Calculate sparse principal components of time-dependent variation

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()