Nothing
R/stan_models.R
In bayesforecast: Bayesian Time Series Modeling with Stan
Defines functions
stan_Hw
stan_Holt
stan_LocalLevel
stan_ssm
stan_SVM
stan_naive
stan_garch
stan_sarima
Documented in
stan_garch
stan_Holt
stan_Hw
stan_LocalLevel
stan_naive
stan_sarima
stan_ssm
stan_SVM
#' Fitting a Multiplicative Seasonal ARIMA model.
#'
#' Fitting a SARIMA model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param order a three length vector with the specification of the non-seasonal
#' part of the ARIMA model. The three components `c(p, d, q)` are the AR,
#' the number of differences, and the MA orders respectively.
#' @param seasonal a three length vector with the specification of the seasonal
#' part of the ARIMA model. The three components `c(P, D, Q)` are the seasonal AR,
#' the degree of seasonal differences, and the seasonal MA orders respectively.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series. By
#' default, \code{period = frequency(ts)}.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in an
#' ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_sar The prior distribution for the seasonal auto-regressive
#' parameters in a SARIMA model. By default, uses `normal(0,0.5)` priors.
#' @param prior_sma The prior distribution for the seasonal moving average
#' parameters in a SARIMA model. By default, uses `normal(0,0.5)` priors.
#' @param prior_breg The prior distribution for the regression coefficient
#' parameters in a ARIMAX model. By default, uses `student(7,0,1)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' If \code{xreg} option is used, the model by default will cancel the
#' seasonal differences adjusted (D = 0). If a value \code{d} > 0 is used, all
#' the regressor variables in \code{xreg} will be difference as well.
#'
#' The default priors used in `Sarima` model are:
#'
#' \itemize{
#' \item{ar ~ normal(0,0.5)}
#' \item{ma ~ normal(0,0.5)}
#' \item{mu0 ~ t-student(0,2.5,6)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{sar ~ normal(0,0.5)}
#' \item{sma ~ normal(0,0.5)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' @author Asael Alonzo Matamoros
#'
#' @return A \code{varstan} object with the fitted SARIMA model.
#'
#' @importFrom stats as.ts time frequency
#' @importFrom utils tail
#' @export
#'
#' @references
#' Box, G. E. P. and Jenkins, G.M. (1978). Time series analysis: Forecasting and
#' control. San Francisco: Holden-Day. \emph{Biometrika}, 60(2), 297-303.
#' \code{doi:10.1093/biomet/65.2.297}.
#'
#' Kennedy, P. (1992). Forecasting with dynamic regression models: Alan Pankratz, 1991.
#' \emph{International Journal of Forecasting}. 8(4), 647-648.
#' \code{url: https://EconPapers.repec.org/RePEc:eee:intfor:v:8:y:1992:i:4:p:647-648}.
#'
#' Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
#' forecast package for \code{R}. \emph{Journal of Statistical Software}. 26(3),
#' 1-22.\code{doi: 10.18637/jss.v027.i03}
#'
#' @seealso \code{garch}, and \code{set_prior}.
#'
#' @examples
#' \donttest{
#' # Declare a multiplicative seasonal ARIMA model for the birth data.
#' sf1 = stan_sarima(birth,order = c(0,1,2),
#' seasonal = c(1,1,1),iter = 500,chains = 1)
#'
#'
#' #Declare an Dynamic Harmonic Regression model for the birth data.
#' sf2 = stan_sarima(birth,order = c(1,0,1),
#' xreg = fourier(birth,K = 2),iter = 500,chains = 1)
#' }
#'
stan_sarima = function(ts, order = c(1,0,0), seasonal = c(0,0,0), xreg = NULL,
period = 0, chains = 4, iter = 2000, warmup = floor(iter/2),
adapt.delta = 0.9, tree.depth = 10, prior_mu0 = NULL,
prior_sigma0 = NULL, prior_ar = NULL,
prior_ma = NULL, prior_sar = NULL, prior_sma = NULL,
prior_breg = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Sarima(ts = ts, order = order, seasonal = seasonal,
xreg = xreg, period = period, series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat,par = "mu0",dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat,par = "ar",dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat,par = "ma",dist = prior_ma)
if(!is.null(prior_sar)) dat = set_prior(model = dat,par = "sar",dist = prior_sar)
if(!is.null(prior_sma)) dat = set_prior(model = dat,par = "sma",dist = prior_sma)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
# Fitting the Sarima model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Fitting for a GARCH(s,k,h) model.
#'
#' Fitting a \code{GARCH(s,k,h)} model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param order a vector of length three specifying the GARCH model. The three
#' components `c(s, k, h)` are the ARCH order, the GARCH order, and the MGARCH
#' orders respectively.
#' @param arma a vector of length two with the ARMA model configuration. The two
#' components `c(p, q)` are the AR, and the MA orders respectively.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a bool value to specify for a generalized t-student GARCH model.
#' @param asym a string value for the asymmetric function for an asymmetric
#' GARCH process. By default, the value \code{"none"} for standard GARCH process.
#' \code{"logit"} option a specifies a logistic function for asymmetry, and option
#' \code{"exp"} defines an exponential function.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in an
#' ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_arch The prior distribution for the ARCH parameters in a GARCH
#' model. By default, sets `normal(0,0.5)` priors.
#' @param prior_garch The prior distribution for the GARCH parameters in a GARCH
#' model. By default, sets `normal(0,0.5)` priors.
#' @param prior_mgarch The prior distribution for the mean GARCH parameters in a
#' GARCH model. By default, sets `normal(0,0.5)` priors.
#' @param prior_breg The prior distribution for the regression coefficient
#' parameters in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param prior_df The prior distribution for the degree freedom parameters in a
#' t-student innovations GARCH model. By default, sets a `gamma(2,0.1)` prior.
