| Type: | Package |
| Title: | Statistical Deadband Algorithms Comparison |
| Version: | 0.1.0 |
| Author: | Nunzio Torrisi |
| Maintainer: | Nunzio Torrisi <nunzio.torrisi@ieee.org> |
| Description: | Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms. |
| License: | GPL-2 |
| Depends: | R (≥ 2.10) |
| Imports: | TTR |
| LazyData: | TRUE |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-09-12 01:31:23 UTC; robin |
| Repository: | CRAN |
| Date/Publication: | 2016-09-12 08:11:40 |
deadbandAD Function
Description
This function allows you to compute the Absolute Deadband(AD) algorithm
Usage
deadbandAD(x, EUmax, EUmin, d, offset)
Arguments
x |
The vector of the samples before the deadband algorithm |
EUmax |
The Engineering Unit higher bound |
EUmin |
The Engineering Unit lower bound |
d |
Deadband percent parameter in range 0..1 |
offset |
How many sample do you want skip at begin? Defaults is n=20 |
Value
A list containing the L2 distance and the Number of filtered samples
Examples
deadbandAD(rnorm(40, mean = 0, sd = 1),+0.5,-0.5,0.01,20)
deadbandBD Function
Description
This function allows you to compute the Bollinger Deadband(BD) algorithm
Usage
deadbandBD(x, d, offset, k)
Arguments
x |
The vector of the samples before the deadband algorithm |
d |
Deadband percent parameter in range 0..1 |
offset |
How many sample do you want skip at begin? Defaults is n=20 |
k |
multiplier used in Bollinger theory |
Value
A list containing the L2 distance and the Number of filtered samples
Examples
deadbandBD(rnorm(40, mean = 0, sd = 1),0.01,20,2)
deadbandVD Function
Description
This function allows you to compute the Volatility Deadband(VD) algorithm
Usage
deadbandVD(x, d, offset, k)
Arguments
x |
The vector of the samples before the deadband algorithm |
d |
Deadband percent parameter in range 0..1 |
offset |
How many sample do you want skip at begin? Defaults is n=20 |
k |
multiplier used in Bollinger theory |
Value
A list containing the L2 distance and the Number of filtered samples
Examples
deadbandVD(rnorm(40, mean = 0, sd = 1),0.01,20,2)
Samples subset of 10 pesudo periodic signals
Description
Sampling rate: 210ms for synthetic.sub35;
Usage
synthetic.sub35
Format
A data table with a column for each signal:
Details
The original dataset containing the 10 pseudo periodoc signal are available for download at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.gz More Info at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.html
Dataset freely available for research use.
Samples subset of 10 pesudo periodic signals
Description
Sampling rate: 240ms for synthetic.sub40;
Usage
synthetic.sub40
Format
A data table with a column for each signal:
Details
The original dataset containing the 10 pseudo periodoc signal are available for download at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.gz More Info at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.html
Dataset freely available for research use.
Samples subset of 10 pesudo periodic signals
Description
Sampling rate: 252ms for synthetic.sub42; The original dataset containing the 10 pseudo periodoc signal are available for download at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.gz More Info at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.html
Usage
synthetic.sub42
Format
A data table with a column for each signal:
Details
Dataset freely available for research use.
Samples subset of 10 pesudo periodic signals
Description
Sampling rate: 300ms for synthetic.sub50;
Usage
synthetic.sub50
Format
A data table with a column for each signal:
Details
The original dataset containing the 10 pseudo periodoc signal are available for download at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.gz More Info at: http://archive.ics.uci.edu/ml/machine-learning-databases/synthetic-mld/synthetic.data.html
Dataset freely available for research use.