cvPCR: Cross validation of PCR reconstruction.
In ntthung/ldsr: Linear Dynamical System Reconstruction

Cross validation of PCR reconstruction.

cvPCR(
  Qa,
  pc,
  start.year,
  transform = "log",
  Z = NULL,
  metric.space = "original",
  use.robust.mean = TRUE
)

`Qa`	Observations: a data.frame of annual streamflow with at least two columns: year and Qa.
`pc`	For a single model: a data.frame, one column for each principal component. For an ensemble reconstruction: a list, each element is a data.frame of principal components.
`start.year`	Starting year of the climate proxies, i.e, the first year of the paleo period. `start.year + nrow(pc) - 1` will determine the last year of the study horizon, which must be greater than or equal to the last year in `Qa`.
`transform`	Flow transformation, either "log", "boxcox" or "none". Note that if the Box-Cox transform is used, the confidence interval after back-transformation is simply the back-transform of the trained onfidence interval; this is hackish and not entirely accurate.
`Z`	A list of cross-validation folds. If `NULL`, will be created with `make_Z()` with default settings. Users are advised to use `make_Z()` to create the cross-validation folds beforehand. See make_Z for details.
`metric.space`	Either "transformed" or "original", the space to calculate the performance metrics.
`use.robust.mean`	If TRUE (the default), use Tukey's biweight robust mean when calculating mean scores across the cross-validation run. If FALSE, use arithmetic mean. The Tukey's biweight robust mean function is `dplR::tbrm()`.

A list of cross validation results

metrics.dist: distribution of performance metrics across all cross-validation runs; a matrix, one column for each metric, with column names.
metrics: average performance metrics; a named vector.
obs: the (transformed) observations, a data.table with two columns (year, y)
Ycv: the predicted streamflow in each cross validation run; a matrix, one column for each cross-validation run
Z: the cross-validation fold