R/tramoseats_spec.R

In RJDemetra: Interface to 'JDemetra+' Seasonal Adjustment Software

#' TRAMO-SEATS model specification
#'
#' @description
#'
#' Function to create (and/or modify) a \code{c("SA_spec", "TRAMO_SEATS")} class object with the SA model specification
#' for the TRAMO-SEATS method. It can be done from a pre-defined 'JDemetra+' model specification (a \code{character}),
#' a previous specification (\code{c("SA_spec", "TRAMO_SEATS")} object) or a seasonal adjustment model (\code{c("SA", "TRAMO_SEATS")} object).
#'
#' @param spec a TRAMO-SEATS model specification. It can be the 'JDemetra+' name (\code{character}) of a predefined TRAMO-SEATS model specification
#' (see \emph{Details}), an object of class \code{c("SA_spec","TRAMO_SEATS")} or an object of class \code{c("SA", "TRAMO_SEATS")}.
#' The default is \code{"RSAfull"}.
#'
#' @inheritParams  regarima_spec_tramoseats
#' @inheritParams  x13_spec
#' @param seats.predictionLength integer: the number of forecasts used in the decomposition. Negative values correspond to number of years. Default=-1.
#'
#' @param seats.approx character: the approximation mode. When the ARIMA model estimated by TRAMO does not accept an admissible decomposition, SEATS: \code{"None"} - performs an approximation; \code{"Legacy"} - replaces the model with a decomposable one; \code{"Noisy"} - estimates a new model by adding a white noise to the non-admissible model estimated by TRAMO. Default="Legacy".
#' @param seats.trendBoundary numeric: the trend boundary. The boundary beyond which an AR root is integrated in the trend component.
#' If the modulus of the inverse real root is greater than the trend boundary, the AR root is integrated in the trend component.
#' Below this value, the root is integrated in the transitory component. Possible values [0,1]. Default=0.5.
#' @param seats.seasdBoundary numeric: the seasonal boundary. The boundary beyond which a negative AR root is integrated in the seasonal component.  If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
#' @param seats.seasdBoundary1 numeric: the seasonal boundary (unique). The boundary beyond which a negative AR root is integrated
#' in the seasonal component, when the root is the unique seasonal root. If the modulus of the inverse negative real root is greater (or equal) than Seasonal boundary, the AR root is integrated into the seasonal component. Otherwise the root is integrated into the trend or transitory component. Possible values [0,1]. Default=0.8.
#' @param seats.seasTol numeric: the seasonal tolerance. The tolerance (measured in degrees) to allocate the AR non-real roots
#' to the seasonal component (if the modulus of the inverse complex AR root is greater than the trend boundary
#' and the frequency of this root differs from one of the seasonal frequencies by less than Seasonal tolerance)
#' or the transitory component (otherwise). Possible values in [0,10]. Default value 2.
#' @param seats.maBoundary numeric: the MA unit root boundary. When the modulus of an estimated MA root falls in the range (xl, 1),
#' it is set to xl. Possible values [0.9,1]. Default=0.95.
#' @param seats.method character: the estimation method for the unobserved components. The choice can be made from:
#' \itemize{
#' \item \code{"Burman"}: the default value. May result in a significant underestimation of the components' standard deviation,
#' as it may become numerically unstable when some roots of the MA polynomial are near 1;
