hm_dcm_data_gen: Artificial data generating function for the hidden-Markov DCM...

In variationalDCM: Variational Bayesian Estimation for Diagnostic Classification Models

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
)

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)

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

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. \Sexpr[results=rd]{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)

variationalDCM documentation built on May 29, 2024, 6:45 a.m.