tirt (version 0.1.3)

sim_trt: Simulate Testlet Response Theory Data (Vector Supported Version)

Description

Simulate testlet responses data. Support both dichotomous and polytomous responses. Provide an easy implementation with a few default settings.

Usage

sim_trt(
  n_people = 1000,
  item_structure = list(),
  theta = NULL,
  theta_mean = 0,
  theta_sd = 1
)

Value

A list containing:

resp

data.frame of responses (rows=people, cols=items)

true_item_params

data.frame of true item parameters

true_person_params

vector of true latent traits

Arguments

n_people

Integer. Number of examinees.

item_structure

List of lists defining item blocks.

theta

Numeric vector (Optional). If provided, these exact ability values are used.

theta_mean

Numeric. Mean of latent trait (used if theta is NULL).

theta_sd

Numeric. SD of latent trait (used if theta is NULL).

Examples

  # =========================================================================
  # Example 1: Complex Testlet Design
  # =========================================================================
  # Define the Testlet Blueprint
  trt_design <- list(
    # Testlet 1: Rasch Testlet Model (High dependence: var=0.8)
    list(model = "RaschT", n_items = 5, testlet_id = "Read_A",
         testlet_var = 0.8, b = c(-1, 1)),

    # Testlet 2: 2PL Testlet Model (Default dependence: var=0.5)
    list(model = "2PLT", n_items = 5, testlet_id = "Read_B",
         a = c(0.7, 1.3)),

    # Testlet 3: Graded Response Testlet (Polytomous, 4 categories)
    list(model = "GRT", n_items = 4, testlet_id = "Survey",
         categories = 4, testlet_var = 0.2)
  )

  # Run Simulation
  trt_data <- sim_trt(n_people = 500, item_structure = trt_design)

  # Inspect Results
  # 1. Responses
  head(trt_data$resp)

  # 2. Item Parameters
  # (Notice 'testlet_loading' equals 'discrimination' for standard models)
  head(trt_data$true_item_params)

  # 3. Person Parameters (Ability + Gamma for each testlet)
  head(trt_data$true_person_params)

  # =========================================================================
  # Example 2: Manual Control (Theta, Gamma, and Parameters)
  # =========================================================================

  # 1. Manual Theta (e.g., everyone has high ability)
  manual_theta <- rep(2.0, 100)

  # 2. Manual Gamma (e.g., zero effect for T1)
  manual_gamma <- rep(0, 100)

  # 3. Item Parameters: Exact Match vs Range Sampling
  custom_structure <- list(
    # Case A: Manual Gamma Vector
    list(model = "2PLT", n_items = 5, testlet_id = "T1",
         gamma_vector = manual_gamma),

    # Case B: Exact Parameter Match (Length of 'a' equals n_items)
    list(model = "2PLT", n_items = 2, testlet_id = "T2",
         a = c(0.5, 2.5)),

    # Case C: Range Sampling (Length of 'a' is 2, but n_items != 2)
    list(model = "2PLT", n_items = 5, testlet_id = "T3",
         a = c(0.5, 2.5))
  )

  res_custom <- sim_trt(n_people = 100, theta = manual_theta,
                        item_structure = custom_structure)

  # Verify Manual Theta
  print(mean(res_custom$true_person_params$ability)) # Should be 2.0

  # Verify Manual Gamma (T1 should be 0)
  print(head(res_custom$true_person_params$testlet_T1))

  # Verify Exact Match (T2 discrimination should be 0.5 and 2.5)
  print(res_custom$true_item_params[res_custom$true_item_params$testlet_id == "T2",
                                    "discrimination"])