regarima_outliers.R

#' @include utils.R
NULL

#' Outlier Detection with a RegARIMA Model
#'
#' @param y the dependent variable (a `ts` object).
#' @param order,seasonal the orders of the ARIMA model.
#' @param mean Boolean to include or not the mean.
#' @param X user defined regressors (other than calendar).
#' @param X.td calendar regressors.
#' @param ao,ls,so,tc Boolean to indicate which type of outliers should be detected.
#' @param cv  `numeric`. The entered critical value for the outlier detection procedure.
#' If equal to 0 the critical value for the outlier detection procedure is automatically determined
#' by the number of observations.
#' @param clean Clean missing values at the beginning/end of the series. Regression variables are automatically resized, if need be.
#'
#' @return a `"JD3_REGARIMA_OUTLIERS"` object, containing input variables and results
#'
#' @examples
#' regarima_outliers(rjd3toolkit::ABS$X0.2.09.10.M)
#'
#' @export
regarima_outliers<-function(y, order=c(0L,1L,1L), seasonal=c(0L,1L,1L), mean=FALSE,
                        X=NULL, X.td=NULL, ao=TRUE, ls=TRUE, tc=FALSE, so=FALSE, cv=0, clean = FALSE){
  if (!is.ts(y)){
    stop("y must be a time series")
  }
  if (! is.null(X.td)){
    sy<-start(y)
    td<-rjd3toolkit::td(s = y, groups = X.td)
    X<-cbind(X, td)
  }


  jregarima<-.jcall("jdplus/x13/base/r/RegArimaOutliersDetection", "Ljdplus/x13/base/r/RegArimaOutliersDetection$Results;", "process",
                    rjd3toolkit::.r2jd_tsdata(y), as.integer(order), as.integer(seasonal), mean, rjd3toolkit::.r2jd_matrix(X),
                 ao, ls, tc, so, cv, clean)
  model<-list(
    y=rjd3toolkit::.proc_ts(jregarima, "y"),
    variables=rjd3toolkit::.proc_vector(jregarima, "variables"),
    X=rjd3toolkit::.proc_matrix(jregarima, "regressors"),
    b=rjd3toolkit::.proc_vector(jregarima, "b"),
    bcov=rjd3toolkit::.proc_matrix(jregarima, "bvar"),
    linearized=rjd3toolkit::.proc_vector(jregarima, "linearized")
  )

  ll0<-rjd3toolkit::.proc_likelihood(jregarima, "initiallikelihood.")
  ll1<-rjd3toolkit::.proc_likelihood(jregarima, "finallikelihood.")

  return(structure(list(
    model=model,
    likelihood=list(initial=ll0, final=ll1)),
    class="JD3_REGARIMA_OUTLIERS"))
}