makePlotsParticleMetropolisHastingsSVModel: Make plots for tutorial
In pmhtutorial: Minimal Working Examples for Particle Metropolis-Hastings
Description
Usage
Arguments
Value
Author(s)
References
Description
Creates diagnoistic plots from runs of the particle Metropolis-Hastings algorithm.
Usage
1
2
makePlotsParticleMetropolisHastingsSVModel(y, res, noBurnInIterations,
noIterations, nPlot)
Arguments
y
Observations from the model for t=1,...,T.
res
The output from a run of particleMetropolisHastings,
particleMetropolisHastingsSVmodel or
particleMetropolisHastingsSVmodelReparameterised.
noBurnInIterations
The number of burn-in iterations in the
PMH algorithm. Must be smaller than noIterations.
noIterations
The number of iterations in the PMH algorithm.
nPlot
Number of steps in the Markov chain to plot.
Value
The function returns plots similar to the ones in the reference as
well as the estimate of the integrated autocorrelation time
for each parameter.
Author(s)
Johan Dahlin uni@johandahlin.com
References
Dahlin, J. & Schon, T. B. "Getting Started with Particle
Metropolis-Hastings for Inference in Nonlinear Dynamical Models."
Journal of Statistical Software, Code Snippets,
88(2): 1–41, 2019.
pmhtutorial documentation built on May 2, 2019, 3:25 a.m.
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Description Usage Arguments Value Author(s) References
Description
Creates diagnoistic plots from runs of the particle Metropolis-Hastings algorithm.
Usage
1 2 | makePlotsParticleMetropolisHastingsSVModel(y, res, noBurnInIterations,
noIterations, nPlot)
|
Arguments
y |
Observations from the model for t=1,...,T. |
res |
The output from a run of particleMetropolisHastings, particleMetropolisHastingsSVmodel or particleMetropolisHastingsSVmodelReparameterised. |
noBurnInIterations |
The number of burn-in iterations in the PMH algorithm. Must be smaller than noIterations. |
noIterations |
The number of iterations in the PMH algorithm. |
nPlot |
Number of steps in the Markov chain to plot. |
Value
The function returns plots similar to the ones in the reference as well as the estimate of the integrated autocorrelation time for each parameter.
Author(s)
Johan Dahlin uni@johandahlin.com
References
Dahlin, J. & Schon, T. B. "Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models." Journal of Statistical Software, Code Snippets, 88(2): 1–41, 2019.
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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