diff --git a/.Rbuildignore b/.Rbuildignore
index 630c475..1ab290f 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -7,3 +7,5 @@ TO_DO
^docs$
^pkgdown$
^\.github$
+
+^LICENSE$
diff --git a/.github/workflows/check-changelog.yml b/.github/workflows/check-changelog.yml
new file mode 100644
index 0000000..e0c5bb1
--- /dev/null
+++ b/.github/workflows/check-changelog.yml
@@ -0,0 +1,17 @@
+name: Check changelog
+
+on: [ push, pull_request ]
+
+jobs:
+ check-changelog-job:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ submodules: true
+ fetch-depth: 0
+
+ - uses: jbangdev/jbang-action@v0.115.0
+ with:
+ script: com.github.nbbrd.heylogs:heylogs-cli:0.7.2:bin
+ scriptargs: "check NEWS.md"
diff --git a/DESCRIPTION b/DESCRIPTION
index 17962bc..03c903e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -20,14 +20,14 @@ Imports:
Remotes:
github::rjdemetra/rjd3toolkit,
github::rjdemetra/rjd3sts
-SystemRequirements: Java JRE 17 or higher
+SystemRequirements: Java (>= 17)
License: EUPL
URL: https://github.com/rjdemetra/rjd3highfreq, https://rjdemetra.github.io/rjd3highfreq/
LazyData: TRUE
Suggests:
knitr,
rmarkdown
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
BugReports: https://github.com/rjdemetra/rjd3highfreq/issues
Encoding: UTF-8
Collate:
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..20fc266
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,185 @@
+EUROPEAN UNION PUBLIC LICENCE v. 1.2
+
+EUPL © the European Union 2007, 2016
+
+This European Union Public Licence (the ‘EUPL’) applies to the Work (as defined below) which is provided under the terms of this Licence. Any use of the Work, other than as authorised under this Licence is prohibited (to the extent such use is covered by a right of the copyright holder of the Work).
+
+The Work is provided under the terms of this Licence when the Licensor (as defined below) has placed the following notice immediately following the copyright notice for the Work:
+
+Licensed under the EUPL
+
+or has expressed by any other means his willingness to license under the EUPL.
+
+
+1. Definitions
+
+In this Licence, the following terms have the following meaning:
+
+— ‘The Licence’: this Licence.
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+— ‘The Original Work’: the work or software distributed or communicated by the Licensor under this Licence, available as Source Code and also as Executable Code as the case may be.
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+ Copyleft clause: If the Licensee distributes or communicates copies of the Original Works or Derivative Works, this Distribution or Communication will be done under the terms of this Licence or of a later version of this Licence unless the Original Work is expressly distributed only under this version of the Licence — for example by communicating ‘EUPL v. 1.2 only’. The Licensee (becoming Licensor) cannot offer or impose any additional terms or conditions on the Work or Derivative Work that alter or restrict the terms of the Licence.
+ Compatibility clause: If the Licensee Distributes or Communicates Derivative Works or copies thereof based upon both the Work and another work licensed under a Compatible Licence, this Distribution or Communication can be done under the terms of this Compatible Licence. For the sake of this clause, ‘Compatible Licence’ refers to the licences listed in the appendix attached to this Licence. Should the Licensee's obligations under the Compatible Licence conflict with his/her obligations under this Licence, the obligations of the Compatible Licence shall prevail.
+ Provision of Source Code: When distributing or communicating copies of the Work, the Licensee will provide a machine-readable copy of the Source Code or indicate a repository where this Source will be easily and freely available for as long as the Licensee continues to distribute or communicate the Work.
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+
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+
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+
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+8. Disclaimer of Liability
+
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+9. Additional agreements
+
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+
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+
+
+11. Information to the public
+
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+
+
+12. Termination of the Licence
+
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+
+Such a termination will not terminate the licences of any person who has received the Work from the Licensee under the Licence, provided such persons remain in full compliance with the Licence.
+
+
+13. Miscellaneous
+
+Without prejudice of Article 9 above, the Licence represents the complete agreement between the Parties as to the Work.
+
+If any provision of the Licence is invalid or unenforceable under applicable law, this will not affect the validity or enforceability of the Licence as a whole. Such provision will be construed or reformed so as necessary to make it valid and enforceable.
