UpdateTheta_templateICA {templateICAr}

Parameter Estimates in EM Algorithm for Template ICA Model

Description

Parameter Estimates in EM Algorithm for Template ICA Model

Usage

UpdateTheta_templateICA.spatial(
  template_mean,
  template_var,
  meshes,
  BOLD,
  theta,
  C_diag,
  s0_vec,
  D,
  Dinv_s0,
  verbose = FALSE,
  return_MAP = FALSE,
  update = c("all", "kappa", "A")
)

UpdateTheta_templateICA.independent(
  template_mean,
  template_var,
  BOLD,
  theta,
  C_diag,
  verbose
)

Arguments

template_mean

(V \times Q matrix) mean maps for each IC in template

template_var

(V \times Q matrix) between-subject variance maps for each IC in template

meshes

NULL for spatial independence model, otherwise a list of objects of class "templateICA_mesh" containing the triangular mesh (see make_mesh) for each brain structure.

BOLD

(V \times Q matrix) dimension-reduced fMRI data

theta

(list) current parameter estimates

C_diag

(Qx1) diagonal elements of residual covariance after dimension reduction

s0_vec

Vectorized template means

D

Sparse diagonal matrix of template standard deviations

Dinv_s0

The inverse of D times s0_vec

verbose

If TRUE, display progress of algorithm. Default: FALSE.

return_MAP

If TRUE. return the posterior mean and precision of the latent fields instead of the parameter estimates. Default: FALSE.

update

Which parameters to update. Either "all", "A" or "kappa".

Value

An updated list of parameter estimates, theta, OR if return_MAP=TRUE, the posterior mean and precision of the latent fields