IW-GVEM Algorithm for Confirmatory M2PL Analysis

Usage

C2PL_iw2(
  data,
  model = matrix(1, ncol(data)),
  criterion = "BIC",
  iter = 200,
  eps = 0.001,
  c = 1,
  S = 10,
  M = 10,
  lr = 0.1,
  SE.level = NULL
)

Arguments

data: An $N\times J$ binary matrix of item responses (missing responses should be coded as NA)
model: A $J\times K$ binary matrix of loading indicators (all items load on the only dimension by default)
criterion: Information criterion for model selection, one of 'GIC' (recommended), 'BIC', or 'AIC'
iter: Maximum number of iterations
eps: Termination criterion on numerical accuracy
c: Constant for computing GIC
S: Sample size for approximating the expected lower bound
M: Sample size for approximating a tighter lower bound
lr: Learning rate for the Adam optimizer
SE.level: Accuracy level of Gaussian quadrature for mvQuad to compute standard errors (SEs are not computed if SE.level is NULL)

Value

An object of class vemirt_DIF, which is a list containing the following elements:

N: Number of respondents
niter0: Number(s) of iterations for initialization
fit: The only element of all
best: Equal to 1
all: A list of model which has one element:
...$niter: Number(s) of iterations
...$SIGMA: Person-level posterior covariance matrices
...$MU: Person-level posterior mean vectors
...$Sigma: Population covariance matrix
...$Mu: Population mean vector
...$a: Slopes
...$b: Intercepts
...$SE.a: Standard errors of a
...$SE.b: Standard errors of b
...$ll: Estimated lower bound of log-likelihood
...$l0: Number of nonzero elements in model
...$AIC: Akaike Information Criterion: -2*ll+l0*2
...$BIC: Bayesian Information Criterion: -2*ll+l0*log(N)
...$GIC: Generalized Information Criterion: -2*ll+c*l0*log(N)*log(log(N))

Author

Weicong Lyu <weiconglyu@um.edu.mo>

Examples

if (FALSE) { # \dontrun{
with(C2PL_data, C2PL_iw2(data, model, SE = TRUE))} # }

IW-GVEM Algorithm for Confirmatory M2PL Analysis

Usage

Arguments

Value

See also

Author

Examples