e1_simulation: Generate datasets for MixfMRI simulations

In MixfMRI: Mixture fMRI Clustering Analysis

Generate datasets for MixfMRI simulations

Description

Generate datasets for MixfMRI simulations

Usage

  gendataset(phantom, overlap, smooth = FALSE)

Arguments

phantom	a phantom dataset.
overlap	a desired overlap level.
smooth	if gcv.smooth2d() be applied to the data.

Details

This is a function to generate simulated fMRI data based on the input phantom and the desired overlap level for the fMRI p-value.

Value

Return a list contains eta for mixing proportion, overlap for the desired level, mu for center of p-values, class.id for the true classifications where p-values belong to, tval for the testing statistics, and pval for the p-values of interesting in simulations.

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().

Examples

.rem <- function(){

  library(MixfMRI, quietly = TRUE)
  set.seed(1234)
  da <- gendataset(phantom = shepp1fMRI, overlap = 0.01)$pval
  da2 <- gendataset(phantom = shepp2fMRI, overlap = 0.01)$pval

  par(mfrow = c(2, 2), mar = rep(0.05, 4))
  image(shepp1fMRI[50:210, 50:210], axes = FALSE)
  image(shepp2fMRI[50:210, 50:210], axes = FALSE)
  image(da[50:210, 50:210], axes = FALSE)
  image(da2[50:210, 50:210], axes = FALSE)

}

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