variationalDCM (version 2.0.1)

hm_dcm_data_gen: Artificial data generating function for the hidden-Markov DCM based on the given Q-matrix

Description

hm_dcm_data_gen() returns the artificially generated item response data for the HM-DCM

Usage

hm_dcm_data_gen(
  I = 500,
  Q,
  min_theta = 0.2,
  max_theta = 0.8,
  att_cor = 0.1,
  seed = 17
)

Value

A list including:

X

the generated artificial item response data

alpha_true

the generated true vale of the attribute mastery pattern, matrix form

alpha_patt_true

the generated true vale of the attribute mastery pattern, string form

Arguments

I

the number of assumed respondents

Q

the \(J \times K\) binary matrix

min_theta

the minimum value of the item parameter \(\theta_{jht}\)

max_theta

the maximum value of the item parameter \(\theta_{jht}\)

att_cor

the true value of the correlation among attributes (default: 0.1)

seed

the seed value used for random number generation (default: 17)

References

Yamaguchi, K., & Martinez, A. J. (2024). Variational Bayes inference for hidden Markov diagnostic classification models. British Journal of Mathematical and Statistical Psychology, 77(1), 55–79. tools:::Rd_expr_doi("10.1111/bmsp.12308")

Examples

indT = 3
Q = sim_Q_J30K3
hm_sim_Q = lapply(1:indT,function(time_point) Q)
hm_sim_data = hm_dcm_data_gen(Q=hm_sim_Q,I=200)