generate_data: Simulate Q-sort data

In bayesqm: Bayesian Q Methodology: Probabilistic Factor Analysis

Simulate Q-sort data

Description

generate_data() is the top-level data-generating function used by the package's simulation studies and tests. It builds a loading matrix (generate_loadings()), factor scores, noise of the chosen type (generate_noise()), and discretises the continuous signal onto a forced Q-sort grid (discretize_to_grid()). See also get_distribution() for the standard forced-distribution lookup.

Usage

generate_data(
  N,
  J,
  K,
  noise_sd = 1,
  error_type = "normal",
  nu = 5,
  contam_prop = 0.1,
  contam_scale = 4,
  loading_type = "simple",
  primary_range = c(0.55, 0.85),
  cross_range = c(-0.15, 0.15),
  seed = NULL
)

generate_loadings(
  N,
  K,
  primary_range = c(0.55, 0.85),
  cross_range = c(-0.15, 0.15),
  type = "simple"
)

generate_noise(
  J,
  N,
  type = "normal",
  sd = 1,
  nu = 5,
  contam_prop = 0.1,
  contam_scale = 4
)

discretize_to_grid(Y_cont, distr)

get_distribution(J)

Arguments

N, J, K	Numbers of participants, statements, and factors.
noise_sd	Residual SD.
error_type	One of "normal", "t", "contaminated".
nu	Degrees of freedom for error_type = "t".
contam_prop, contam_scale	Contamination rate and scale.
loading_type	"simple" or "complex".
primary_range, cross_range	Uniform ranges for primary and cross-loadings.
seed	Optional RNG seed; restored on exit.
type	For generate_noise(), one of "normal", "t", "contaminated". For generate_loadings(), "simple" or "complex".
sd	Residual SD for generate_noise().
Y_cont	Continuous scores (for discretize_to_grid()).
distr	Integer forced-distribution counts.

Value

generate_data() returns a list with Y, Lambda_true, F_true, distribution, N, J, K. The component helpers return their respective raw objects.

bayesqm documentation built on June 18, 2026, 1:07 a.m.