EM_templateICA | R Documentation |
EM Algorithms for Template ICA Models
EM_templateICA.spatial(
template_mean,
template_var,
meshes,
BOLD,
theta0,
C_diag,
H,
Hinv,
maxiter = 100,
usePar = FALSE,
epsilon = 0.001,
reduce_dim = TRUE,
verbose = FALSE
)
EM_templateICA.independent(
template_mean,
template_var,
BOLD,
theta0,
C_diag,
H,
Hinv,
maxiter = 100,
epsilon = 0.001,
reduce_dim = FALSE,
usePar = FALSE,
verbose
)
template_mean |
( |
template_var |
( |
meshes |
|
BOLD |
( |
theta0 |
(list) initial guess at parameter values: A ( |
C_diag |
( |
H , Hinv |
For dimension reduction
of the spatial template ICA model, which assumes that all IC's have the
same smoothness parameter, |
maxiter |
Maximum number of EM iterations. Default: 100. |
usePar |
Parallelize the computation? Default: |
epsilon |
Smallest proportion change between iterations. Default: 0.001. |
reduce_dim |
Reduce the temporal dimension of the data using PCA?
Default: |
verbose |
If |
EM_templateICA.spatial
implements the expectation-maximization
(EM) algorithm described in Mejia et al. (2019+) for estimating the
subject-level ICs and unknown parameters in the template ICA model with
spatial priors on subject effects.
In both models, if original fMRI timeseries has covariance
\sigma^2 I_T
, the prewhitened timeseries achieved by premultiplying
by (QxT
) matrix H
from PCA has diagonal covariance
\sigma^2HH'
, so C_diag is diag(HH')
.
A list: theta (list of final parameter estimates), subICmean
(estimates of subject-level ICs), subICvar (variance of subject-level ICs,
for non-spatial model) or subjICcov (covariance matrix of subject-level ICs,
for spatial model – note that only diagonal and values for neighbors are
computed), and success (flag indicating convergence (TRUE
) or not
(FALSE
))
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