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
)A list that contains:
zeta_1A vector of length N
sigma_zeta_inv_1A matrix of dimensions V x V
gamma_1A vector of length P
beta_1A vector of length V
BA matrix of length V
A vector with length N x 1 containing
unique subject IDs.
A vector with length N*K x 1 containing
subject IDs.
A vector with length N*K x 1 containing
subject choices.
A matrix with dimensions N*K x P containing
fixed effect variables.
A matrix with dimensions N*K x V containing
random effect variables.
A V[[1]] dimensional column vector
indicating which zeta_i relate to theta_i.
A vector with dimensions P_1 x 1 containing
fixed parameter estimates.
A vector with dimensions P_2 x 1 containing
random parameter estimates.
A vector with dimensions N x 1 containing
subject understanding estimates.
A matrix with dimensions N x V containing
random parameter estimates.
A field<matrix> P x P x N contains the
caching for direct sum.
A vector with dimensions N*K x 1
A vector with dimensions N*K x 1
A matrix with dimensions P x P that is the
prior inverse sigma matrix for gamma.
A vector with length P x 1 that is the prior
mean vector for gamma.
A matrix with dimensions V x V that is the
prior inverse sigma matrix for zeta.
A matrix with dimensions V x V that is the
prior sigma matrix for zeta.
A vector with dimensions P_2 x 1, that is
the mean of beta.
A matrix with dimensions P_2 x P_2, that is
the inverse sigma matrix of beta.
Steven Andrew Culpepper and James Joseph Balamuta
The function is implemented to decrease the amount of vectorizations necessary.
rwishart() and TwoPLChoicemcmc()