#' \item \code{"KalmanSmoother"}: it is not disturbed by the (quasi-) unit roots in MA;
#' \item \code{"McElroyMatrix"}: it has the same stability issues as the Burman's algorithm.
#' }
#'
#' @details
#'
#' The available predefined 'JDemetra+' model specifications are described in the table below:
#'
#' \tabular{rrrrrrrr}{
#' \strong{Identifier} |\tab \strong{Log/level detection} |\tab \strong{Outliers detection} |\tab \strong{Calendar effects} |\tab \strong{ARIMA}\cr
#' RSA0 |\tab \emph{NA} |\tab \emph{NA} |\tab \emph{NA} |\tab Airline(+mean)\cr
#' RSA1 |\tab automatic |\tab AO/LS/TC |\tab \emph{NA} |\tab Airline(+mean)\cr
#' RSA2 |\tab automatic |\tab AO/LS/TC |\tab 2 td vars + Easter |\tab Airline(+mean)\cr
#' RSA3 |\tab automatic |\tab AO/LS/TC |\tab \emph{NA} |\tab automatic\cr
#' RSA4 |\tab automatic |\tab AO/LS/TC |\tab 2 td vars + Easter |\tab automatic\cr
#' RSA5 |\tab automatic |\tab AO/LS/TC |\tab 7 td vars + Easter |\tab automatic\cr
#' RSAfull |\tab automatic |\tab AO/LS/TC |\tab automatic |\tab automatic
#' }
#' @return
#'
#' A two-element list of class \code{c("SA_spec", "TRAMO_SEATS")}, containing:
#' (1) an object of class \code{c("regarima_spec", "TRAMO_SEATS")} with the RegARIMA model specification,
#' (2) an object of class \code{c("seats_spec", "data.frame")} with the SEATS algorithm specification.
#' Each component refers to a different part of the SA model specification, mirroring the arguments of the function
#' (for details see the function arguments in the description).
#' Each lowest-level component (except span, pre-specified outliers, user-defined variables and pre-specified ARMA coefficients)
#' is structured as a data frame with columns denoting different variables of the model specification and rows referring to:
#' \itemize{
#' \item first row: the base specification, as provided within the argument \code{spec};
#' \item second row: user modifications as specified by the remaining arguments of the function (e.g.: \code{arima.d});
#' \item and third row: the final model specification.
#'
#' The final specification (third row) shall include user modifications (row two) unless they were wrongly specified.
#' The pre-specified outliers, user-defined variables and pre-specified ARMA coefficients consist of a list of
#' \code{Predefined} (base model specification) and \code{Final} values.
#'
#' \item \code{regarima}: an object of class \code{c("regarima_spec", "TRAMO_SEATS")}. See \emph{Value} of the function \code{\link{regarima_spec_tramoseats}}.
#'
#' \item \code{seats}: a data.frame of class \code{c("seats_spec", "data.frame")}, containing the \emph{seats} variables in line with
#' the names of the arguments variables. The final values can also be accessed with the function \code{\link{s_seats}}.
#'}
#' @references
#' More information and examples related to 'JDemetra+' features in the online documentation:
#' \url{https://jdemetra-new-documentation.netlify.app/}
#'
#'
#' @seealso \code{\link{tramoseats}}
#'
#' @examples\donttest{
#' myseries <- ipi_c_eu[, "FR"]
#' myspec1 <- tramoseats_spec(spec = c("RSAfull"))
#' mysa1 <- tramoseats(myseries, spec = myspec1)
#'
#' # To modify a pre-specified model specification
#' myspec2 <- tramoseats_spec(spec = "RSAfull", tradingdays.mauto = "Unused",
#'                            tradingdays.option = "WorkingDays",
#'                            easter.type = "Standard",
#'                            automdl.enabled = FALSE, arima.mu = TRUE)
#' mysa2 <- tramoseats(myseries, spec = myspec2)
