algorithm {MixfMRI} | R Documentation |
Main algorithms implemented in fclust
Description
Main algorithms implemented in fclust.
Usage
ecm.step.gbd(PARAM.org)
apecma.step.gbd(PARAM.org)
em.step.gbd(PARAM.org)
Arguments
PARAM.org |
an initialized |
Details
These are main algorithms implemented in fclust()
.
Value
Return an optimized PARAM
.
Author(s)
Wei-Chen Chen and Ranjan Maitra.
References
Chen, W.-C. and Maitra, R. (2021) “A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging Studies”, arXiv:2102.03639.
See Also
set.global()
, fclust()
, PARAM
,
PARAM.org
.
Examples
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
### Test toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
PV.gbd <- toy1$PV.gbd
PARAM <- set.global(X.gbd, PV.gbd, K = 2)
PARAM.new <- initial.em.gbd(PARAM)
PARAM.toy1 <- em.step.gbd(PARAM.new)
id.toy1 <- .MixfMRIEnv$CLASS.gbd
print(PARAM.toy1$ETA)
RRand(toy1$CLASS.gbd, id.toy1)
.rem <- function(){
### Test toy2.
set.seed(1234)
X.gbd <- toy2$X.gbd
PV.gbd <- toy2$PV.gbd
PARAM <- set.global(X.gbd, PV.gbd, K = 3)
PARAM.new <- initial.em.gbd(PARAM)
PARAM.toy2 <- em.step.gbd(PARAM.new)
id.toy2 <- .MixfMRIEnv$CLASS.gbd
print(PARAM.toy2$ETA)
RRand(toy2$CLASS.gbd, id.toy2)
}
[Package MixfMRI version 0.1-3 Index]