PCA for waveform data — waveformPCA • PhysioMoCap

Performs Principal Component Analysis on waveform data, either using extracted features or raw waveforms.

Usage

waveformPCA(
  x,
  method = c("features", "raw"),
  features = c("statistical", "shape"),
  n_components = 10,
  scale = TRUE
)

Arguments

x: A PhysioExperiment object or matrix.
method: Feature extraction method: "features" or "raw".
features: If method = "features", which features to extract.
n_components: Number of PCs to retain.
scale: Logical; scale features to unit variance.

Value

A list of class "waveform_pca" containing:

scores: PC scores (observations x components)
loadings: PC loadings (features x components)
variance_explained: Variance explained by each PC
cumulative_variance: Cumulative variance
center: Feature means
scale: Feature SDs (if scaled)

References

van der Maaten L, Hinton G (2008). "Visualizing Data using t-SNE." Journal of Machine Learning Research, 9, 2579-2605.

See also

extractWaveformFeatures(), waveformUMAP(), plotPCAScatter(), plotPCAVariance()

Examples

# PCA on gait features
set.seed(123)
data <- matrix(rnorm(1000), nrow = 100, ncol = 10)
pca_result <- waveformPCA(data, method = "features")
plotPCAScatter(pca_result)