#'
#' # To modify the model specification of a "SA" object
#' myspec3 <- tramoseats_spec(mysa1, tradingdays.mauto = "Unused",
#'                            tradingdays.option = "WorkingDays",
#'                            easter.type = "Standard", automdl.enabled = FALSE, arima.mu = TRUE)
#' mysa3 <- tramoseats(myseries, myspec3)
#'
#' # To modify the model specification of a "SA_spec" object
#' myspec4 <- tramoseats_spec(myspec1, tradingdays.mauto = "Unused",
#'                            tradingdays.option = "WorkingDays",
#'                            easter.type = "Standard", automdl.enabled = FALSE, arima.mu = TRUE)
#' mysa4 <- tramoseats(myseries, myspec4)
#'
#' # Pre-specified outliers
#' myspec5 <- tramoseats_spec(spec = "RSAfull",
#'                            usrdef.outliersEnabled = TRUE,
#'                            usrdef.outliersType = c("LS", "LS"),
#'                            usrdef.outliersDate = c("2008-10-01", "2003-01-01"),
#'                            usrdef.outliersCoef = c(10,-8), transform.function = "None")
#' s_preOut(myspec5)
#' mysa5 <- tramoseats(myseries, myspec5)
#' mysa5
#' s_preOut(mysa5)
#'
#' # User-defined calendar regressors
#' var1 <- ts(rnorm(length(myseries))*10, start = start(myseries), frequency = 12)
#' var2 <- ts(rnorm(length(myseries))*100, start = start(myseries), frequency = 12)
#' var<- ts.union(var1, var2)
#'
#' myspec6 <- tramoseats_spec(spec = "RSAfull", tradingdays.option = "UserDefined",
#'                            usrdef.varEnabled = TRUE, usrdef.var = var,
#'                            usrdef.varType = c("Calendar", "Calendar"))
#' s_preVar(myspec6)
#' mysa6 <- tramoseats(myseries, myspec6)
#'
#' myspec7 <- tramoseats_spec(spec = "RSAfull", usrdef.varEnabled = TRUE,
#'                            usrdef.var = var, usrdef.varCoef = c(17,-1),
#'                            transform.function = "None")
#' mysa7 <- tramoseats(myseries, myspec7)
#'
#' # Pre-specified ARMA coefficients
#' myspec8 <- tramoseats_spec(spec = "RSAfull",
#'                            arima.coefEnabled = TRUE, automdl.enabled = FALSE,
#'                            arima.p = 2, arima.q = 0,
#'                            arima.bp = 1, arima.bq = 1,
#'                            arima.coef = c(-0.12, -0.12, -0.3, -0.99),
#'                            arima.coefType = rep("Fixed", 4))
#' mysa8 <- tramoseats(myseries, myspec8)
#' mysa8
#' s_arimaCoef(myspec8)
#' s_arimaCoef(mysa8)
#' }
#' @export
tramoseats_spec <- function(
    spec = c("RSAfull", "RSA0", "RSA1", "RSA2", "RSA3", "RSA4", "RSA5"),
    preliminary.check = NA,
    estimate.from = NA_character_,
    estimate.to = NA_character_,
    estimate.first = NA_integer_,
    estimate.last = NA_integer_,
    estimate.exclFirst = NA_integer_,
    estimate.exclLast = NA_integer_,
    estimate.tol = NA_integer_,
    estimate.eml = NA,
    estimate.urfinal = NA_integer_,
    transform.function = c(NA, "Auto", "None", "Log"),
    transform.fct = NA_integer_,
    usrdef.outliersEnabled = NA,
    usrdef.outliersType = NA,
    usrdef.outliersDate = NA,
    usrdef.outliersCoef = NA,
    usrdef.varEnabled = NA,
    usrdef.var = NA,
    usrdef.varType = NA,
    usrdef.varCoef = NA,
    tradingdays.mauto = c(NA, "Unused", "FTest", "WaldTest"),
    tradingdays.pftd = NA_integer_,
    tradingdays.option = c(NA, "TradingDays", "WorkingDays", "UserDefined", "None"),
    tradingdays.leapyear = NA,
    tradingdays.stocktd = NA_integer_,
    tradingdays.test = c(NA, "Separate_T", "Joint_F", "None"),
