pc-ar: Utility functions for the PC prior for a an AR(p) model
In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion
Description
Usage
Arguments
Value
Author(s)
Description
Functions to evaluate and sample from the
PC prior for an AR(p) model
Usage
1
2
3
inla.pc.ar.rpacf(n=1, p, lambda = 1)
inla.pc.ar.dpacf(pac, lambda = 1, log = TRUE)
Arguments
p
The order of the AR-model
pac
A vector of partial autocorrelation coefficients
n
Number of observations
lambda
The rate parameter in the prior
log
Logical. Return the density in natural or log-scale.
Value
inla.pc.ar.rpac generate samples from the prior, returning a matrix
where each row is a sample of theta.
inla.pc.ar.dpac evaluates the density of pac.
Use inla.ar.pacf2phi, inla.ar.phi2pacf,
inla.ar.pacf2acf and inla.ar.acf2pacf to convert between various
parameterisations.
Author(s)
Havard Rue hrue@math.ntnu.no
andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.
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Description Usage Arguments Value Author(s)
Description
Functions to evaluate and sample from the PC prior for an AR(p) model
Usage
1 2 3 | inla.pc.ar.rpacf(n=1, p, lambda = 1)
inla.pc.ar.dpacf(pac, lambda = 1, log = TRUE)
|
Arguments
p |
The order of the AR-model |
pac |
A vector of partial autocorrelation coefficients |
n |
Number of observations |
lambda |
The rate parameter in the prior |
log |
Logical. Return the density in natural or log-scale. |
Value
inla.pc.ar.rpac generate samples from the prior, returning a matrix
where each row is a sample of theta.
inla.pc.ar.dpac evaluates the density of pac.
Use inla.ar.pacf2phi, inla.ar.phi2pacf,
inla.ar.pacf2acf and inla.ar.acf2pacf to convert between various
parameterisations.
Author(s)
Havard Rue hrue@math.ntnu.no
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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