ldsr (version 0.0.2)

cvPCR: Cross validation of PCR reconstruction.

Description

Cross validation of PCR reconstruction.

Usage

cvPCR(
  Qa,
  pc,
  start.year,
  transform = "log",
  Z = NULL,
  metric.space = "transformed"
)

Arguments

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.

Value

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

Examples

# NOT RUN {
cvPCR(NPannual, NPpc, start.year = 1200)
# }