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Generate v2 Dynamic Synergy Data for Model Selection Experiments

Source: R/simulate_synergy_export.R
generate_synergy_data.Rd

This function provides a standardized interface for generating v2 dynamic synergy data suitable for model selection experiments. It wraps simulate_dynamic_synergy_data and returns data in a format optimized for parameter sweep experiments with model selection.

Usage

generate_synergy_data(
  N = 30,
  K = 3,
  r = 3,
  M = 8,
  T_each = 200,
  cluster_sep_spatial = 1,
  cluster_sep_temporal = 1,
  cluster_sep_stability = 1,
  freq_range = c(0.5, 3),
  amp_range = c(0.5, 1.5),
  base_level = 0.05,
  rectify_output = TRUE,
  seed
)

Arguments

N

Number of subjects (time series). Default is 30.

K

Number of clusters (subgroups). Default is 3.

r

Number of synergies per cluster. Default is 3.

M

Number of observed channels (muscles). Default is 8.

T_each

Time-series length per subject. Default is 200.

cluster_sep_spatial

Numeric scalar (0-2) controlling spatial structure differentiation between clusters. Default is 1.0.

cluster_sep_temporal

Numeric scalar (0-2) controlling temporal coordination differentiation between clusters. Default is 1.0.

cluster_sep_stability

Numeric scalar (0-2) controlling stability-adaptability differentiation between clusters. Default is 1.0.

freq_range

Numeric vector of length 2 specifying the range of base frequencies (in Hz) for the periodic activation signals. Default is c(0.5, 3.0).

amp_range

Numeric vector of length 2 specifying the range of amplitudes for the periodic activation signals. Default is c(0.5, 1.5).

base_level

Numeric scalar specifying the baseline activation level. Default is 0.05.

rectify_output

Logical; if TRUE, apply half-wave rectification. Default is TRUE.

seed

Random seed for reproducibility. Required for deterministic results.

Value

A list with the following components:

list_of_data

List of length N; each element is a (T_each x M) matrix of generated EMG signals. This is the standard input format for MFA/MPCA fitting.

true_cluster

Integer vector of length N indicating true cluster assignments (1 to K). Named as true_cluster for consistency.

true_Lambda_list

List of length K; each element is a (M x r) matrix of true cluster-specific synergy loadings. Essential for subspace recovery evaluation. This is the canonical name for ground truth basis matrices.

params

List containing all generation parameters for reproducibility. Includes both canonical short names (T, sep_spatial, sep_temporal, sep_stability) and full names (T_each, cluster_sep_spatial, etc.) for compatibility. Also includes r_mode = "global" for future extension.

Lambda_list

List of length K; each element is a (M x r) matrix of cluster-specific synergy loadings. Kept for backward compatibility; identical to true_Lambda_list.

C_list

List of length N; each element is a (T_each x r) matrix of synergy activation signals (for visualization/validation).

spatial_params

List containing spatial structure parameters for each cluster.

temporal_params

List containing temporal coordination parameters for each cluster.

stability_params

List containing stability-adaptability parameters for each cluster.

Details

This function is designed for use in parameter sweep experiments where model selection (K, r) is performed on generated data. The key differences from simulate_dynamic_synergy_data are:

  • Returns list_of_data (renamed from X_list) for consistency with fitting functions

  • Returns true_cluster (renamed from z_true) for consistency with evaluation functions

  • Includes a params list for tracking generation parameters

  • Seed is required (not optional) to ensure reproducibility

See also

simulate_dynamic_synergy_data for the underlying simulation function, run_param_sweep_modelsel for parameter sweep experiments.

Examples

if (FALSE) { # \dontrun{
# Generate data for model selection experiment
data <- generate_synergy_data(
  N = 30, K = 3, r = 3, M = 8, T_each = 200,
  cluster_sep_spatial = 1.0,
  cluster_sep_temporal = 1.0,
  cluster_sep_stability = 1.0,
  seed = 123
)

# Access the data for fitting
list_of_data <- data$list_of_data
true_cluster <- data$true_cluster

# Fit MFA model
fit <- mfa_em_fit(list_of_data, K = 3, r = 3)

# Evaluate clustering accuracy
ari <- mclust::adjustedRandIndex(true_cluster, fit$z)
} # }