#' @param prior_gamma The prior distribution for the asymmetric parameters in
#' MGARCH model. By default, sets `normal(0,0.5)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' By default the \code{garch()} function generates a GARCH(1,1) model. The
#' \code{genT = TRUE} option defines a t-student innovations GARCH model
#' (see Ardia (2010)). For Asymmetric GARCH models use the
#' option \code{asym} for specify the asymmetric functions, see Fonseca,
#' et. al (2019) for more details.
#'
#' The default priors used in a GARCH(s,k,h) model are:
#'
#' \itemize{
#' \item{ar ~ normal(0,0.5)}
#' \item{ma ~ normal(0,0.5)}
#' \item{mu0 ~ t-student(0,2.5,6)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{arch ~ normal(0,0.5)}
#' \item{garch ~ normal(0,0.5)}
#' \item{mgarch ~ normal(0,0.5)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted GARCH model.
#'
#' @export
#' @importFrom stats as.ts time
#'
#' @references
#' Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of
#' the Variance of United Kingdom Inflation. \emph{Econometrica}, 50(4), 987-1007.
#' \code{url: http://www.jstor.org/stable/1912773}.
#'
#' Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity.
#' \emph{Journal of Econometrics}. 31(3), 307-327.
#' \code{doi: https://doi.org/10.1016/0304-4076(86)90063-1}.
#'
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' Ardia, D. and Hoogerheide, L. (2010). Bayesian Estimation of the GARCH(1,1) Model
#' with Student-t Innovations. \emph{The R Journal}. 2(7), 41-47.
#' \code{doi: 10.32614/RJ-2010-014}.
#'
#' @seealso \code{Sarima}, \code{auto.arima}, and \code{set_prior}.
#'
#' @examples
#' \donttest{
#' # Declaring a garch(1,1) model for the ipc data.
#' sf1 = stan_garch(ipc,order = c(1,1,0),iter = 500,chains = 1)
#'
#' # Declaring a t-student M-GARCH(2,3,1)-ARMA(1,1) process for the ipc data.
#' sf2 = stan_garch(ipc,order = c(2,3,1),arma = c(1,1),genT = TRUE,iter = 500,chains = 1)
#' }
#'
stan_garch = function(ts,order = c(1,1,0), arma = c(0,0), xreg = NULL, genT = FALSE,
asym = "none", chains = 4, iter = 2000, warmup = floor(iter/2),
adapt.delta = 0.9, tree.depth = 10, prior_mu0 = NULL,
prior_sigma0 = NULL, prior_ar = NULL, prior_ma = NULL,
prior_mgarch = NULL, prior_arch = NULL, prior_garch = NULL,
prior_breg = NULL, prior_gamma = NULL, prior_df = NULL,
series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = garch(ts = ts, order = order, arma = arma, xreg = xreg, genT = genT,
asym = asym, series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat, par = "mu0", dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat, par = "sigma0", dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat, par = "ar", dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat, par = "ma", dist = prior_ma)
if(!is.null(prior_breg)) dat = set_prior(model = dat, par = "breg", dist = prior_breg)
if(!is.null(prior_arch)) dat = set_prior(model = dat, par = "arch", dist = prior_arch)
if(!is.null(prior_garch)) dat = set_prior(model = dat, par = "garch", dist = prior_garch)
if(!is.null(prior_mgarch)) dat = set_prior(model = dat ,par = "mgarch", dist = prior_mgarch)
if(!is.null(prior_df)) dat = set_prior(model = dat, par = "df", dist = prior_df)
if(!is.null(prior_gamma)) dat = set_prior(model = dat, par = "gamma", dist = prior_gamma)
# Fitting the garch model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Naive and Random Walk models.
#'
#' Naive is the model constructor for a random walk model applied to \code{y}.
#' This is equivalent to an ARIMA(0,1,0) model. \code{naive()} is simply a wrapper
#' to maintain forecast package similitude. \code{seasonal} returns the model
#' constructor for a seasonal random walk equivalent to an ARIMA(0,0,0)(0,1,0)m
#' model where m is the seasonal period.
#'
#' The random walk with drift model is
#' \deqn{Y_t = mu_0 + Y_{t-1} + epsilon_t}{Y[t]= mu_0 +Y[t-1] + epsilon[t]}
#' where \eqn{epsilon_t}{epsilon[t]} is a normal iid error.
#'
#' The seasonal naive model is
#' \deqn{Y_t = mu_0 + Y_{t-m} + epsilon_t}{Y[t]= mu_0 +Y[t-m] + epsilon[t]}
#' where \eqn{epsilon_t}{epsilon[t]} is a normal iid error.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param seasonal a Boolean value for select a seasonal random walk instead.
#' @param m an optional integer value for the seasonal period.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm-up iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @return A \code{varstan} object with the fitted naive Random Walk model.
#'
#' @author Asael Alonzo Matamoros
#'
#' @seealso \code{Sarima}.
#' @export
#'
#' @references
#'
#' Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
#' forecast package for \code{R}. \emph{Journal of Statistical Software}. 26(3),
#' 1-22.\code{doi: 10.18637/jss.v027.i03}.
#'
#' Box, G. E. P. and Jenkins, G.M. (1978). Time series analysis: Forecasting and
#' control. San Francisco: Holden-Day. \emph{Biometrika}, 60(2), 297-303.
#' \code{doi:10.1093/biomet/65.2.297}.
#'
#' Kennedy, P. (1992). Forecasting with dynamic regression models: Alan Pankratz, 1991.
#' \emph{International Journal of Forecasting}. 8(4), 647-648.
#' \code{url: https://EconPapers.repec.org/RePEc:eee:intfor:v:8:y:1992:i:4:p:647-648}.
#'
#' @examples
#' \donttest{
#' # A seasonal Random-walk model.