+
+The European Commission may publish other linguistic versions or new versions of this Licence or updated versions of the Appendix, so far this is required and reasonable, without reducing the scope of the rights granted by the Licence. New versions of the Licence will be published with a unique version number.
+
+All linguistic versions of this Licence, approved by the European Commission, have identical value. Parties can take advantage of the linguistic version of their choice.
+
+
+14. Jurisdiction
+
+Without prejudice to specific agreement between parties,
+
+— any litigation resulting from the interpretation of this License, arising between the European Union institutions, bodies, offices or agencies, as a Licensor, and any Licensee, will be subject to the jurisdiction of the Court of Justice of the European Union, as laid down in article 272 of the Treaty on the Functioning of the European Union,
+
+— any litigation arising between other parties and resulting from the interpretation of this License, will be subject to the exclusive jurisdiction of the competent court where the Licensor resides or conducts its primary business.
+
+
+15. Applicable Law
+
+Without prejudice to specific agreement between parties,
+
+— this Licence shall be governed by the law of the European Union Member State where the Licensor has his seat, resides or has his registered office,
+
+— this licence shall be governed by Belgian law if the Licensor has no seat, residence or registered office inside a European Union Member State.
+Appendix
+
+‘Compatible Licences’ according to Article 5 EUPL are:
+
+— GNU General Public License (GPL) v. 2, v. 3
+
+— GNU Affero General Public License (AGPL) v. 3
+
+— Open Software License (OSL) v. 2.1, v. 3.0
+
+— Eclipse Public License (EPL) v. 1.0
+
+— CeCILL v. 2.0, v. 2.1
+
+— Mozilla Public Licence (MPL) v. 2
+
+— GNU Lesser General Public Licence (LGPL) v. 2.1, v. 3
+
+— Creative Commons Attribution-ShareAlike v. 3.0 Unported (CC BY-SA 3.0) for works other than software
+
+— European Union Public Licence (EUPL) v. 1.1, v. 1.2
+
+— Québec Free and Open-Source Licence — Reciprocity (LiLiQ-R) or Strong Reciprocity (LiLiQ-R+)
+
+The European Commission may update this Appendix to later versions of the above licences without producing a new version of the EUPL, as long as they provide the rights granted in Article 2 of this Licence and protect the covered Source Code from exclusive appropriation.
+
+All other changes or additions to this Appendix require the production of a new EUPL version.
diff --git a/NEWS.md b/NEWS.md
index f512f98..000ec51 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,12 +1,25 @@
-# rjd3highfreq dev version
+# Changelog
+All notable changes to this project will be documented in this file.
-* New plots for `JDFractionalAirlineDecomposition` and `JDFractionalAirlineEstimation`
-* Update prints
-* Add documentation for plots and prints
-* Update readme
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres
+to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+## [Unreleased]
-# rjd3highfreq 1.0.1
+### Added
-First commit
+- New plots for `JDFractionalAirlineDecomposition` and `JDFractionalAirlineEstimation`
+- Add documentation for plots and prints
+
+### Changed
+
+- Update readme
+- Update prints
+
+
+[Unreleased]: https://github.com/rjdemetra/rjd3highfreq/compare/v2.0.0...HEAD
+
+## [2.0.0] - 2023-12-12
+
+[2.0.0]: https://github.com/rjdemetra/rjd3highfreq/releases/tag/v2.0.0
diff --git a/R/jd3_fractionalairline.R b/R/jd3_fractionalairline.R
index f0a74d4..1e96d63 100644
--- a/R/jd3_fractionalairline.R
+++ b/R/jd3_fractionalairline.R
@@ -1,18 +1,18 @@
#' @include utils.R
NULL
-ucm_extract<-function(jrslt, cmp){
- path<-paste0("ucarima.component(", cmp,")")
- return (arima_extract(jrslt, path))
+ucm_extract<-function(jrslt, cmp) {
+ path<-paste0("ucarima.component(", cmp,")")
+ return (arima_extract(jrslt, path))
