Estimate Radius.

Find a fixed or optimal radius.

est_radius(
  RM = NULL,
  y1 = NULL,
  y2 = NULL,
  emLag = 1,
  emDim = 1,
  method = "Euclidean",
  type = c("fixed", "optimal")[1],
  startRadius = NULL,
  eachRadius = 1,
  targetMeasure = c("RR", "DET", "LAM", "T1", "all")[1],
  targetValue = 0.05,
  tol = 0.01,
  maxIter = 100,
  theiler = NA,
  histIter = FALSE,
  noiseLevel = 0.75,
  noiseType = c("normal", "uniform")[1],
  plotROC = FALSE,
  standardise = c("mean.sd", "median.mad", "none")[3],
  radiusOnFail = c("tiny", "huge", "percentile")[1],
  silent = FALSE
)

Arguments

RM: Unthresholded Recurrence Matrix
y1: A numeric vector or time series
y2: A numeric vector or time series for cross recurrence
emLag: The embedding lag
emDim: The embedding dimensions
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")
type: Either "fixed" (default) or "optimal", "fixed" will search for a radius that is close to the value for the targetMeasure in targetValue, "optimal" will optimise the radius for the targetMeasure, targetValue is ignored.
startRadius: If type = "fixed" this is the starting value for the radius (default = percentile of unique distances in RM given by targetValue). If type = "optimal" this will be a range of radius values (in normalised SD units) that will be considered (default = seq(0,2,by=.01))
eachRadius: If type = "optimal" this is the number of signal and noise series that will be generated for each level in startRadius (default = 1)
targetMeasure: If type = "optimal", it must be a character vector indicating which recurrence measure to optimise the radius for, options are "RR" (default), "DET", "LAM", "T1", and "all". The option targetMeasure = "all" will report all the optimal values obtained from one realisation of startRadius * eachRadius signal and noise series.
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)
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"
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 in RM, which will give a matrix full of ones, "percentile" = quantile(RM, prob = targetValue) of distances greater than 0.
silent: Silent-ish

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)

Arguments

Value

See also