    easter.type = c(NA, "Unused", "Standard", "IncludeEaster", "IncludeEasterMonday"),
    easter.julian = NA,
    easter.duration = NA_integer_,
    easter.test = NA,
    outlier.enabled = NA,
    outlier.from = NA_character_,
    outlier.to = NA_character_,
    outlier.first = NA_integer_,
    outlier.last = NA_integer_,
    outlier.exclFirst = NA_integer_,
    outlier.exclLast = NA_integer_,
    outlier.ao = NA,
    outlier.tc = NA,
    outlier.ls = NA,
    outlier.so = NA,
    outlier.usedefcv = NA,
    outlier.cv = NA_integer_,
    outlier.eml = NA,
    outlier.tcrate = NA_integer_,
    automdl.enabled = NA,
    automdl.acceptdefault = NA,
    automdl.cancel = NA_integer_,
    automdl.ub1 = NA_integer_,
    automdl.ub2 = NA_integer_,
    automdl.armalimit = NA_integer_,
    automdl.reducecv = NA_integer_,
    automdl.ljungboxlimit = NA_integer_,
    automdl.compare = NA,
    arima.mu = NA,
    arima.p = NA_integer_,
    arima.d = NA_integer_,
    arima.q = NA_integer_,
    arima.bp = NA_integer_,
    arima.bd = NA_integer_,
    arima.bq = NA_integer_,
    arima.coefEnabled = NA,
    arima.coef= NA,
    arima.coefType = NA,
    fcst.horizon = NA_integer_,
    seats.predictionLength = NA_integer_,
    seats.approx = c(NA, "None", "Legacy", "Noisy"),
    seats.trendBoundary = NA_integer_,
    seats.seasdBoundary = NA_integer_,
    seats.seasdBoundary1 = NA_integer_,
    seats.seasTol = NA_integer_,
    seats.maBoundary = NA_integer_,
    seats.method = c(NA, "Burman", "KalmanSmoother", "McElroyMatrix"),
    benchmarking.enabled = NA,
    benchmarking.target = c(NA, "Original", "CalendarAdjusted"),
    benchmarking.useforecast = NA,
    benchmarking.rho = NA_real_,
    benchmarking.lambda = NA_real_
                     ){
  UseMethod("tramoseats_spec", spec)
}
#' @export
tramoseats_spec.character <- function(
    spec = c("RSAfull", "RSA0", "RSA1", "RSA2", "RSA3", "RSA4", "RSA5"),
    preliminary.check = NA,
    estimate.from = NA_character_,
    estimate.to = NA_character_,
    estimate.first = NA_integer_,
    estimate.last = NA_integer_,
    estimate.exclFirst = NA_integer_,
    estimate.exclLast = NA_integer_,
    estimate.tol = NA_integer_,
    estimate.eml = NA,
    estimate.urfinal = NA_integer_,
    transform.function = c(NA, "Auto", "None", "Log"),
    transform.fct = NA_integer_,
    usrdef.outliersEnabled = NA,
    usrdef.outliersType = NA,
    usrdef.outliersDate = NA,
    usrdef.outliersCoef = NA,
    usrdef.varEnabled = NA,
    usrdef.var = NA,
    usrdef.varType = NA,
    usrdef.varCoef = NA,
    tradingdays.mauto = c(NA, "Unused", "FTest", "WaldTest"),
    tradingdays.pftd = NA_integer_,
    tradingdays.option = c(NA, "TradingDays", "WorkingDays", "UserDefined", "None"),
    tradingdays.leapyear = NA,
    tradingdays.stocktd = NA_integer_,
    tradingdays.test = c(NA, "Separate_T", "Joint_F", "None"),
    easter.type = c(NA, "Unused", "Standard", "IncludeEaster", "IncludeEasterMonday"),
    easter.julian = NA,
    easter.duration = NA_integer_,
    easter.test = NA,
    outlier.enabled = NA,
    outlier.from = NA_character_,
    outlier.to = NA_character_,
    outlier.first = NA_integer_,
    outlier.last = NA_integer_,
    outlier.exclFirst = NA_integer_,
    outlier.exclLast = NA_integer_,
    outlier.ao = NA,
    outlier.tc = NA,
    outlier.ls = NA,
    outlier.so = NA,
    outlier.usedefcv = NA,
    outlier.cv = NA_integer_,
    outlier.eml = NA,
    outlier.tcrate = NA_integer_,
