varstan: Constructor of a varstan object.
In bayesforecast: Bayesian Time Series Modeling with Stan

Description Usage Arguments Details Value Author(s) References See Also Examples

Constructor of the varstan object for Bayesian estimation in Stan.

1 2	varstan(model,chains=4,iter=2000,warmup=floor(iter/2), adapt.delta = 0.90,tree.depth =10,...)

`model`	One of the `varstan` model classes defined in the package.
`chains`	An integer of the number of Markov Chains chains to be run, by default 4 chains are run.
`iter`	An integer of total iterations per chain including the warm-up, by default the number of iterations are 2000.
`warmup`	A positive integer specifying number of warm-up (aka burn-in) iterations. This also specifies the number of iterations used for stepsize adaptation, so warmup samples should not be used for inference. The number of warmup should not be larger than `iter` and the default is `iter/2`.
`adapt.delta`	An optional real value between 0 and 1, the thin of the jumps in a HMC method. By default is 0.9.
`tree.depth`	An integer of the maximum depth of the trees evaluated during each iteration. By default is 10.
`...`	Further arguments passed to `rstan()` function.

The function estimates one of the defined models in Stan using the stan() function for sampling.

This is the principal package's function and the link with Stan, this function fits the posterior distribution of every parameter for a defined model using a HMC method.

Every estimated model become a varstan object, with different methods for summary, diagnostic, forecast and plotting.

Defining priors

Default priors are chosen to be non or very weakly informative so that their influence on the results will. However, after getting more familiar with Bayesian statistics, I recommend you to start thinking about reasonable informative priors for your model parameters.

Those can be changed using the function set_prior() before estimating the model with the varstan() function. For checking the defined priors use get_prior() and report() functions.

Adjusting the sampling behavior of Stan

In addition to choosing the number of iterations, warmup samples, and chains, users can control the behavior of the NUTS sampler, by using the control argument. The most important reason to use control is to decrease (or eliminate at best) the number of divergent transitions that cause a bias in the obtained posterior samples. Whenever you see the warning "There were x divergent transitions after warmup." you should really think about increasing adapt_delta. Increasing adapt_delta will slow down the sampler but will decrease the number of divergent transitions threatening the validity of your posterior samples.

Another problem arises when the depth of the tree being evaluated in each iteration is exceeded. This is less common than having divergent transitions, but may also bias the posterior samples. When it happens, Stan will throw out a warning suggesting to increase max_treedepth. For more details on the control argument see stan.

a varstan object with the estimated time series model.

A varstan object is a list that contains the following values:

Stanfit a Stanfit object returned by rstan pacakge.
stan.parmaters The parameters used in Stan for the sample.
model The defined model for the time series.
series.name The time series' name.
ts The provided time series data.

Asael Alonzo Matamoros

Carpenter, B. and Gelman, A. and Hoffman, D. and Lee, D. and Goodrich, B. and Betancourt, M. and Brubaker, and Guo, L. and Riddell. 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76(1). doi: 10.18637/jss.v076.i01.

Stan Development Team. (2018). Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0. url: http://mc-stan.org.

Paul-Christian Buerkner (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01

Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software. 26(3), 1-22.doi: 10.18637/jss.v027.i03.

rstan:stan.

 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)
 fit1

 # Fitting a GARCH(1,1) model
 dat = garch(ipc,order = c(1,1,0))
 fit2 = varstan(dat,iter = 500,chains = 1)
 fit2