}
-arima_extract<-function(jrslt, path){
- str<-rjd3toolkit::.proc_str(jrslt, paste0(path, ".name"))
- ar<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".ar"))
- delta<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".delta"))
- ma<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".ma"))
- var<-rjd3toolkit::.proc_numeric(jrslt, paste0(path, ".var"))
- return (rjd3toolkit::arima_model(str, ar,delta,ma,var))
+arima_extract<-function(jrslt, path) {
+ str<-rjd3toolkit::.proc_str(jrslt, paste0(path, ".name"))
+ ar<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".ar"))
+ delta<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".delta"))
+ ma<-rjd3toolkit::.proc_vector(jrslt, paste0(path, ".ma"))
+ var<-rjd3toolkit::.proc_numeric(jrslt, paste0(path, ".var"))
+ return (rjd3toolkit::arima_model(str, ar,delta,ma,var))
}
@@ -33,18 +33,23 @@ arima_extract<-function(jrslt, path){
#' @export
#'
#' @examples
-#'
-fractionalAirlineDecomposition <- function(y, period, sn = F, stde = F,
- nbcasts = 0, nfcasts = 0, log = FALSE, y_time = NULL)
-{
- checkmate::assertNumeric(y, null.ok = F)
- checkmate::assertNumeric(period, len = 1, null.ok = F)
- checkmate::assertLogical(sn, len = 1, null.ok = F)
- jrslt <- .jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
- "decompose", as.numeric(y), period, sn, stde, as.integer(nbcasts),
- as.integer(nfcasts))
- return(jd2r_fractionalAirlineDecomposition(jrslt, sn, stde, period, log, y_time))
+#'
+fractionalAirlineDecomposition <- function(y,
+ period,
+ sn = FALSE,
+ stde = FALSE,
+ nbcasts = 0,
+ nfcasts = 0,
+ log = FALSE,
+ y_time = NULL) {
+ checkmate::assertNumeric(y, null.ok = FALSE)
+ checkmate::assertNumeric(period, len = 1, null.ok = FALSE)
+ checkmate::assertLogical(sn, len = 1, null.ok = FALSE)
+ jrslt <- .jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
+ "decompose", as.numeric(y), period, sn, stde, as.integer(nbcasts),
+ as.integer(nfcasts))
+ return(jd2r_fractionalAirlineDecomposition(jrslt, sn, stde, period, log, y_time))
}
@@ -64,28 +69,26 @@ fractionalAirlineDecomposition <- function(y, period, sn = F, stde = F,
#' @export
#'
#' @examples
-#'
-multiAirlineDecomposition <- function(y, periods, ndiff = 2, ar = F, stde = F,
- nbcasts = 0, nfcasts = 0, log = FALSE, y_time = NULL)
-{
- if (length(periods) == 1) {
- return(fractionalAirlineDecomposition(y, periods, stde = stde,
- nbcasts = nbcasts, nfcasts = nfcasts,
- log = log, y_time = y_time))
- }
- checkmate::assertNumeric(y, null.ok = F)
- jrslt <- .jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
- "decompose", as.numeric(y), .jarray(periods), as.integer(ndiff),
- ar, stde, as.integer(nbcasts), as.integer(nfcasts))
- if (length(periods) == 1) {
- return(jd2r_fractionalAirlineDecomposition(jrslt, sn = F, stde, periods,
- log = log, y_time = y_time))
- }
- else {
- return(jd2r_multiAirlineDecomposition(jrslt, stde, periods,
- log = log, y_time = y_time))
- }
+#'
+multiAirlineDecomposition <- function(y, periods, ndiff = 2, ar = FALSE, stde = FALSE,
+ nbcasts = 0, nfcasts = 0, log = FALSE, y_time = NULL) {
+ if (length(periods) == 1) {
+ return(fractionalAirlineDecomposition(y, periods, stde = stde,
+ nbcasts = nbcasts, nfcasts = nfcasts,
+ log = log, y_time = y_time))
+ }
+ checkmate::assertNumeric(y, null.ok = FALSE)
+ jrslt <- .jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
+ "decompose", as.numeric(y), .jarray(periods), as.integer(ndiff),
+ ar, stde, as.integer(nbcasts), as.integer(nfcasts))
+ if (length(periods) == 1) {
+ return(jd2r_fractionalAirlineDecomposition(jrslt, sn = FALSE, stde, periods,
+ log = log, y_time = y_time))
+ } else {
+ return(jd2r_multiAirlineDecomposition(jrslt, stde, periods,
+ log = log, y_time = y_time))
+ }
}
@@ -97,175 +100,231 @@ multiAirlineDecomposition <- function(y, periods, ndiff = 2, ar = F, stde = F,
#' @param mean add constant mean to y after differencing.