#' sf1 = stan_naive(birth,seasonal = TRUE,iter = 500,chains = 1)
#' }
#'
stan_naive = function(ts, seasonal = FALSE, m = 0, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_mu0 = NULL, prior_sigma0 = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = naive(ts = ts,seasonal = seasonal,m = m)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat, par = "mu0", dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat, par = "sigma0", dist = prior_sigma0)
# Fitting the naive model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Fitting a Stochastic volatility model.
#'
#' Fitting a Stochastic Volatility model (SVM) in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param arma Optionally, a specification of the ARMA model,same
#' as order parameter: the two components (p, q) are the AR order,and
#' the MA order.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in
#' an ARMA model. By default, sets `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARMA model. By default, sets the `normal(0,0.5)` priors.
#' @param prior_alpha The prior distribution for the auto-regressive parameters in
#' a SVM model. By default, set a `normal(0, 0.5)` prior.
#' @param prior_beta The prior distribution for the exponential intercept parameter
#' in a SVM model. By default, uses a `normal(0,0.5)` prior.
#' @param prior_breg The prior distribution for the regression coefficient parameters
#' in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @return A \code{varstan} object with the fitted SVM model.
#'
#' @author Asael Alonzo Matamoros
#'
#' @export
#'
#' @references
#' Sangjoon,K. and Shephard, N. and Chib.S (1998). Stochastic Volatility: Likelihood
#' Inference and Comparison with ARCH Models. \emph{Review of Economic Studies}.
#' 65(1), 361-93. \code{url: https://www.jstor.org/stable/2566931}.
#'
#' Tsay, R (2010). Analysis of Financial Time Series.
#' \emph{Wiley-Interscience}. 978-0470414354, second edition.
#'
#' Shumway, R.H. and Stoffer, D.S. (2010).Time Series Analysis and Its
#' Applications: With R Examples. \emph{Springer Texts in Statistics}.
#' isbn: 9781441978646. First edition.
#'
#' @seealso \code{garch}, and \code{set_prior}
#'
#' @examples
#' \donttest{
#' # Declares a SVM model for the IPC data
#' sf1 = stan_SVM(ipc,arma = c(1,1),iter = 500,chains = 1)
#' }
#'
stan_SVM = function(ts, arma = c(0,0), xreg = NULL, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_mu0 = NULL, prior_sigma0 = NULL, prior_ar = NULL,
prior_ma = NULL, prior_alpha = NULL, prior_beta = NULL,
prior_breg = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = SVM(ts = ts,arma = arma,xreg = xreg,series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat,par = "mu0",dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat,par = "ar",dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat,par = "ma",dist = prior_ma)
if(!is.null(prior_alpha)) dat = set_prior(model = dat,par = "alpha",dist = prior_alpha)
if(!is.null(prior_beta)) dat = set_prior(model = dat,par = "beta",dist = prior_beta)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
# Fitting the SVM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting an Additive linear State space model.
#'
#' Fitting an Additive linear State space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param trend a bool value to specify a trend local level model. By default
#' is \code{FALSE}.
#' @param damped a boolvalue to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param seasonal a bool value to specify a seasonal local level model. By default
#' is \code{FALSE}.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series by
#' default the value frequency(ts) is used.
#' @param genT a bool value to specify for a generalized t-student SSM model.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm-up iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default, sets a `normal(0,0.5)` prior.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default, sets a `normal(0,0.5)` prior.
#' @param prior_damped The prior distribution for the damped trend parameter in a
#' SSM model. By default, sets a `normal(0,0.5)` prior.
#' @param prior_seasonal The prior distribution for the seasonal parameter in a
#' SSM model. By default, sets a `normal(0,0.5)` prior.
#' @param prior_level1 The prior distribution for the initial level parameter in
#' a SSM model. By default, sets a `student(6,0,2.5)` prior.
#' @param prior_trend1 The prior distribution for the initial trend parameter in
#' a SSM model. By default, sets a `student(6,0,2.5)` prior.
#' @param prior_seasonal1 The prior distribution for the initial seasonal parameters
#' in a SSM model. The prior is specified for the first m seasonal parameters,
#' where `m` is the periodicity of the defined time series. By default, sets a
#' `normal(0,0.5)` prior.
#' @param prior_breg The prior distribution for the regression coefficient parameters
#' in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param prior_df The prior distribution for the degree freedom parameters in a
#' t-student innovations SSM model. By default, sets a `gamma(2,0.1)` prior
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' By default the \code{ssm()} function generates a local-level, `ets("A","N","N")`,
#' or exponential smoothing model from the \pkg{forecast} package. When
#' \code{trend = TRUE} the SSM transforms into a local-trend, `ets("A","A","N")`,
#' or the equivalent Holt model. For damped trend models set \code{damped = TRUE}.
#' If \code{seasonal = TRUE}, the model is a seasonal local level model, or
#' `ets("A","N","A")` model. Finally, the Holt-Winters method (`ets("A","A","A")`)
#' is obtained by setting both \code{Trend = TRUE} and \code{seasonal = TRUE}.
#'
#' The \code{genT = TRUE} option generates a t-student innovations SSM model. For
#' a detailed explanation, check Ardia (2010); or Fonseca, et. al (2019).
#'
#' The default priors used in a `ssm` model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{Seasonal ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{seasonal1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{Sarima}, \code{auto.arima}, \code{set_prior}, and \code{garch}.
#'
#' @examples
#' \donttest{
#' # Declaring a local level model for the ipc data.
#' sf1 = stan_ssm(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt model for the ipc data.
#' sf2 = stan_ssm(ipc,trend = TRUE,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_ssm = function(ts, trend = FALSE, damped = FALSE, seasonal = FALSE, xreg = NULL,
period = 0, genT = FALSE, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_trend = NULL, prior_trend1 = NULL, prior_damped = NULL,
prior_seasonal = NULL, prior_seasonal1 = NULL, prior_breg = NULL,
prior_df = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = ssm(ts = ts, trend = trend, damped = damped, seasonal = seasonal, xreg = xreg,
period = period, genT = genT, series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_seasonal)) dat = set_prior(model = dat,par = "seasonal",dist = prior_seasonal)
if(!is.null(prior_seasonal1)) dat = set_prior(model = dat,par = "seasonal1",dist = prior_seasonal1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting a Local level state-space model.
