Find a fixed radius without building the recurrence matrix.
Usage
est_radius_rqa(
y1 = NULL,
y2 = NULL,
emDim = NA,
emLag = NA,
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
- emDim
The embedding dimensions
- emLag
The embedding lag
- AUTO
Auto-recurrence? (default =
FALSE)- method
Distance measure to use. Any option that is valid for argument
methodofproxy::dist(). Typeproxy::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
typeis set to "fixed", the value represents the target value for the measure intargetMeasure(default =RR = .05).- tol
Tolerance for achieving
targetValuefortargetMeasure(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
yiftype == "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 byemDimandemLag(Multidimensional RQA). Ify1and/ory2are 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()