#' @param outliers type of outliers sub vector of c("AO","LS","WO")
#' @param criticalValue Critical value for automatic outlier detection
-#' @param precision Precision of the likelihood
+#' @param precision Precision of the likelihood
#' @param approximateHessian Compute approximate hessian (based on the optimizing procedure)
#' @param nfcasts Number of forecasts
-#' @param log
+#' @param log a logical
#' @param y_time vector of times at which `y` is indexed
-#'
+#'
#' @return
#' @export
#'
#' @examples
-#'
-fractionalAirlineEstimation <- function(
- y, periods, x = NULL, ndiff = 2, ar = F, mean = FALSE,
- outliers = NULL, criticalValue = 6, precision = 1e-12,
- approximateHessian = F, nfcasts = 0, log = FALSE,
- y_time = NULL)
-{
- checkmate::assertNumeric(y, null.ok = F)
- checkmate::assertNumeric(criticalValue, len = 1, null.ok = F)
- checkmate::assertNumeric(precision, len = 1, null.ok = F)
- checkmate::assertLogical(mean, len = 1, null.ok = F)
- if (is.null(outliers))
- joutliers <- .jnull("[Ljava/lang/String;")
- else joutliers = .jarray(outliers, "java.lang.String")
- jrslt <- .jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/extendedairline/ExtendedAirlineEstimation;", "estimate",
- as.numeric(y), rjd3toolkit::.r2jd_matrix(x), mean, .jarray(periods),
- as.integer(ndiff), ar, joutliers, criticalValue, precision,
- approximateHessian, as.integer(nfcasts),log)
-
- external_variables <- .proc_variable_outlier_names(jrslt$getOutliers(),jrslt$getNx())
- reg_mat <- rjd3toolkit::.proc_matrix(jrslt, "regressors")
-
- if (!is.null(colnames(x)) && sum(duplicated(colnames(x))) == 0) {
- external_variables[seq_along(colnames(x))] <- colnames(x)
- }
- if (ncol(reg_mat) > 0) {
- colnames(reg_mat) <- external_variables
- }
-
- model <- list(
- y = rjd3toolkit::.proc_vector(jrslt, "y"),
- y_time = y_time,
- periods = periods,
- variables = external_variables,
- # "variables " names of variables and outliers
- xreg = reg_mat,
- # "xreg" matrix of regressor (external variables and outliers)
- b = rjd3toolkit::.proc_vector(jrslt, "b"),
- bcov = rjd3toolkit::.proc_matrix(jrslt, "bvar"),
- linearized = rjd3toolkit::.proc_vector(jrslt, "lin"),
- residuals=rjd3toolkit::.proc_vector(jrslt,"residuals"),
- component_wo = rjd3toolkit::.proc_vector(jrslt, "component_wo"),
- component_ao = rjd3toolkit::.proc_vector(jrslt, "component_ao"),
- component_ls = rjd3toolkit::.proc_vector(jrslt, "component_ls"),
- component_outliers = rjd3toolkit::.proc_vector(jrslt, "component_outliers"),
- component_userdef_reg_variables = rjd3toolkit::.proc_vector(jrslt, "component_userdef_reg_variables"),
- component_mean = rjd3toolkit::.proc_vector(jrslt, "component_mean"),
- log=rjd3toolkit::.proc_bool(jrslt,"log"),
- missingOrNegative=rjd3toolkit::.proc_vector(jrslt, "missing"))
-
- estimation <- list(parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
- score = rjd3toolkit::.proc_vector(jrslt, "score"),
- covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"))
-
- likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
-
- return(structure(list(model = model,
- estimation = estimation,
- likelihood = likelihood),
- class = "JDFractionalAirlineEstimation"))
+#'