#'
#' Fitting a Local level state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a Local_level( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted Local Level model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}.
#'
#' @examples
#' \donttest{
#' # Declaring a local level model for the ipc data.
#' sf1 = stan_LocalLevel(ipc,iter = 500,chains = 1)
#' }
#'
stan_LocalLevel = function(ts, xreg = NULL, genT = FALSE, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = LocalLevel(ts = ts,xreg = xreg,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting an Holt state-space model.
#'
#' Fitting an Holt state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param damped a boolean value to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup A positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm up should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_damped The prior distribution for the damped trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_trend1 The prior distribution for the initial trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a ssm( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return a \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}.
#'
#' @examples
#' \donttest{
#' # Declaring a Holt model for the ipc data.
#' sf1 = stan_Holt(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt damped trend model for the ipc data.
#' sf2 = stan_Holt(ipc,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_Holt = function(ts, damped = FALSE, xreg = NULL, genT = FALSE, chains = 4,
iter = 2000, warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_trend = NULL, prior_trend1 = NULL, prior_damped = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Holt(ts = ts,damped = damped,xreg = xreg,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting a Holt-Winters state-space model.
#'
#' Fitting a Holt-Winters state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param damped a boolean value to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series by
#' default the value frequency(ts) is used.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup A positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_damped The prior distribution for the damped trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_seasonal The prior distribution for the seasonal parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_trend1 The prior distribution for the initial trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_seasonal1 The prior distribution for the initial seasonal parameters in a SSM model.
#' The prior is specified for the first m seasonal parameters, where m is the periodicity of the
#' defined time series. By default the value is set \code{NULL}, then the default normal(0,0.5) priors
#' are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a ssm( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{Seasonal ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{seasonal1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}
#'
#' @examples
#' \donttest{
#' # Declaring a Holt-Winters model for the ipc data.
#' sf1 = stan_Hw(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt-Winters damped trend model for the ipc data.
#' sf2 = stan_ssm(ipc,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_Hw = function(ts,damped = FALSE, xreg = NULL, period = 0, genT = FALSE,
chains = 4, iter = 2000, warmup = floor(iter/2), adapt.delta = 0.9,
tree.depth = 10, prior_sigma0 = NULL, prior_level = NULL,
prior_level1 = NULL, prior_trend = NULL, prior_trend1 = NULL,
prior_damped = NULL,prior_seasonal = NULL, prior_seasonal1 = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Hw(ts = ts,damped = damped,xreg = xreg,period = period,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_seasonal)) dat = set_prior(model = dat,par = "seasonal",dist = prior_seasonal)
if(!is.null(prior_seasonal1)) dat = set_prior(model = dat,par = "seasonal1",dist = prior_seasonal1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
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Defines functions stan_Hw stan_Holt stan_LocalLevel stan_ssm stan_SVM stan_naive stan_garch stan_sarima
Documented in stan_garch stan_Holt stan_Hw stan_LocalLevel stan_naive stan_sarima stan_ssm stan_SVM
#' Fitting a Multiplicative Seasonal ARIMA model.
#'
#' Fitting a SARIMA model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param order a three length vector with the specification of the non-seasonal
#' part of the ARIMA model. The three components `c(p, d, q)` are the AR,
#' the number of differences, and the MA orders respectively.
#' @param seasonal a three length vector with the specification of the seasonal
#' part of the ARIMA model. The three components `c(P, D, Q)` are the seasonal AR,
#' the degree of seasonal differences, and the seasonal MA orders respectively.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series. By
#' default, \code{period = frequency(ts)}.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in an
#' ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_sar The prior distribution for the seasonal auto-regressive
#' parameters in a SARIMA model. By default, uses `normal(0,0.5)` priors.
#' @param prior_sma The prior distribution for the seasonal moving average
#' parameters in a SARIMA model. By default, uses `normal(0,0.5)` priors.
#' @param prior_breg The prior distribution for the regression coefficient
#' parameters in a ARIMAX model. By default, uses `student(7,0,1)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' If \code{xreg} option is used, the model by default will cancel the
#' seasonal differences adjusted (D = 0). If a value \code{d} > 0 is used, all
#' the regressor variables in \code{xreg} will be difference as well.
#'
#' The default priors used in `Sarima` model are:
#'
#' \itemize{
#' \item{ar ~ normal(0,0.5)}
#' \item{ma ~ normal(0,0.5)}
#' \item{mu0 ~ t-student(0,2.5,6)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{sar ~ normal(0,0.5)}
#' \item{sma ~ normal(0,0.5)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' @author Asael Alonzo Matamoros
#'
#' @return A \code{varstan} object with the fitted SARIMA model.
#'
#' @importFrom stats as.ts time frequency
#' @importFrom utils tail
#' @export
#'
#' @references
#' Box, G. E. P. and Jenkins, G.M. (1978). Time series analysis: Forecasting and
#' control. San Francisco: Holden-Day. \emph{Biometrika}, 60(2), 297-303.
#' \code{doi:10.1093/biomet/65.2.297}.
#'
#' Kennedy, P. (1992). Forecasting with dynamic regression models: Alan Pankratz, 1991.
#' \emph{International Journal of Forecasting}. 8(4), 647-648.
#' \code{url: https://EconPapers.repec.org/RePEc:eee:intfor:v:8:y:1992:i:4:p:647-648}.
#'
#' Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
#' forecast package for \code{R}. \emph{Journal of Statistical Software}. 26(3),
#' 1-22.\code{doi: 10.18637/jss.v027.i03}
#'
#' @seealso \code{garch}, and \code{set_prior}.
#'
#' @examples
#' \donttest{
#' # Declare a multiplicative seasonal ARIMA model for the birth data.
#' sf1 = stan_sarima(birth,order = c(0,1,2),
#' seasonal = c(1,1,1),iter = 500,chains = 1)
#'
#'
#' #Declare an Dynamic Harmonic Regression model for the birth data.
