UpdateTheta_templateICA: Parameter Estimates in EM Algorithm for Template ICA Model

In templateICAr: Estimate Brain Networks and Connectivity with ICA and Empirical Priors

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

templateICAr documentation built on Feb. 16, 2023, 8:14 p.m.