Performs modeling procedure for a Probit Hierarchical Level Model.

probitHLM(
  unique_subject_ids,
  subject_ids,
  choices_nk,
  fixed_effects_design,
  rv_effects_design,
  B_elem_plus1,
  gamma,
  beta,
  theta,
  zeta_rv,
  WtW,
  Z_c,
  Wzeta_0,
  inv_Sigma_gamma,
  mu_gamma,
  Sigma_zeta_inv,
  S0,
  mu_beta,
  sigma_beta_inv
)

Arguments

unique_subject_ids

A vector with length N x 1 containing unique subject IDs.

subject_ids

A vector with length N*K x 1 containing subject IDs.

choices_nk

A vector with length N*K x 1 containing subject choices.

fixed_effects_design

A matrix with dimensions N*K x P containing fixed effect variables.

rv_effects_design

A matrix with dimensions N*K x V containing random effect variables.

B_elem_plus1

A V[[1]] dimensional column vector indicating which zeta_i relate to theta_i.

gamma

A vector with dimensions P_1 x 1 containing fixed parameter estimates.

beta

A vector with dimensions P_2 x 1 containing random parameter estimates.

theta

A vector with dimensions N x 1 containing subject understanding estimates.

zeta_rv

A matrix with dimensions N x V containing random parameter estimates.

WtW

A field<matrix> P x P x N contains the caching for direct sum.

Z_c

A vector with dimensions N*K x 1

Wzeta_0

A vector with dimensions N*K x 1

inv_Sigma_gamma

A matrix with dimensions P x P that is the prior inverse sigma matrix for gamma.

mu_gamma

A vector with length P x 1 that is the prior mean vector for gamma.

Sigma_zeta_inv

A matrix with dimensions V x V that is the prior inverse sigma matrix for zeta.

S0

A matrix with dimensions V x V that is the prior sigma matrix for zeta.

mu_beta

A vector with dimensions P_2 x 1, that is the mean of beta.

sigma_beta_inv

A matrix with dimensions P_2 x P_2, that is the inverse sigma matrix of beta.

Value

A list that contains:

zeta_1

A vector of length N

sigma_zeta_inv_1

A matrix of dimensions V x V

gamma_1

A vector of length P

beta_1

A vector of length V

B

A matrix of length V

Details

The function is implemented to decrease the amount of vectorizations necessary.

Author

Steven Andrew Culpepper and James Joseph Balamuta