TASC | R Documentation |
Trinarizes real-valued data using the multiscale TASC method.
TASC(vect,
method = c("A","B"),
tau = 0.01,
numberOfSamples = 999,
sigma = seq(0.1, 20, by=.1),
na.rm=FALSE,
error = c("mean", "min"))
method |
Chooses the TASC method to use (see details), i.e. either "A" or "B". |
vect |
A real-valued vector of data to trinarize. |
tau |
This parameter adjusts the sensitivity and the specificity of the statistical testing procedure that rates the quality of the trinarization. Defaults to 0.01. |
numberOfSamples |
The number of samples for the bootstrap test. Defaults to 999. |
sigma |
If |
na.rm |
If set to |
error |
Determines which error should be used for the data points between two thresholds, the "mean" error (default) to the thresholds or the "min" error. |
The two TASC methods can be subdivided into three steps:
An initial step function is obtained by rearranging the original time series measurements in increasing order. Then, step functions with fewer discontinuities are calculated. TASC A calculates these step functions in such a way that each minimizes the Euclidean distance to the initial step function. TASC B obtains step functions from smoothened versions of the input function in a scale-space manner.
A strong discontinuity is a high jump size (derivative) in combination with a low approximation error. For TASC a pair of strongest discontinuities is determined.
Based on these estimates, data values can be excluded from further analyses.
Returns an object of class TASCResult
.
TrinarizationResult
,
TASCResult
par(mfrow=c(2,1))
result <- TASC(iris[,"Petal.Width"], method="A", tau=0.15)
print(result)
plot(result)
result <- TASC(iris[,"Petal.Width"], method="B", tau=0.15)
print(result)
plot(result)
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