aic: Computes posterior sample of the point wise AIC method from a...
In bayesforecast: Bayesian Time Series Modeling with Stan
aic R Documentation
Computes posterior sample of the point wise AIC method from a varstan object
Description
Convenience function for computing the point wise Akaike Information Criteria
method from a varstan object.
Usage
aic(x)
Arguments
x
A varstan object of the time series fitted model.
Value
A numeric array of size R, containing the posterior samples of the AICc
for a varstan object, where R is the number of iterations. If multiple chains are
fitted, then the array is of length M*R, where M is the number of chains
Author(s)
Asael Alonzo Matamoros
Examples
model = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
fit1 = varstan(model,iter = 500,chains = 1)
aic1 = aic(fit1)
mean(aic1)
bayesforecast documentation built on June 8, 2025, 10:42 a.m.
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.
| aic | R Documentation |
Computes posterior sample of the point wise AIC method from a varstan object
Description
Convenience function for computing the point wise Akaike Information Criteria
method from a varstan object.
Usage
aic(x)
Arguments
x |
A |
Value
A numeric array of size R, containing the posterior samples of the AICc
for a varstan object, where R is the number of iterations. If multiple chains are
fitted, then the array is of length M*R, where M is the number of chains
Author(s)
Asael Alonzo Matamoros
Examples
model = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
fit1 = varstan(model,iter = 500,chains = 1)
aic1 = aic(fit1)
mean(aic1)
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.