est_parameters | R Documentation |
Find optimal parameters for constructing a Recurrence Matrix. A wrapper for various algorithms used to find optimal values for the embedding delay and the number of embedding dimensions.
est_parameters(
y,
lagMethods = c("first.minimum", "global.minimum", "max.lag"),
estimateDimensions = "preferSmallestInLargestHood",
maxDim = 10,
emLag = NULL,
maxLag = NA,
minVecLength = 20,
nnSize = NA,
nnRadius = NA,
nnThres = 10,
theiler = 0,
doPlot = TRUE,
silent = TRUE,
...
)
y |
A numeric vector or time series |
lagMethods |
A character vector with one or more of the following strings: |
estimateDimensions |
Decide on an optimal embedding dimension relative to the values in
|
maxDim |
Maximum number of embedding dimensions to consider (default = |
emLag |
Optimal embedding lag (delay), e.g., provided by an optimising algorithm. If |
maxLag |
Maximum embedding lag to consider. If |
minVecLength |
The minimum length of state space vectors after delay-embedding. For short time series, this will affect the possible values of |
nnSize |
Neighbourhood diameter (integer, the |
nnRadius |
Points smaller than the radius are considered neighbours. If |
nnThres |
Threshold value (in percentage 0-100) representing the percentage of Nearest Neighbours that would be acceptable when using N surrogate dimensions. The smallest number of surrogate dimensions that yield a value below the threshold will be considered optimal (default = |
theiler |
Theiler window on distance matrix (default = |
doPlot |
Produce a diagnostic plot the results (default = |
silent |
Silent-ish mode |
... |
Other parameters passed to |
A number of functions are called to determine optimal parameters for delay embedding a time series:
Embedding lag (emLag
): The default is to call est_emLag()
, which is a wrapper around nonlinearTseries::timeLag()
with technique=ami
to get lags based on the mutual information function.
Embedding dimension (m
, emDim
): The default is to call est_emDim()
, which is a wrapper around tseriesChaos::false.nearest()
A list object containing the optimal values (as indicated by the user) and iteration history.
Other Estimate Recurrence Parameters:
est_emDim()
,
est_emLag()
,
est_parameters_roc()
,
est_radius()
,
est_radius_rqa()
set.seed(4321)
est_parameters(y=rnorm(100))
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