BaSAR-package: Bayesian Spectrum Analysis in R
In JIC-CSB/BaSAR: Bayesian Spectrum Analysis in R
Description
Details
Note
Author(s)
References
Description
Bayesian Spectrum Analysis of time series data
Details
Package: BaSAR
Type: Package
Version: 1.1
Date: 2012-01-09
Repository: CRAN
License: GPL (>= 2)
LazyLoad: yes
The key function is BaSAR.post. It computes the normalized posterior probability distribution over a predefined range.
Model comparison can be done for time series with trends.
BaSAR.modelratio computes the model ratio between two models, and when the ratio is above 1 the simpler model is preferred. The procedure of adding additional background functions to the model until this ratio is above 1 is automated in BaSAR.auto.
When there are multiple frequencies present in the data, the nested sampling routine, BaSAR.nest, should be preferred over the functions BaSAR.modelratio and BaSAR.auto to compare models. Plot log of posterior probability distribution and visually inspect if there are additional frequencies present.
BaSAR.nest calculates the evidence for a given model, and a model with a higher evidence should be preferred.
The functions output the posterior over omega (angular frequency). If the user wants to plot over period instead, the function BaSAR.plotperiod will produce the posterior over period.
Note
Requires the R libraries polynom and orthopolynom, which will be loaded automatically.
Author(s)
Emma Granqvist, Matthew Hartley and Richard J Morris.
Maintainer: Matthew Hartley list("matthew.hartley@jic.ac.uk")
References
Bretthorst, G. L. (1988) Bayesian spectrum analysis and parameter estimation. Lecture notes
in statistics. New York: Springer-Verlag.
Granqvist, E., Oldroyd, G. E. and Morris, R. J. (2011) Automated Bayesian model development for frequency detection in biological time series. BMC Syst Biol 5, 97.
http://dx.doi.org/10.1186/1752-0509-5-97
Sivia, D. S. and Skilling, J. (2006) Data analysis: a Bayesian tutorial. 2nd Edition. Oxford: Oxford
science publications. Oxford University Press.
JIC-CSB/BaSAR documentation built on May 21, 2019, 1:41 p.m.
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Description Details Note Author(s) References
Description
Bayesian Spectrum Analysis of time series data
Details
| Package: | BaSAR |
| Type: | Package |
| Version: | 1.1 |
| Date: | 2012-01-09 |
| Repository: | CRAN |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
The key function is BaSAR.post. It computes the normalized posterior probability distribution over a predefined range.
Model comparison can be done for time series with trends.
BaSAR.modelratio computes the model ratio between two models, and when the ratio is above 1 the simpler model is preferred. The procedure of adding additional background functions to the model until this ratio is above 1 is automated in BaSAR.auto.
When there are multiple frequencies present in the data, the nested sampling routine, BaSAR.nest, should be preferred over the functions BaSAR.modelratio and BaSAR.auto to compare models. Plot log of posterior probability distribution and visually inspect if there are additional frequencies present.
BaSAR.nest calculates the evidence for a given model, and a model with a higher evidence should be preferred.
The functions output the posterior over omega (angular frequency). If the user wants to plot over period instead, the function BaSAR.plotperiod will produce the posterior over period.
Note
Requires the R libraries polynom and orthopolynom, which will be loaded automatically.
Author(s)
Emma Granqvist, Matthew Hartley and Richard J Morris.
Maintainer: Matthew Hartley list("matthew.hartley@jic.ac.uk")
References
Bretthorst, G. L. (1988) Bayesian spectrum analysis and parameter estimation. Lecture notes in statistics. New York: Springer-Verlag.
Granqvist, E., Oldroyd, G. E. and Morris, R. J. (2011) Automated Bayesian model development for frequency detection in biological time series. BMC Syst Biol 5, 97.
http://dx.doi.org/10.1186/1752-0509-5-97
Sivia, D. S. and Skilling, J. (2006) Data analysis: a Bayesian tutorial. 2nd Edition. Oxford: Oxford science publications. Oxford University Press.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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