    automdl.enabled = NA,
    automdl.acceptdefault = NA,
    automdl.cancel = NA_integer_,
    automdl.ub1 = NA_integer_,
    automdl.ub2 = NA_integer_,
    automdl.armalimit = NA_integer_,
    automdl.reducecv = NA_integer_,
    automdl.ljungboxlimit = NA_integer_,
    automdl.compare = NA,
    arima.mu = NA,
    arima.p = NA_integer_,
    arima.d = NA_integer_,
    arima.q = NA_integer_,
    arima.bp = NA_integer_,
    arima.bd = NA_integer_,
    arima.bq = NA_integer_,
    arima.coefEnabled = NA,
    arima.coef= NA,
    arima.coefType = NA,
    fcst.horizon = NA_integer_,
    seats.predictionLength = NA_integer_,
    seats.approx = c(NA, "None", "Legacy", "Noisy"),
    seats.trendBoundary = NA_integer_,
    seats.seasdBoundary = NA_integer_,
    seats.seasdBoundary1 = NA_integer_,
    seats.seasTol = NA_integer_,
    seats.maBoundary = NA_integer_,
    seats.method = c(NA, "Burman", "KalmanSmoother", "McElroyMatrix"),
    benchmarking.enabled = NA,
    benchmarking.target = c(NA, "Original", "CalendarAdjusted"),
    benchmarking.useforecast = NA,
    benchmarking.rho = NA_real_,
    benchmarking.lambda = NA_real_)
{
  spec <- match.arg(spec)
  reg_spec <- gsub("RSA", "TR", spec)
  regarima <-  regarima_spec_tramoseats(
    spec = reg_spec, preliminary.check = preliminary.check,
    estimate.from = estimate.from, estimate.to = estimate.to,
    estimate.first = estimate.first, estimate.last = estimate.last,
    estimate.exclFirst = estimate.exclFirst, estimate.exclLast = estimate.exclLast,
    estimate.tol = estimate.tol, estimate.eml = estimate.eml,
    estimate.urfinal = estimate.urfinal, transform.function = transform.function,
    transform.fct = transform.fct, usrdef.outliersEnabled = usrdef.outliersEnabled,
    usrdef.outliersType = usrdef.outliersType, usrdef.outliersDate = usrdef.outliersDate,
    usrdef.outliersCoef = usrdef.outliersCoef, usrdef.varEnabled = usrdef.varEnabled,
    usrdef.var = usrdef.var, usrdef.varType = usrdef.varType,
    usrdef.varCoef = usrdef.varCoef, tradingdays.mauto = tradingdays.mauto,
    tradingdays.pftd = tradingdays.pftd, tradingdays.option = tradingdays.option,
    tradingdays.leapyear = tradingdays.leapyear, tradingdays.stocktd = tradingdays.stocktd,
    tradingdays.test = tradingdays.test, easter.type = easter.type,
    easter.julian = easter.julian, easter.duration = easter.duration,
    easter.test = easter.test, outlier.enabled = outlier.enabled,
    outlier.from = outlier.from, outlier.to = outlier.to, outlier.first = outlier.first,
    outlier.last = outlier.last, outlier.exclFirst = outlier.exclFirst,
    outlier.exclLast = outlier.exclLast, outlier.ao = outlier.ao,
    outlier.tc = outlier.tc, outlier.ls = outlier.ls, outlier.so = outlier.so,
    outlier.usedefcv = outlier.usedefcv, outlier.cv = outlier.cv,
    outlier.eml = outlier.eml, outlier.tcrate = outlier.tcrate,
    automdl.enabled = automdl.enabled, automdl.acceptdefault = automdl.acceptdefault,
    automdl.cancel = automdl.cancel, automdl.ub1 = automdl.ub1,
    automdl.ub2 = automdl.ub2, automdl.armalimit = automdl.armalimit,
    automdl.reducecv = automdl.reducecv, automdl.ljungboxlimit = automdl.ljungboxlimit,
    automdl.compare = automdl.compare, arima.mu = arima.mu, arima.p = arima.p,
    arima.d = arima.d, arima.q = arima.q, arima.bp = arima.bp,
    arima.bd = arima.bd, arima.bq = arima.bq, arima.coefEnabled = arima.coefEnabled,
    arima.coef = arima.coef, arima.coefType = arima.coefType,
    fcst.horizon = fcst.horizon)

