bayes_factor.varstan {bayesforecast} | R Documentation |
Bayes Factors from Marginal Likelihoods.
Description
Compute Bayes factors from marginal likelihoods.
Usage
## S3 method for class 'varstan'
bayes_factor(x1, x2, log = FALSE, ...)
Arguments
x1 |
A |
x2 |
Another |
log |
A boolean parameter for report the Bayes_factor in log scale. The default value is FALSE. |
... |
Additional arguments passed to |
Details
The computation of marginal likelihoods based on bridge sampling requires
a lot more posterior samples than usual. A good conservative rule of thump
is perhaps 10-fold more samples (read: the default of 4000 samples may not
be enough in many cases). If not enough posterior samples are provided, the
bridge sampling algorithm tends to be unstable leading to considerably different
results each time it is run. We thus recommend running bridge_sampler
multiple times to check the stability of the results.
For more details check the bridgesampling package.
Value
The bayes factors of two models.
Examples
library(astsa)
# Fitting a seasonal arima model
mod1 = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
fit1 = varstan(mod1,iter = 500,chains = 1)
# Fitting a Dynamic harmonic regression
mod2 = Sarima(birth,order = c(0,1,2),xreg = fourier(birth,K=6))
fit2 = varstan(mod2,iter = 500,chains = 1)
# compute the Bayes factor
bayes_factor(fit1, fit2)