This function serves as a wrapper for function rp(), it will add some attributes to the matrix related to network representation. These attributes will be used to decide which network type to generate (e.g. undirected, directed, weighted, etc.)
rn(
y1,
y2 = NULL,
emDim = 1,
emLag = 1,
emRad = NULL,
theiler = 0,
directed = FALSE,
cumulative = TRUE,
weighted = FALSE,
weightedBy = c("none", "si", "rt", "rf")[1],
rescaleWeights = FALSE,
fs = NA,
to.ts = NULL,
order.by = NULL,
to.sparse = FALSE,
method = c("Euclidean", "max", "SBD")[1],
targetValue = 0.05,
returnGraph = FALSE,
doPlot = FALSE,
doEmbed = TRUE,
silent = TRUE,
...
)A numeric vector or time series
A numeric vector or time series for cross recurrence
The embedding dimensions
The embedding lag
The threshold (emRad) to apply to the distance matrix to create a binary or weighted matrix. If NULL, an unthresholded matrix will be created (default = NULL)
Use a theiler window around the main diagonal (Line of Identity/Synchronisation) to remove auto-correlations at short time-lags:
0 will include the main diagonal in all RQA measure calculations.
1 will remove the main diagonal from all RQA measure calculations.
NA (default), will check if the matrix is symmetrical , if so, it will remove the diagonal by setting theiler = 1 (Line of Identity, Auto-RQA), if it is not symmetrical (Line of Synchronisation, Cross-RQA) it will set theiler = 0.
A value greater than 1 will remove that many diagonals around and including the diagonal from all RQA measure calculations. So theiler = 2 means exclude 2 diagonals around the main diagonal, including the main diagonal itself: [-1,0,1].
If theiler is a numeric vector of length(theiler) == 2 it is possible to exclude an asymmetrical window. The values are interpreted as end points in a sequence of diagonal ID's, e.g. theiler = c(-1,5) will exclude [-1,0,1,2,3,4,5]. If length(theiler) > 2, the values will be considered individual diagonal ID's, e.g. theiler = c(-3,-1,0,2,5), will exclude only those specific ID's. Also see the note.
Should the matrix be considered to represent a directed network? (default = FALSE)
To make the network represent cumulative time, set directed = TRUE and cumulative = TRUE. This will set the upper triangle of the recurrence matrix to 0 and ensures that the network edges represent recurrent values that have occurred in the past relative to the current observed value (node). If directed = FALSE the argument is ignored (default = TRUE).
Should the matrix be considered to represent a weighted network? (default = FALSE)
After setting values smaller than emRad to 0, what should the recurrent values represent? The default is to use the state space similarity (distance/proximity) values as weights ("si"). Other option are "rt" for recurrence time and "rf" for recurrence time frequency, Because vertices represent time points in \(\epsilon\)-thresholded recurrence networks, a difference of two vertex-indices represents duration. If an edge e1 connects v1 and v10 then the recurrence time will be the difference of the vertex indices, 9, and the recurrence time frequency will be 1/9.
If set to TRUE and weighted = TRUE, all weight values will be rescaled to [0,1], where 0 means no recurrence relation and 1 the maximum weight value.
Sample frequency: A numeric value interpreted as the number of observed samples per unit of time. If the weights represent recurrence times ("rt"), they will be divided by the value in fs. If the weights represent recurrence time frequencies ("rf"), they will be multiplied by the value of fs (default = NA)
Should y1 and y2 be converted to time series objects?
If to.ts = TRUE, pass a vector of the same length as y1 and y2. It will be used as the time index, if NA the vector indices will be used to represent time.
Should sparse matrices be used?
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")
A value passed to est_radius(...,type="fixed", targetMeasure="RR") if is.na(emRad)==TRUE.
Return an igraph::igraph() object (default = FALSE)
Plot the matrix by calling rp_plot() with default settings
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)
Silent-ish mode
Any paramters to pass to rn_plot() if doPlot = TRUE
A (Coss-) Recurrence matrix that can be interpreted as an adjacency (or incidence) matrix.