mirtjml_expr: Constrained joint maximum likelihood estimation for...

In mirtjml: Joint Maximum Likelihood Estimation for High-Dimensional Item Factor Analysis

Description

Constrained joint maximum likelihood estimation for exploratory item factor analysis on the multidimensional two parameter logistic model.

Usage

mirtjml_expr(
  response,
  K,
  theta0 = NULL,
  A0 = NULL,
  d0 = NULL,
  cc = NULL,
  tol = 5,
  print_proc = TRUE
)

Arguments

response	N by J matrix containing 0/1/NA responses, where N is the number of respondents, J is the number of items, and NA indicates a missing response.
K	The number of factors in exploratory item factor analysis.
theta0	N by K matrix, the initial value of latent factor scores for each respondent.
A0	J by K matrix, the initial value of loading matrix.
d0	Length J vector, the initial value of intercept parameters.
cc	A constant constraining the magnitude of the norms of person and item parameter vectors.
tol	The tolerance for convergence with a default value 5.
print_proc	Print the precision during the estimation procedure with a default value TRUE.

Value

The function returns a list with the following components:

theta_hat

The estimated person parameter matrix.

A_hat

The estimated loading parameter matrix

d_hat

The estimated intercept parameters.

References

Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. <doi:10.1007/s11336-018-9646-5>;

Examples

# load a simulated dataset
attach(data_sim)

# use all available cores by running
# setMIRTthreads(-1)

# run the exploratory analysis
res <- mirtjml_expr(response, K)

mirtjml documentation built on July 1, 2020, 6:05 p.m.