b2_algorithm: Main algorithms implemented in fclust

In MixfMRI: Mixture fMRI Clustering Analysis

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 PARAM, usually returned by set.global(), initial.em.gbd(), and initial.RndEM.gbd().

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)

}

MixfMRI documentation built on Sept. 8, 2023, 5:06 p.m.