# Calculate sparse principal components of time-dependent variation

Source:`R/zeitzeiger_fit.R`

`zeitzeigerSpc.Rd`

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()`

.