Run Principal Component Analysis

A generic function to perform PCA with support for multiple methods and large-scale data processing.

Usage

run_pca(x, ...)

# Default S3 method
run_pca(
  x,
  method = c("irlba", "base", "parallel"),
  n_components = 50,
  n_cores = NULL,
  diagnostic_plots = TRUE,
  output_scores = TRUE,
  scale_scores = FALSE,
  ...
)

# S3 method for class 'Sap'
run_pca(
  x,
  segment_type = c("motifs", "syllables", "bouts", "segments"),
  data_type = "traj_mat",
  method = c("irlba", "base", "parallel"),
  n_components = 50,
  n_cores = NULL,
  diagnostic_plots = TRUE,
  scale_scores = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x: An object to analyze, either a matrix/data frame or a SAP object
...: Additional arguments passed to specific methods
method: PCA method ('irlba', 'base', or 'parallel')
n_components: Number of principal components
n_cores: Number of cores for parallel processing
diagnostic_plots: Whether to create diagnostic plots
output_scores: Whether to return PC scores
scale_scores: Whether to scale PC scores
segment_type: For SAP objects: Type of segments to analyze ('motifs', 'syllables', 'bouts', 'segments')
data_type: For SAP objects: Type of data to analyze
verbose: For SAP objects: Whether to print progress

Value

For default method: PCA results or PC scores matrix For SAP objects: Updated SAP object with PCA results stored in features slot

Details

This generic function supports PCA through two methods:

Default method for matrices with multiple PCA implementations
SAP object method for organized trajectory data

Examples

if (FALSE) { # \dontrun{
# Run PCA on matrix
pca <- run_pca(matrix, method = "irlba", n_components = 50)

# Run PCA on SAP object
sap_obj <- run_pca(sap_object,
                   segment_type = "motifs",
                   data_type = "traj_mat")
} # }