  seats <- seats_spec_def(
    spec = spec, seats.predictionLength = seats.predictionLength,
    seats.approx = seats.approx, seats.trendBoundary = seats.trendBoundary,
    seats.seasdBoundary = seats.seasdBoundary, seats.seasdBoundary1 = seats.seasdBoundary1,
    seats.seasTol = seats.seasTol, seats.maBoundary = seats.maBoundary,
    seats.method = seats.method)
  benchmarking <- benchmarking_spec_def(
    spec = spec,
    benchmarking.enabled = benchmarking.enabled,
    benchmarking.target = benchmarking.target,
    benchmarking.useforecast = benchmarking.useforecast,
    benchmarking.rho = benchmarking.rho,
    benchmarking.lambda = benchmarking.lambda)

  z <- list(regarima = regarima, seats = seats, benchmarking = benchmarking)
  class(z) <- c("SA_spec","TRAMO_SEATS")
  return(z)
}

seats_spec_def <- function(spec=c("RSAfull", "RSA0", "RSA1", "RSA2", "RSA3", "RSA4", "RSA5"),
                           seats.predictionLength = NA_integer_,
                          seats.approx = c(NA_character_,"None","Legacy","Noisy"),
                          seats.trendBoundary = NA_integer_,
                          seats.seasdBoundary = NA_integer_,
                          seats.seasdBoundary1 = NA_integer_,
                          seats.seasTol = NA_integer_,
                          seats.maBoundary = NA_integer_,
                          seats.method = c(NA_character_,"Burman","KalmanSmoother","McElroyMatrix"))
{
  spec <- match.arg(spec)
  seats.approx <- match.arg(seats.approx)
  seats.method <- match.arg(seats.method)

  list.numeric <- list("seats.predictionLength", "seats.trendBoundary",
                       "seats.seasdBoundary", "seats.seasdBoundary1",
                       "seats.seasTol", "seats.maBoundary")

  var.list<-list()
  for (i in 1:length(list.numeric)) {
    eval(parse(text=paste("if( !is.numeric(",list.numeric[i],")) {",list.numeric[i]," = NA; var.list=append(var.list,'",list.numeric[i],"')}",sep="")))
  }
  if (length(var.list)>0) {warning(paste("Variable(s)",deparse(as.character(var.list))," should be numeric. They are ignored."), call. = FALSE)}

  # modifed values
  seats <- do.call(data.frame, as.list(match.call()[c(-1,-2)]))
  # create the java object
  jrspec<-.jcall("jdr/spec/tramoseats/TramoSeatsSpec", "Ljdr/spec/tramoseats/TramoSeatsSpec;", "of", spec)
  rspec <- specSeats_jd2r(spec = jrspec)
  seats.spec <- do.call(data.frame, rspec)
  names(seats.spec) <- paste0("seats.",names(seats.spec))
  seats.mod <- rbind(seats.spec,seats,rep(NA,length(seats.spec)))
  z <- spec_seats(seats.mod)

