em_estimation: 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
)

Value

a "list" of matrices with unprocessed model estimates

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.