Mstep_simple: 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
)

Value

a "list" of estimates obtained from the maximization step using univariate Newton-Raphson

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