Estimate Radius without building a recurrence matrix

Find a fixed radius without building the recurrence matrix.

est_radius_rqa(
  y1 = NULL,
  y2 = NULL,
  AUTO = NULL,
  method = "Euclidean",
  startRadius = NULL,
  targetValue = 0.05,
  tol = 0.01,
  maxIter = 100,
  theiler = NA,
  histIter = FALSE,
  standardise = c("mean.sd", "median.mad", "none")[3],
  radiusOnFail = c("tiny", "huge", "percentile")[3],
  silent = FALSE,
  useParallel = TRUE,
  doEmbed = TRUE
)

Arguments

y1

A numeric vector or time series

y2

A numeric vector or time series for cross recurrence

AUTO

Auto-recurrence? (default = FALSE)

method

Distance measure to use. Any option that is valid for argument method of proxy::dist(). Type proxy::pr_DB$get_entries() to see a list of all the options. Common methods are: "Euclidean", "Manhattan", "Minkowski", "Chebysev" (or the same but shorter: "L2","L1","Lp", "max" distance). To use the shape based distance for phase-based recurrence use "SBD" (default = "Euclidean")

startRadius

The starting value for the radius (default = SD of time series values)

targetValue

When argument type is set to "fixed", the value represents the target value for the measure in targetMeasure (default = RR = .05).

tol

Tolerance for achieving targetValue for targetMeasure (default = 0.01)

maxIter

If type = "fixed": Maximum number of iterations to reach targetValue.

theiler

Size of theiler window (default 0)

histIter

Return iteration history? (default = FALSE)

standardise

Standardise y if type == "optimal"

radiusOnFail

Radius to return when search fails "tiny" = 0 + ,Machine.double.eps, this will likely cause a matrix full of zeros. "huge" = 1 + max. distance, which will give a matrix full of ones, "minimum" = minimum distance in matrix.

silent

Silent-ish

useParallel

Should evaluation run using package parallel? This is will only be beneficial if the time series contains more than 10k data points (default = TRUE)

doEmbed

If FALSE, a distance matrix will be returned that is not embedded by emDim and emLag (Multidimensional RQA). If y1 and/or y2 are data frames, the columns will be used as the state space dimensions (default = TRUE)

noiseLevel

Noise level to construct the signal + noiseLevel * \(N(\mu=0,\sigma=1)\) (default = 0.75)

noiseType

Type

plotROC

Generates an ROC plot if type = "optimal"

Value

A dataframe listing settings used to search for the radius, the radius found given the settings and the recurrence rate produced by the radius (either 1 row or the entire iteration history)

See also

Other Estimate Recurrence Parameters: est_emDim(), est_emLag(), est_parameters(), est_parameters_roc(), est_radius()