  class(z) <- c("seats_spec","data.frame")
  return(z)
}
#' @export
tramoseats_spec.TRAMO_SEATS <- function(spec,
                            preliminary.check = NA,
                            estimate.from = NA_character_,
                            estimate.to = NA_character_,
                            estimate.first = NA_integer_,
                            estimate.last = NA_integer_,
                            estimate.exclFirst = NA_integer_,
                            estimate.exclLast = NA_integer_,
                            estimate.tol = NA_integer_,
                            estimate.eml = NA,
                            estimate.urfinal = NA_integer_,
                            transform.function = c(NA, "Auto", "None", "Log"),
                            transform.fct = NA_integer_,
                            usrdef.outliersEnabled = NA,
                            usrdef.outliersType = NA,
                            usrdef.outliersDate = NA,
                            usrdef.outliersCoef = NA,
                            usrdef.varEnabled = NA,
                            usrdef.var = NA,
                            usrdef.varType = NA,
                            usrdef.varCoef = NA,
                            tradingdays.mauto = c(NA, "Unused", "FTest", "WaldTest"),
                            tradingdays.pftd = NA_integer_,
                            tradingdays.option = c(NA, "TradingDays", "WorkingDays", "UserDefined", "None"),
                            tradingdays.leapyear = NA,
                            tradingdays.stocktd = NA_integer_,
                            tradingdays.test = c(NA, "Separate_T", "Joint_F", "None"),
                            easter.type = c(NA, "Unused", "Standard", "IncludeEaster", "IncludeEasterMonday"),
                            easter.julian = NA,
                            easter.duration = NA_integer_,
                            easter.test = NA,
                            outlier.enabled = NA,
                            outlier.from = NA_character_,
                            outlier.to = NA_character_,
                            outlier.first = NA_integer_,
                            outlier.last = NA_integer_,
                            outlier.exclFirst = NA_integer_,
                            outlier.exclLast = NA_integer_,
                            outlier.ao = NA,
                            outlier.tc = NA,
                            outlier.ls = NA,
                            outlier.so = NA,
                            outlier.usedefcv = NA,
                            outlier.cv = NA_integer_,
                            outlier.eml = NA,
                            outlier.tcrate = NA_integer_,
                            automdl.enabled = NA,
                            automdl.acceptdefault = NA,
                            automdl.cancel = NA_integer_,
                            automdl.ub1 = NA_integer_,
                            automdl.ub2 = NA_integer_,
                            automdl.armalimit = NA_integer_,
                            automdl.reducecv = NA_integer_,
                            automdl.ljungboxlimit = NA_integer_,
                            automdl.compare = NA,
                            arima.mu = NA,
                            arima.p = NA_integer_,
                            arima.d = NA_integer_,
                            arima.q = NA_integer_,
                            arima.bp = NA_integer_,
                            arima.bd = NA_integer_,
                            arima.bq = NA_integer_,
                            arima.coefEnabled = NA,
                            arima.coef= NA,
                            arima.coefType = NA,
                            fcst.horizon = NA_integer_,
                            seats.predictionLength = NA_integer_,
                            seats.approx = c(NA, "None", "Legacy", "Noisy"),
                            seats.trendBoundary = NA_integer_,
                            seats.seasdBoundary = NA_integer_,
                            seats.seasdBoundary1 = NA_integer_,
                            seats.seasTol = NA_integer_,
                            seats.maBoundary = NA_integer_,
                            seats.method = c(NA, "Burman", "KalmanSmoother", "McElroyMatrix"),
                            benchmarking.enabled = NA,
                            benchmarking.target = c(NA, "Original", "CalendarAdjusted"),
                            benchmarking.useforecast = NA,
                            benchmarking.rho = NA_real_,
                            benchmarking.lambda = NA_real_)
{
  if ( !inherits(spec, c("SA","SA_spec")))
    stop("The function must only be used with c(\"SA\",\"TRAMO_SEATS\") and c(\"SA_spec\",\"TRAMO_SEATS\") objects", call. = FALSE)

