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.
Usage
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,
...
)
Arguments
- y
A numeric vector or time series
- lagMethods
A character vector with one or more of the following strings:
"first.minimum","global.minimum","max.lag"
. IfemLag
represents a valid lag this value will be reported as"user.lag"
(default =c("first.minimum","global.minimum","max.lag")
)- estimateDimensions
Decide on an optimal embedding dimension relative to the values in
maxDim
andlagMethods
, according to a number of preferences passed as a character vector. The order in which the preferences appear in the vector affects the selection procedure, with index1
being most important preference. The following options are available:preferNone
- No optimal number will be picked all other preferences will be ignoredpreferSmallestDim
- Pick smallest number of dimensions associated with a percentage NN belownnThres
preferSmallestNN
- Pick the number of dimensions that is associated with the smallest percentage NN belownnThres
preferSmallestLag
- If the value ofnnThres
does not lead to a unique preference for a pair of dimension and lag values, use the pair with the smallest lagpreferSmallestInLargestHood
- The default option: If no unique pair can be found, prefer pairs with smallest values for lag, dimensions, percentage NN for the largest NN size
- maxDim
Maximum number of embedding dimensions to consider (default =
10
)- emLag
Optimal embedding lag (delay), e.g., provided by an optimising algorithm. If
NULL
the lags based on the mutual information inlagMethods
will be reported. If a numeric value representing a valid lag is passed, this value will be used to estimate the number of dimensions (default =NULL
)- maxLag
Maximum embedding lag to consider. If
NA
then the value is caclulated asfloor(length(y)/(maxDim+1))
(default =NA
)- minVecLength
The minimum length of state space vectors after delay-embedding. For short time series, this will affect the possible values of
maxDim
that can be used to evaluate the drop in nearest neighbours. In general it is not recommended to evaluate high dimensional state spaces, based on a small number of state soace coordinates, the default is an absolute minimum and possibly even lower than that. (default =20
)- nnSize
Neighbourhood diameter (integer, the
number.boxes
parameter oftseriesChaos::false.nearest()
) used to speed up neighbour search. (default =NA
)- nnRadius
Points smaller than the radius are considered neighbours. If
NA
the value will besd(y)/10
(default =NA
)- 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 =
10
)- theiler
Theiler window on distance matrix (default =
0
)- doPlot
Produce a diagnostic plot the results (default =
TRUE
)- silent
Silent-ish mode
- ...
Other parameters passed to
nonlinearTseries::timeLag()
Details
A number of functions are called to determine optimal parameters for delay embedding a time series:
Embedding lag (
emLag
): The default is to callest_emLag()
, which is a wrapper aroundnonlinearTseries::timeLag()
withtechnique=ami
to get lags based on the mutual information function.Embedding dimension (
m
,emDim
): The default is to callest_emDim()
, which is a wrapper aroundtseriesChaos::false.nearest()
See also
Other Estimate Recurrence Parameters:
est_emDim()
,
est_emLag()
,
est_parameters_roc()
,
est_radius()
,
est_radius_rqa()