Mstep_simple: Maximization step.
In regDIF: Regularized Differential Item Functioning

Mstep_simple

R Documentation

Maximization step.

Description

Maximization step.

Usage

Mstep_simple(
  p,
  item_data,
  pred_data,
  prox_data,
  mean_predictors,
  var_predictors,
  eout,
  item_type,
  pen_type,
  tau_current,
  pen,
  pen.deriv,
  alpha,
  gamma,
  anchor,
  final_control,
  samp_size,
  num_responses,
  num_items,
  num_quad,
  num_predictors,
  num_tau,
  max_tau,
  optim_method
)

Arguments

`p`	List of parameters.
`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.
`eout`	E-step output, including matrix for item and impact equations, in addition to theta values (possibly adaptive).
`item_type`	Optional character value or vector indicating the type of item to be modeled.
`pen_type`	Character value indicating the penalty function to use.
`tau_current`	A single numeric value of tau that exists within `tau_vec`.
`pen`	Current penalty index.
`pen.deriv`	Logical value indicating whether to use the second derivative of the penalized parameter during regularization. The default is TRUE.
`alpha`	Numeric value indicating the alpha parameter in the elastic net penalty function.
`gamma`	Numeric value indicating the gamma parameter in the MCP function.
`anchor`	Optional numeric value or vector indicating which item response(s) are anchors (e.g., `anchor = 1`).
`final_control`	Control parameters.
`samp_size`	Sample size in data set.
`num_responses`	Number of responses for each item.
`num_items`	Number of items in data set.
`num_quad`	Number of quadrature points used for approximating the latent variable.
`num_predictors`	Number of predictors.
`num_tau`	Logical indicating whether the minimum tau value needs to be identified during the regDIF procedure.
`max_tau`	Logical indicating whether to output the minimum tau value needed to remove all DIF from the model.
`optim_method`	Character value of the type of estimation method to use