  regarima <- regarima_spec_tramoseats(
    spec = spec, preliminary.check = preliminary.check,
    estimate.from = estimate.from, estimate.to = estimate.to,
    estimate.first = estimate.first, estimate.last = estimate.last,
    estimate.exclFirst = estimate.exclFirst, estimate.exclLast = estimate.exclLast,
    estimate.tol = estimate.tol, estimate.eml = estimate.eml,
    estimate.urfinal = estimate.urfinal, transform.function = transform.function,
    transform.fct = transform.fct, usrdef.outliersEnabled = usrdef.outliersEnabled,
    usrdef.outliersType = usrdef.outliersType, usrdef.outliersDate = usrdef.outliersDate,
    usrdef.outliersCoef = usrdef.outliersCoef, usrdef.varEnabled = usrdef.varEnabled,
    usrdef.var = usrdef.var, usrdef.varType = usrdef.varType,
    usrdef.varCoef = usrdef.varCoef, tradingdays.mauto = tradingdays.mauto,
    tradingdays.pftd = tradingdays.pftd, tradingdays.option = tradingdays.option,
    tradingdays.leapyear = tradingdays.leapyear, tradingdays.stocktd = tradingdays.stocktd,
    tradingdays.test = tradingdays.test, easter.type = easter.type,
    easter.julian = easter.julian, easter.duration = easter.duration,
    easter.test = easter.test, outlier.enabled = outlier.enabled,
    outlier.from = outlier.from, outlier.to = outlier.to, outlier.first = outlier.first,
    outlier.last = outlier.last, outlier.exclFirst = outlier.exclFirst,
    outlier.exclLast = outlier.exclLast, outlier.ao = outlier.ao,
    outlier.tc = outlier.tc, outlier.ls = outlier.ls, outlier.so = outlier.so,
    outlier.usedefcv = outlier.usedefcv, outlier.cv = outlier.cv,
    outlier.eml = outlier.eml, outlier.tcrate = outlier.tcrate,
    automdl.enabled = automdl.enabled, automdl.acceptdefault = automdl.acceptdefault,
    automdl.cancel = automdl.cancel, automdl.ub1 = automdl.ub1,
    automdl.ub2 = automdl.ub2, automdl.armalimit = automdl.armalimit,
    automdl.reducecv = automdl.reducecv, automdl.ljungboxlimit = automdl.ljungboxlimit,
    automdl.compare = automdl.compare, arima.mu = arima.mu, arima.p = arima.p,
    arima.d = arima.d, arima.q = arima.q, arima.bp = arima.bp,
    arima.bd = arima.bd, arima.bq = arima.bq, arima.coefEnabled = arima.coefEnabled,
    arima.coef = arima.coef, arima.coefType = arima.coefType,
    fcst.horizon = fcst.horizon)

  seats <- seats_spec(
    spec = spec, seats.predictionLength = seats.predictionLength,
    seats.approx = seats.approx, seats.trendBoundary = seats.trendBoundary,
    seats.seasdBoundary = seats.seasdBoundary, seats.seasdBoundary1 = seats.seasdBoundary1,
    seats.seasTol = seats.seasTol, seats.maBoundary = seats.maBoundary,
    seats.method = seats.method)
  benchmarking <- benchmarking_spec(
    spec = spec,
    benchmarking.enabled = benchmarking.enabled,
    benchmarking.target = benchmarking.target, benchmarking.useforecast = benchmarking.useforecast,
    benchmarking.rho = benchmarking.rho, benchmarking.lambda = benchmarking.lambda)

  z <- list(regarima = regarima, seats = seats, benchmarking = benchmarking)
  class(z) <- c("SA_spec","TRAMO_SEATS")
  return(z)
}

seats_spec<- function(spec,
                      seats.predictionLength = NA_integer_,
                      seats.approx = c(NA_character_,"None","Legacy","Noisy"),
                      seats.trendBoundary = NA_integer_,
                      seats.seasdBoundary = NA_integer_,
                      seats.seasdBoundary1 = NA_integer_,
                      seats.seasTol = NA_integer_,
                      seats.maBoundary = NA_integer_,
                      seats.method = c(NA_character_,"Burman","KalmanSmoother","McElroyMatrix"))
{
  seats.approx <- match.arg(seats.approx)
  seats.method <- match.arg(seats.method)

  list.numeric <- list("seats.predictionLength","seats.trendBoundary",
                       "seats.seasdBoundary", "seats.seasdBoundary1",
                       "seats.seasTol", "seats.maBoundary")

  var.list<-list()
  for (i in 1:length(list.numeric)) {
    eval(parse(text=paste("if( !is.numeric(",list.numeric[i],")) {",list.numeric[i]," = NA; var.list=append(var.list,'",list.numeric[i],"')}",sep="")))
  }
  if (length(var.list)>0) {warning(paste("Variable(s)",deparse(as.character(var.list))," should be numeric. They are ignored."), call. = FALSE)}

  # modified values
  seats <- do.call(data.frame, as.list(match.call()[c(-1,-2)]))
  seats.spec <- s_seats(spec)
  seats.mod <- rbind(seats.spec,seats,rep(NA,length(seats.spec)))
  z <- spec_seats(seats.mod)

  class(z) <- c("seats_spec","data.frame")
  return(z)
}