#' sf2 = stan_sarima(birth,order = c(1,0,1),
#' xreg = fourier(birth,K = 2),iter = 500,chains = 1)
#' }
#'
stan_sarima = function(ts, order = c(1,0,0), seasonal = c(0,0,0), xreg = NULL,
period = 0, chains = 4, iter = 2000, warmup = floor(iter/2),
adapt.delta = 0.9, tree.depth = 10, prior_mu0 = NULL,
prior_sigma0 = NULL, prior_ar = NULL,
prior_ma = NULL, prior_sar = NULL, prior_sma = NULL,
prior_breg = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Sarima(ts = ts, order = order, seasonal = seasonal,
xreg = xreg, period = period, series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat,par = "mu0",dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat,par = "ar",dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat,par = "ma",dist = prior_ma)
if(!is.null(prior_sar)) dat = set_prior(model = dat,par = "sar",dist = prior_sar)
if(!is.null(prior_sma)) dat = set_prior(model = dat,par = "sma",dist = prior_sma)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
# Fitting the Sarima model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Fitting for a GARCH(s,k,h) model.
#'
#' Fitting a \code{GARCH(s,k,h)} model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param order a vector of length three specifying the GARCH model. The three
#' components `c(s, k, h)` are the ARCH order, the GARCH order, and the MGARCH
#' orders respectively.
#' @param arma a vector of length two with the ARMA model configuration. The two
#' components `c(p, q)` are the AR, and the MA orders respectively.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a bool value to specify for a generalized t-student GARCH model.
#' @param asym a string value for the asymmetric function for an asymmetric
#' GARCH process. By default, the value \code{"none"} for standard GARCH process.
#' \code{"logit"} option a specifies a logistic function for asymmetry, and option
#' \code{"exp"} defines an exponential function.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in an
#' ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARIMA model. By default, sets a `normal(0,0.5)` priors.
#' @param prior_arch The prior distribution for the ARCH parameters in a GARCH
#' model. By default, sets `normal(0,0.5)` priors.
#' @param prior_garch The prior distribution for the GARCH parameters in a GARCH
#' model. By default, sets `normal(0,0.5)` priors.
#' @param prior_mgarch The prior distribution for the mean GARCH parameters in a
#' GARCH model. By default, sets `normal(0,0.5)` priors.
#' @param prior_breg The prior distribution for the regression coefficient
#' parameters in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param prior_df The prior distribution for the degree freedom parameters in a
#' t-student innovations GARCH model. By default, sets a `gamma(2,0.1)` prior.
#' @param prior_gamma The prior distribution for the asymmetric parameters in
#' MGARCH model. By default, sets `normal(0,0.5)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' By default the \code{garch()} function generates a GARCH(1,1) model. The
#' \code{genT = TRUE} option defines a t-student innovations GARCH model
#' (see Ardia (2010)). For Asymmetric GARCH models use the
#' option \code{asym} for specify the asymmetric functions, see Fonseca,
#' et. al (2019) for more details.
#'
#' The default priors used in a GARCH(s,k,h) model are:
#'
#' \itemize{
#' \item{ar ~ normal(0,0.5)}
#' \item{ma ~ normal(0,0.5)}
#' \item{mu0 ~ t-student(0,2.5,6)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{arch ~ normal(0,0.5)}
#' \item{garch ~ normal(0,0.5)}
#' \item{mgarch ~ normal(0,0.5)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted GARCH model.
#'
#' @export
#' @importFrom stats as.ts time
#'
#' @references
#' Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of
#' the Variance of United Kingdom Inflation. \emph{Econometrica}, 50(4), 987-1007.
#' \code{url: http://www.jstor.org/stable/1912773}.
#'
#' Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity.
#' \emph{Journal of Econometrics}. 31(3), 307-327.
#' \code{doi: https://doi.org/10.1016/0304-4076(86)90063-1}.
#'
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' Ardia, D. and Hoogerheide, L. (2010). Bayesian Estimation of the GARCH(1,1) Model
#' with Student-t Innovations. \emph{The R Journal}. 2(7), 41-47.
#' \code{doi: 10.32614/RJ-2010-014}.
#'
#' @seealso \code{Sarima}, \code{auto.arima}, and \code{set_prior}.
#'
#' @examples
#' \donttest{
#' # Declaring a garch(1,1) model for the ipc data.
#' sf1 = stan_garch(ipc,order = c(1,1,0),iter = 500,chains = 1)
#'
#' # Declaring a t-student M-GARCH(2,3,1)-ARMA(1,1) process for the ipc data.
#' sf2 = stan_garch(ipc,order = c(2,3,1),arma = c(1,1),genT = TRUE,iter = 500,chains = 1)
#' }
#'
stan_garch = function(ts,order = c(1,1,0), arma = c(0,0), xreg = NULL, genT = FALSE,
asym = "none", chains = 4, iter = 2000, warmup = floor(iter/2),
adapt.delta = 0.9, tree.depth = 10, prior_mu0 = NULL,
prior_sigma0 = NULL, prior_ar = NULL, prior_ma = NULL,
prior_mgarch = NULL, prior_arch = NULL, prior_garch = NULL,
prior_breg = NULL, prior_gamma = NULL, prior_df = NULL,
series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = garch(ts = ts, order = order, arma = arma, xreg = xreg, genT = genT,
asym = asym, series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat, par = "mu0", dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat, par = "sigma0", dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat, par = "ar", dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat, par = "ma", dist = prior_ma)
if(!is.null(prior_breg)) dat = set_prior(model = dat, par = "breg", dist = prior_breg)
if(!is.null(prior_arch)) dat = set_prior(model = dat, par = "arch", dist = prior_arch)
if(!is.null(prior_garch)) dat = set_prior(model = dat, par = "garch", dist = prior_garch)
if(!is.null(prior_mgarch)) dat = set_prior(model = dat ,par = "mgarch", dist = prior_mgarch)
if(!is.null(prior_df)) dat = set_prior(model = dat, par = "df", dist = prior_df)
if(!is.null(prior_gamma)) dat = set_prior(model = dat, par = "gamma", dist = prior_gamma)
# Fitting the garch model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Naive and Random Walk models.
#'
#' Naive is the model constructor for a random walk model applied to \code{y}.
