Description Usage Arguments Details Value Author(s) References See Also Examples
Main MixfMRI functions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  fclust(X.gbd, PV.gbd, K = 2,
PARAM.init = NULL,
min.1st.prop = .FC.CT$INIT$min.1st.prop,
max.PV = .FC.CT$INIT$max.PV,
class.method = .FC.CT$INIT$class.method[1],
RndEM.iter = .FC.CT$CONTROL$RndEM.iter,
algorithm = .FC.CT$algorithm[1],
model.X = .FC.CT$model.X[1],
ignore.X = .FC.CT$ignore.X,
stop.unstable = TRUE,
MPI.gbd = .FC.CT$MPI.gbd, common.gbd = .FC.CT$common.gbd)
set.global(X.gbd, PV.gbd, K = 2,
min.1st.prop = .FC.CT$INIT$min.1st.prop,
max.PV = .FC.CT$INIT$max.PV,
class.method = .FC.CT$INIT$class.method[1],
RndEM.iter = .FC.CT$CONTROL$RndEM.iter,
algorithm = .FC.CT$algorithm[1],
model.X = .FC.CT$model.X[1],
ignore.X = .FC.CT$ignore.X,
check.X.unit = .FC.CT$check.X.unit,
MPI.gbd = .FC.CT$MPI.gbd, common.gbd = .FC.CT$common.gbd)

X.gbd 
a data matrix of 
PV.gbd 
a pvalue vector of signals associated with voxels.

K 
number of clusters to be estimated. 
PARAM.init 
initial parameters. 
min.1st.prop 
lower bound of mixing proportion (ETA) of the 1st cluster (uniform). 
max.PV 
upper bound of pvalues where initializations pick from. 
class.method 
classification method for initializations. 
RndEM.iter 
number of RndEM iterations. 
algorithm 
either “ecm” (ECM), “apecma” (APECMa) or “em” (EM) algorithm. 
model.X 
either “I” or “V” for covariance matrix. 
ignore.X 
if 
check.X.unit 
if 
stop.unstable 
if 
MPI.gbd 
if MPI (“EGM” algorithm) is used. 
common.gbd 
if 
The fclust()
contains initialization and EM algorithms for clustering
fMRI signal data which have two parts: X.gbd
for voxel information
either 2D or 3D, PV.gbd
for pvalue of signals associated with
voxels. Each signal is assumed as a mixture distribution with K
components with mixing proportion ETA
, and each component has
two independent coordinates with density functions: Beta and multivariate
Normal distributions.
Beta density:
The 1st component is restricted by min.1st.prop
and Beta(1, 1)
distribution. The other K  1
components have Beta(alpha, beta)
distribution with alpha < 1 < beta
.
Multivariate Normal density:
model.X = "I"
is for identity cov matrix of multivariate Normal
distribution, and "V"
for unstructured cov matrix.
ignore.X = TRUE
is to ignore X.gbd
and normal density,
i.e. only Beta density is used.
Currently, APECMa and EM algorithms are implemented with EGM algorithm to speed up convergence if MPI is available. RndEM initialization is also implemented for better chance of good initial values for convergence.
The set.global()
has purposes: create a template/storage of
parameters, save configurations, and called by fclust()
to initial
the parameters, such as initial.em.gbd()
or
initial.RndEM.gbd()
.
A list with class fclust
by fclust()
is returned
which can be summarized by print.fclust()
.
A list PARAM
or PARAM.org
is returned by set.global()
:
N.gbd 
number of observations (within the rank), and should be
equal to 
N.all 
numbers of observations (of all ranks
if 
N 
total number of observations ( 
p 
dimension of an observation (3 for 2D signals, 4 for 3D signals), equivalent to total number of coordinates. 
p.X 
dimension of 
K 
number of clusters. 
ETA 
mixing proportion, length 
log.ETA 

BETA 
a list of length 
MU 
a matrix of dimension 
SIGMA 
a list of length 
logL 
log likelihood value. 
min.1st.prop 
carried from input. 
max.PV 
carried from input. 
class.method 
classification method of initializations. 
min.N.CLASS 

model.X 
carried from input. 
WeiChen Chen and Ranjan Maitra.
http://maitra.public.iastate.edu/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
# .FC.CT$algorithm < "em"
# .FC.CT$model.X < "V"
# .FC.CT$ignore.X < TRUE
.FC.CT$check.X.unit < FALSE
set.seed(1234)
### Test toy1.
X.gbd < toy1$X.gbd[, 3]
PV.gbd < toy1$PV.gbd
PARAM < fclust(X.gbd, PV.gbd, K = 2)
print(PARAM)
id.toy1 < .MixfMRIEnv$CLASS.gbd
print(RRand(toy1$CLASS.gbd, id.toy1))
### Test toy2.
X.gbd < toy2$X.gbd[, 3]
PV.gbd < toy2$PV.gbd
PARAM < fclust(X.gbd, PV.gbd, K = 3)
print(PARAM)
id.toy2 < .MixfMRIEnv$CLASS.gbd
print(RRand(toy2$CLASS.gbd, id.toy2))

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