design_helper,
  sample_sizes = c(100, 250, 500, 1000)
)
study

## ----simulate, cache = FALSE--------------------------------------------------
results <- irt_simulate(
  study      = study,
  iterations = 50,
  seed       = 1,
  progress   = FALSE
)

## ----summary------------------------------------------------------------------
res_summary <- summary(results,
                       criterion = c("mse", "bias", "rmse", "coverage"))
head(res_summary$item_summary)

## ----plot, fig.alt = "MSE of difficulty estimates by sample size, one line per item"----
plot(res_summary,
     criterion = "mse",
     param     = "b",
     threshold = 0.05)

## ----recommend----------------------------------------------------------------
n_rec <- recommended_n(res_summary,
                       criterion = "mse",
                       threshold = 0.05,
                       param     = "b")
n_rec

## ----recommend-details--------------------------------------------------------
attr(n_rec, "details")