#' This is equivalent to an ARIMA(0,1,0) model. \code{naive()} is simply a wrapper
#' to maintain forecast package similitude. \code{seasonal} returns the model
#' constructor for a seasonal random walk equivalent to an ARIMA(0,0,0)(0,1,0)m
#' model where m is the seasonal period.
#'
#' The random walk with drift model is
#' \deqn{Y_t = mu_0 + Y_{t-1} + epsilon_t}{Y[t]= mu_0 +Y[t-1] + epsilon[t]}
#' where \eqn{epsilon_t}{epsilon[t]} is a normal iid error.
#'
#' The seasonal naive model is
#' \deqn{Y_t = mu_0 + Y_{t-m} + epsilon_t}{Y[t]= mu_0 +Y[t-m] + epsilon[t]}
#' where \eqn{epsilon_t}{epsilon[t]} is a normal iid error.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param seasonal a Boolean value for select a seasonal random walk instead.
#' @param m an optional integer value for the seasonal period.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm-up iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @return A \code{varstan} object with the fitted naive Random Walk model.
#'
#' @author Asael Alonzo Matamoros
#'
#' @seealso \code{Sarima}.
#' @export
#'
#' @references
#'
#' Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
#' forecast package for \code{R}. \emph{Journal of Statistical Software}. 26(3),
#' 1-22.\code{doi: 10.18637/jss.v027.i03}.
#'
#' Box, G. E. P. and Jenkins, G.M. (1978). Time series analysis: Forecasting and
#' control. San Francisco: Holden-Day. \emph{Biometrika}, 60(2), 297-303.
#' \code{doi:10.1093/biomet/65.2.297}.
#'
#' Kennedy, P. (1992). Forecasting with dynamic regression models: Alan Pankratz, 1991.
#' \emph{International Journal of Forecasting}. 8(4), 647-648.
#' \code{url: https://EconPapers.repec.org/RePEc:eee:intfor:v:8:y:1992:i:4:p:647-648}.
#'
#' @examples
#' \donttest{
#' # A seasonal Random-walk model.
#' sf1 = stan_naive(birth,seasonal = TRUE,iter = 500,chains = 1)
#' }
#'
stan_naive = function(ts, seasonal = FALSE, m = 0, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_mu0 = NULL, prior_sigma0 = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = naive(ts = ts,seasonal = seasonal,m = m)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat, par = "mu0", dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat, par = "sigma0", dist = prior_sigma0)
# Fitting the naive model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth, ...)
return(sf1)
}
#' Fitting a Stochastic volatility model.
#'
#' Fitting a Stochastic Volatility model (SVM) in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param arma Optionally, a specification of the ARMA model,same
#' as order parameter: the two components (p, q) are the AR order,and
#' the MA order.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_mu0 The prior distribution for the location parameter in an
#' ARIMA model. By default, sets `student(7,0,1)` prior.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_ar The prior distribution for the auto-regressive parameters in
#' an ARMA model. By default, sets `normal(0,0.5)` priors.
#' @param prior_ma The prior distribution for the moving average parameters in
#' an ARMA model. By default, sets the `normal(0,0.5)` priors.
#' @param prior_alpha The prior distribution for the auto-regressive parameters in
#' a SVM model. By default, set a `normal(0, 0.5)` prior.
#' @param prior_beta The prior distribution for the exponential intercept parameter
#' in a SVM model. By default, uses a `normal(0,0.5)` prior.
#' @param prior_breg The prior distribution for the regression coefficient parameters
#' in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @return A \code{varstan} object with the fitted SVM model.
#'
#' @author Asael Alonzo Matamoros
#'
#' @export
#'
#' @references
#' Sangjoon,K. and Shephard, N. and Chib.S (1998). Stochastic Volatility: Likelihood
#' Inference and Comparison with ARCH Models. \emph{Review of Economic Studies}.
#' 65(1), 361-93. \code{url: https://www.jstor.org/stable/2566931}.
#'
#' Tsay, R (2010). Analysis of Financial Time Series.
#' \emph{Wiley-Interscience}. 978-0470414354, second edition.
#'
#' Shumway, R.H. and Stoffer, D.S. (2010).Time Series Analysis and Its
#' Applications: With R Examples. \emph{Springer Texts in Statistics}.
#' isbn: 9781441978646. First edition.
#'
#' @seealso \code{garch}, and \code{set_prior}
#'
#' @examples
#' \donttest{
#' # Declares a SVM model for the IPC data
#' sf1 = stan_SVM(ipc,arma = c(1,1),iter = 500,chains = 1)
#' }
#'
stan_SVM = function(ts, arma = c(0,0), xreg = NULL, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_mu0 = NULL, prior_sigma0 = NULL, prior_ar = NULL,
prior_ma = NULL, prior_alpha = NULL, prior_beta = NULL,
prior_breg = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = SVM(ts = ts,arma = arma,xreg = xreg,series.name = sn)
# Priors selection
if(!is.null(prior_mu0)) dat = set_prior(model = dat,par = "mu0",dist = prior_mu0)
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_ar)) dat = set_prior(model = dat,par = "ar",dist = prior_ar)
if(!is.null(prior_ma)) dat = set_prior(model = dat,par = "ma",dist = prior_ma)
if(!is.null(prior_alpha)) dat = set_prior(model = dat,par = "alpha",dist = prior_alpha)
if(!is.null(prior_beta)) dat = set_prior(model = dat,par = "beta",dist = prior_beta)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
# Fitting the SVM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting an Additive linear State space model.
#'
#' Fitting an Additive linear State space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param trend a bool value to specify a trend local level model. By default
#' is \code{FALSE}.
