A tool for the \(\beta\) Method — parallel

This function performs a single iteration of the \(\beta\) method for A item's validation. It is designed to be used in parallel computing environments to speed up the validation process of the \(\beta\) method. The function is a utility function for validation, and it should not be called independently by the user.

parallel_iter(
  i,
  Y,
  criter.index,
  P.alpha.Xi,
  P.alpha,
  pattern,
  ri,
  Ni,
  Q.pattern.ini,
  model,
  criter,
  search.method,
  P_GDINA,
  Q.beta,
  L,
  K,
  alpha.P,
  get.MLRlasso,
  priority
)

Arguments

i: An integer indicating the item number that needs to be validated.
Y: A matrix of observed data used for validation.
criter.index: An integer representing the index of the criterion.
P.alpha.Xi: A matrix representing individual posterior values.
P.alpha: A vector of attribute prior weights.
pattern: A matrix representing the attribute mastery patterns.
ri: A vector containing the number of examinees in each knowledge state who correctly answered item \(i\).
Ni: A vector containing the total number of examinees in each knowledge state.
Q.pattern.ini: An integer representing the initial pattern number for the model.
model: A model object used for fitting, such as the GDINA model.
criter: A character string specifying the fit criterion. Possible values are "AIC", "BIC", "CAIC", or "SABIC".
search.method: A character string specifying the search method for model selection. Options include "beta", "ESA", "SSA", or "PAA".
P_GDINA: A function that calculates probabilities for the GDINA model.
Q.beta: A Q-matrix used for validation.
L: An integer representing the number of latent patterns.
K: An integer representing the number of attributes.
alpha.P: A matrix of individuals' marginal mastery probabilities (Tu et al., 2022).
get.MLRlasso: A function for Lasso regression with multiple linear regression.
priority: A vector of priorities used in the PAA method search.

Value

A list containing the following components:

fit.index.pre: The previous fit index value after applying the selected search method.
fit.index.cur: The current fit index value after applying the selected search method.
Q.pattern.cur: The pattern that corresponds to the optimal model configuration for the current iteration.
priority: The priority vector used in the PAA method, if applicable.