cvLDS: Cross validate LDS model. This is a wrapper for...
In ldsr: Linear Dynamical System Reconstruction

Description Usage Arguments Value Examples

Cross validate LDS model. This is a wrapper for LDS_reconstruction

cvLDS(
  Qa,
  u,
  v,
  start.year,
  method = "EM",
  transform = "log",
  num.restarts = 50,
  Z = make_Z(Qa$Qa),
  metric.space = "transformed",
  use.raw = FALSE,
  ub = NULL,
  lb = NULL,
  num.islands = 4,
  pop.per.island = 100,
  niter = 1000,
  tol = 1e-05
)

`Qa`	Observations: a data.frame of annual streamflow with at least two columns: year and Qa.
`u`	Input matrix for a single-model reconstruction, or a list of input matrices for an ensemble reconstruction.
`v`	Same as u.
`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`.
`method`	By default this is "EM". There are experimental methods but you should not try.
`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.
`num.restarts`	The number of initial conditions to start the EM search; ignored if `init` is provided.
`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.raw`	Whether performance metrics are calculated on the raw time series. Experimental; don't use.
`ub`	Upper bounds, a vector whose length is the number of parameters
`lb`	Lower bounds
`num.islands`	Number of islands (if method is GA; experimental)
`pop.per.island`	Initial population per island (if method is GA; experimental)
`niter`	Maximum number of iterations, default 1000
`tol`	Tolerance for likelihood convergence, default 1e-5. Note that the log-likelihood is normalized by dividing by the number of observations.

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.
target: the (transformed) observations used for cross-validation; 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

# Make a shorter time horizon so that this example runs fast
u <- v <- t(NPpc[601:813])
# We run this example without parallelism.
foreach::registerDoSEQ()
cvLDS(NPannual, u, v, start.year = 1800, num.restarts = 2,
      Z = make_Z(NPannual$Qa, nRuns = 1))
# Please refer to the vignette for the full run with parallel options. It takes a minute or two.