#' @param damped a boolvalue to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param seasonal a bool value to specify a seasonal local level model. By default
#' is \code{FALSE}.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series by
#' default the value frequency(ts) is used.
#' @param genT a bool value to specify for a generalized t-student SSM model.
#' @param chains an integer of the number of Markov Chains chains to be run. By
#' default, \code{chains = 4}.
#' @param iter an integer of total iterations per chain including the warm-up. By
#' default, \code{iter = 2000}.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm-up iteration should not be larger than \code{iter}.By default,
#' \code{warmup = iter/2}.
#' @param adapt.delta an optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default, is 0.9.
#' @param tree.depth an integer of the maximum depth of the trees evaluated
#' during each iteration. By default, is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an
#' ARIMA model. By default, declares a `student(7,0,1)` prior.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default, sets a `normal(0,0.5)` prior.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default, sets a `normal(0,0.5)` prior.
#' @param prior_damped The prior distribution for the damped trend parameter in a
#' SSM model. By default, sets a `normal(0,0.5)` prior.
#' @param prior_seasonal The prior distribution for the seasonal parameter in a
#' SSM model. By default, sets a `normal(0,0.5)` prior.
#' @param prior_level1 The prior distribution for the initial level parameter in
#' a SSM model. By default, sets a `student(6,0,2.5)` prior.
#' @param prior_trend1 The prior distribution for the initial trend parameter in
#' a SSM model. By default, sets a `student(6,0,2.5)` prior.
#' @param prior_seasonal1 The prior distribution for the initial seasonal parameters
#' in a SSM model. The prior is specified for the first m seasonal parameters,
#' where `m` is the periodicity of the defined time series. By default, sets a
#' `normal(0,0.5)` prior.
#' @param prior_breg The prior distribution for the regression coefficient parameters
#' in an ARIMAX model. By default, sets `student(7,0,1)` priors.
#' @param prior_df The prior distribution for the degree freedom parameters in a
#' t-student innovations SSM model. By default, sets a `gamma(2,0.1)` prior
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' By default the \code{ssm()} function generates a local-level, `ets("A","N","N")`,
#' or exponential smoothing model from the \pkg{forecast} package. When
#' \code{trend = TRUE} the SSM transforms into a local-trend, `ets("A","A","N")`,
#' or the equivalent Holt model. For damped trend models set \code{damped = TRUE}.
#' If \code{seasonal = TRUE}, the model is a seasonal local level model, or
#' `ets("A","N","A")` model. Finally, the Holt-Winters method (`ets("A","A","A")`)
#' is obtained by setting both \code{Trend = TRUE} and \code{seasonal = TRUE}.
#'
#' The \code{genT = TRUE} option generates a t-student innovations SSM model. For
#' a detailed explanation, check Ardia (2010); or Fonseca, et. al (2019).
#'
#' The default priors used in a `ssm` model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{Seasonal ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{seasonal1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{Sarima}, \code{auto.arima}, \code{set_prior}, and \code{garch}.
#'
#' @examples
#' \donttest{
#' # Declaring a local level model for the ipc data.
#' sf1 = stan_ssm(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt model for the ipc data.
#' sf2 = stan_ssm(ipc,trend = TRUE,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_ssm = function(ts, trend = FALSE, damped = FALSE, seasonal = FALSE, xreg = NULL,
period = 0, genT = FALSE, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_trend = NULL, prior_trend1 = NULL, prior_damped = NULL,
prior_seasonal = NULL, prior_seasonal1 = NULL, prior_breg = NULL,
prior_df = NULL, series.name = NULL, ...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = ssm(ts = ts, trend = trend, damped = damped, seasonal = seasonal, xreg = xreg,
period = period, genT = genT, series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_seasonal)) dat = set_prior(model = dat,par = "seasonal",dist = prior_seasonal)
if(!is.null(prior_seasonal1)) dat = set_prior(model = dat,par = "seasonal1",dist = prior_seasonal1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting a Local level state-space model.
#'
#' Fitting a Local level state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup a positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a Local_level( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted Local Level model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}.
#'
#' @examples
#' \donttest{
#' # Declaring a local level model for the ipc data.
#' sf1 = stan_LocalLevel(ipc,iter = 500,chains = 1)
#' }
#'
stan_LocalLevel = function(ts, xreg = NULL, genT = FALSE, chains = 4, iter = 2000,
warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = LocalLevel(ts = ts,xreg = xreg,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting an Holt state-space model.
#'
#' Fitting an Holt state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param damped a boolean value to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup A positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warm up should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_damped The prior distribution for the damped trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_trend1 The prior distribution for the initial trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a ssm( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return a \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}.
#'
#' @examples
#' \donttest{
#' # Declaring a Holt model for the ipc data.
#' sf1 = stan_Holt(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt damped trend model for the ipc data.
#' sf2 = stan_Holt(ipc,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_Holt = function(ts, damped = FALSE, xreg = NULL, genT = FALSE, chains = 4,
iter = 2000, warmup = floor(iter/2), adapt.delta = 0.9, tree.depth = 10,
prior_sigma0 = NULL, prior_level = NULL, prior_level1 = NULL,
prior_trend = NULL, prior_trend1 = NULL, prior_damped = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Holt(ts = ts,damped = damped,xreg = xreg,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
#' Fitting a Holt-Winters state-space model.
#'
#' Fitting a Holt-Winters state-space model in \pkg{Stan}.
#'
#' The function returns a \code{varstan} object with the fitted model.
#'
#' @param ts a numeric or ts object with the univariate time series.
#' @param damped a boolean value to specify a damped trend local level model. By default
#' is \code{FALSE}. If \code{trend} option is \code{FALSE} then \code{damped} is set to
#' \code{FALSE} automatically.
#' @param xreg Optionally, a numerical matrix of external regressors,
#' which must have the same number of rows as ts. It should not be a data frame.
#' @param period an integer specifying the periodicity of the time series by
#' default the value frequency(ts) is used.
