em_estimation: Penalized expectation-maximization algorithm.
In regDIF: Regularized Differential Item Functioning

em_estimation

R Documentation

Penalized expectation-maximization algorithm.

Description

Penalized expectation-maximization algorithm.

Usage

em_estimation(
  p,
  item_data,
  pred_data,
  prox_data,
  mean_predictors,
  var_predictors,
  item_type,
  theta,
  pen_type,
  tau_vec,
  id_tau,
  num_tau,
  alpha,
  gamma,
  pen,
  pen.deriv,
  anchor,
  final_control,
  samp_size,
  num_items,
  num_responses,
  num_predictors,
  num_quad,
  adapt_quad,
  optim_method,
  estimator_history,
  estimator_limit,
  NA_cases,
  exit_code
)

Arguments

`p`	List of parameters with starting values obtained from preprocess.
`item_data`	Matrix or data frame of item responses.
`pred_data`	Matrix or data frame of DIF and/or impact predictors.
`prox_data`	Vector of observed proxy scores.
`mean_predictors`	Possibly different matrix of predictors for the mean impact equation.
`var_predictors`	Possibly different matrix of predictors for the variance impact equation.
`item_type`	Character value or vector indicating the type of item to be modeled.
`theta`	Vector of fixed quadrature points.
`pen_type`	Character value indicating the penalty function to use.
`tau_vec`	Vector of tau values that either are automatically generated or provided by the user. The first `tau_vec` will be equal to `Inf` to identify a minimal value of tau in which all DIF is removed from the model.
`id_tau`	Logical indicating whether to identify the minimum value of tau in which all DIF parameters are removed from the model.
`num_tau`	Numeric value indicating the number of tau values to run regDIF on.
`alpha`	Numeric value indicating the alpha parameter in the elastic net penalty function.
`gamma`	Numeric value indicating the gamma parameter in the MCP function.
`pen`	Index for the tau vector.
`pen.deriv`	Logical value indicating whether to use the second derivative of the penalized parameter during regularization. The default is TRUE.
`anchor`	Optional numeric value or vector indicating which item response(s) are anchors (e.g., `anchor = 1`).
`final_control`	Control parameters.
`samp_size`	Numeric value indicating the sample size.
`num_items`	Numeric value indicating the number of items.
`num_responses`	Vector with number of responses for each item.
`num_predictors`	Numeric value indicating the number of predictors.
`num_quad`	Numeric value indicating the number of quadrature points.
`adapt_quad`	Logical value indicating whether to use adaptive quad. needs to be identified.
`optim_method`	Character value indicating the type of optimization method to use.
`estimator_history`	List to save EM iterations for supplemental EM algorithm.
`estimator_limit`	Logical value indicating whether the EM algorithm reached the maxit limit in the previous estimation round.
`NA_cases`	Logical vector indicating if observation is missing.
`exit_code`	Integer indicating if the model has converged properly.