zeitzeigerSpc: Calculate sparse principal components of time-dependent...

In jakejh/zeitzeiger: Regularized Supervised Learning for Data from Rhythmic Systems

Calculate sparse principal components of time-dependent variation

Description

Calculate the SPCs given the time-dependent means and the residuals from zeitzeigerFit().

Usage

zeitzeigerSpc(
  xFitMean,
  xFitResid,
  nTime = 10,
  useSpc = TRUE,
  sumabsv = 1,
  orth = TRUE,
  ...
)

Arguments

xFitMean	List of bigsplines, length is number of features.
xFitResid	Matrix of residuals, dimensions are observations by features.
nTime	Number of time-points by which to discretize the time-dependent behavior of each feature. Corresponds to the number of rows in the matrix for which the SPCs will be calculated.
useSpc	Logical indicating whether to use PMA::SPC() (default) or base::svd().
sumabsv	L1-constraint on the SPCs, passed to PMA::SPC().
orth	Logical indicating whether to require left singular vectors be orthogonal to each other, passed to PMA::SPC().
...	Other arguments passed to PMA::SPC().

Value

Output of PMA::SPC(), unless useSpc is FALSE, then output of base::svd().

See Also

zeitzeigerFit(), zeitzeigerPredict()

jakejh/zeitzeiger documentation built on Dec. 4, 2022, 4:11 p.m.