#' @param genT a boolean value to specify for a generalized t-student SSM model.
#' @param chains An integer of the number of Markov Chains chains to be run,
#' by default 4 chains are run.
#' @param iter An integer of total iterations per chain including the warm-up,
#' by default the number of iterations are 2000.
#' @param warmup A positive integer specifying number of warm-up (aka burn-in)
#' iterations. This also specifies the number of iterations used for step-size
#' adaptation, so warm-up samples should not be used for inference. The number
#' of warmup should not be larger than \code{iter} and the default is
#' \code{iter/2}.
#' @param adapt.delta An optional real value between 0 and 1, the thin of the jumps
#' in a HMC method. By default is 0.9.
#' @param tree.depth An integer of the maximum depth of the trees evaluated
#' during each iteration. By default is 10.
#' @param prior_sigma0 The prior distribution for the scale parameter in an SSM model. By default
#' the value is set \code{NULL}, then the default student(7,0,1) prior is used.
#' @param prior_level The prior distribution for the level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_trend The prior distribution for the trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_damped The prior distribution for the damped trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_seasonal The prior distribution for the seasonal parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default normal(0,0.5) priors are used.
#' @param prior_level1 The prior distribution for the initial level parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_trend1 The prior distribution for the initial trend parameter in a SSM model.
#' By default the value is set \code{NULL}, then the default student(6,0,2.5) priors are used.
#' @param prior_seasonal1 The prior distribution for the initial seasonal parameters in a SSM model.
#' The prior is specified for the first m seasonal parameters, where m is the periodicity of the
#' defined time series. By default the value is set \code{NULL}, then the default normal(0,0.5) priors
#' are used.
#' @param prior_breg The prior distribution for the regression coefficient parameters in a
#' ARMAX model. By default the value is set \code{NULL}, then the default student(7,0,1) priors
#' are used.
#' @param prior_df The prior distribution for the degree freedom parameters in a t-student innovations
#' SSM model. By default the value is set \code{NULL}, then the default gamma(2,0.1) priors
#' are used.
#' @param series.name an optional string vector with the series names.
#' @param ... Further arguments passed to \code{varstan} function.
#'
#' @details
#' When \code{genT} option is \code{TRUE} a t-student innovations ssm model (see Ardia (2010)) is generated
#' see Fonseca, et. al (2019) for more details.
#'
#' The default priors used in a ssm( ) model are:
#'
#' \itemize{
#' \item{level ~ normal(0,0.5)}
#' \item{Trend ~ normal(0,0.5)}
#' \item{damped~ normal(0,0.5)}
#' \item{Seasonal ~ normal(0,0.5)}
#' \item{sigma0 ~ t-student(0,1,7)}
#' \item{level1 ~ normal(0,1)}
#' \item{trend1 ~ normal(0,1)}
#' \item{seasonal1 ~ normal(0,1)}
#' \item{dfv ~ gamma(2,0.1)}
#' \item{breg ~ t-student(0,2.5,6)}
#' }
#'
#' For changing the default prior use the function \code{set_prior()}.
#'
#' @author Asael Alonzo Matamoros.
#'
#' @return A \code{varstan} object with the fitted SSM model.
#' @export
#' @importFrom stats as.ts time frequency
#'
#' @references
#' Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of
#' degrees of freedom estimation in the Asymmetric GARCH model with Student-t
#' Innovations. \emph{arXiv} \code{doi: arXiv: 1910.01398}.
#'
#' @seealso \code{ssm}
#'
#' @examples
#' \donttest{
#' # Declaring a Holt-Winters model for the ipc data.
#' sf1 = stan_Hw(ipc,iter = 500,chains = 1)
#'
#' # Declaring a Holt-Winters damped trend model for the ipc data.
#' sf2 = stan_ssm(ipc,damped = TRUE,iter = 500,chains = 1)
#' }
#'
stan_Hw = function(ts,damped = FALSE, xreg = NULL, period = 0, genT = FALSE,
chains = 4, iter = 2000, warmup = floor(iter/2), adapt.delta = 0.9,
tree.depth = 10, prior_sigma0 = NULL, prior_level = NULL,
prior_level1 = NULL, prior_trend = NULL, prior_trend1 = NULL,
prior_damped = NULL,prior_seasonal = NULL, prior_seasonal1 = NULL,
prior_breg = NULL, prior_df = NULL, series.name = NULL,...){
if(is.null(series.name))
sn = deparse(substitute(ts))
else
sn = as.character(series.name)
dat = Hw(ts = ts,damped = damped,xreg = xreg,period = period,genT = genT,series.name = sn)
# Priors selection
if(!is.null(prior_sigma0)) dat = set_prior(model = dat,par = "sigma0",dist = prior_sigma0)
if(!is.null(prior_level)) dat = set_prior(model = dat,par = "level",dist = prior_level)
if(!is.null(prior_level1)) dat = set_prior(model = dat,par = "level1",dist = prior_level1)
if(!is.null(prior_trend)) dat = set_prior(model = dat,par = "trend",dist = prior_trend)
if(!is.null(prior_trend1)) dat = set_prior(model = dat,par = "trend1",dist = prior_trend1)
if(!is.null(prior_damped)) dat = set_prior(model = dat,par = "damped",dist = prior_damped)
if(!is.null(prior_seasonal)) dat = set_prior(model = dat,par = "seasonal",dist = prior_seasonal)
if(!is.null(prior_seasonal1)) dat = set_prior(model = dat,par = "seasonal1",dist = prior_seasonal1)
if(!is.null(prior_breg)) dat = set_prior(model = dat,par = "breg",dist = prior_breg)
if(!is.null(prior_df)) dat = set_prior(model = dat,par = "df",dist = prior_df)
# Fitting the SSM model.
sf1 = varstan(model = dat,
chains = chains,
iter = iter,
warmup = warmup,
adapt.delta = adapt.delta,
tree.depth = tree.depth,...)
return(sf1)
}
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