Suggest cluster4d parameters based on data characteristics

Provides parameter recommendations based on data size and user priorities. Experimental methods are reported separately and are never returned as the recommended method.

Usage

suggest_cluster4d_params(
  n_voxels,
  n_timepoints,
  priority = c("balanced", "speed", "quality", "memory")
)

Arguments

n_voxels

Number of voxels in mask

n_timepoints

Number of time points

priority

What to optimize for: "speed", "quality", "memory", or "balanced"

Value

A list with suggested parameters for supported methods plus experimental_methods and an experimental_note. Experimental methods are not recommended or assigned suggested parameter sets.

Examples

# Get parameter suggestions for a typical fMRI dataset
params <- suggest_cluster4d_params(50000, 200, priority = "balanced")
print(params$recommended_method)
#> [1] "flash3d"
print(params$n_clusters)
#> [1] 200

# Speed-optimized parameters for large dataset
speed_params <- suggest_cluster4d_params(100000, 300, priority = "speed")
print(speed_params$recommended_method)
#> [1] "flash3d"

# Quality-optimized parameters for smaller dataset
quality_params <- suggest_cluster4d_params(10000, 150, priority = "quality")
print(quality_params$n_clusters)
#> [1] 48

# Memory-efficient parameters
memory_params <- suggest_cluster4d_params(200000, 100, priority = "memory")
print(memory_params$recommended_method)
#> [1] "snic"