+fractionalAirlineEstimation <- function(y,
+ periods,
+ x = NULL,
+ ndiff = 2,
+ ar = FALSE,
+ mean = FALSE,
+ outliers = NULL,
+ criticalValue = 6,
+ precision = 1e-12,
+ approximateHessian = FALSE,
+ nfcasts = 0,
+ log = FALSE,
+ y_time = NULL) {
+
+ # Input checks
+ checkmate::assertNumeric(y, null.ok = FALSE)
+ checkmate::assertNumeric(criticalValue, len = 1, null.ok = FALSE)
+ checkmate::assertNumeric(precision, len = 1, null.ok = FALSE)
+ checkmate::assertLogical(mean, len = 1, null.ok = FALSE)
+
+ if (is.null(outliers)) {
+ joutliers <- .jnull("[Ljava/lang/String;")
+ } else {
+ joutliers <- .jarray(outliers, "java.lang.String")
+ }
+ jrslt <- .jcall(
+ obj = "jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ returnSig = "Ljdplus/highfreq/base/core/extendedairline/ExtendedAirlineEstimation;",
+ method = "estimate",
+ as.numeric(y),
+ rjd3toolkit::.r2jd_matrix(x),
+ mean,
+ .jarray(periods),
+ as.integer(ndiff),
+ ar,
+ joutliers,
+ criticalValue,
+ precision,
+ approximateHessian,
+ as.integer(nfcasts),
+ log
+ )
+
+ external_variables <- .proc_variable_outlier_names(
+ var_out_names = jrslt$getOutliers(),
+ nX = jrslt$getNx()
+ )
+ reg_mat <- rjd3toolkit::.proc_matrix(rslt = jrslt, name = "regressors")
+
+ if (is.null(y_time) && !is.null(x)) {
+ y_time <- rownames(x)
+ }
+
+ if (!is.null(colnames(x)) && sum(duplicated(colnames(x))) == 0) {
+ external_variables[seq_len(ncol(x))] <- colnames(x)
+ # Outliers
+ if (!is.null(y_time) && (length(external_variables) - ncol(x) > 0)) {
+ outliers <- external_variables[-seq_len(ncol(x))]
+ outliers_type <- substr(outliers, start = 1, stop = 2)
+ outliers_date <- y_time[as.integer(substr(outliers, start = 4, stop = 50))]
+ external_variables[-seq_len(ncol(x))] <- paste0(outliers_type, ".", outliers_date)
+ }
+ } else if (is.null(x)
+ && !is.null(y_time)
+ && length(external_variables) > 0) {
+ outliers <- external_variables
+ outliers_type <- substr(outliers, start = 1, stop = 2)
+ outliers_date <- y_time[as.integer(substr(outliers, start = 4, stop = 50))]
+ external_variables <- paste0(outliers_type, ".", outliers_date)
+ }
+ if (!is.null(reg_mat) && ncol(reg_mat) > 0) {
+ colnames(reg_mat) <- external_variables
+ }
+ if (!is.null(reg_mat) && nrow(reg_mat) > 0 && !is.null(y_time)) {
+ rownames(reg_mat) <- y_time
+ }
+
+ model <- list(
+ y = rjd3toolkit::.proc_vector(jrslt, "y"),
+ y_time = y_time,
+ periods = periods,
+ variables = external_variables,
+ # "variables " names of variables and outliers
+ xreg = reg_mat,
+ # "xreg" matrix of regressor (external variables and outliers)
+ b = rjd3toolkit::.proc_vector(jrslt, "b"),
+ bcov = rjd3toolkit::.proc_matrix(jrslt, "bvar"),
+ linearized = rjd3toolkit::.proc_vector(jrslt, "lin"),
+ residuals = rjd3toolkit::.proc_vector(jrslt,"residuals"),
+ component_wo = rjd3toolkit::.proc_vector(jrslt, "component_wo"),
+ component_ao = rjd3toolkit::.proc_vector(jrslt, "component_ao"),
+ component_ls = rjd3toolkit::.proc_vector(jrslt, "component_ls"),
+ component_outliers = rjd3toolkit::.proc_vector(jrslt, "component_outliers"),
+ component_userdef_reg_variables = rjd3toolkit::.proc_vector(jrslt, "component_userdef_reg_variables"),
+ component_mean = rjd3toolkit::.proc_vector(jrslt, "component_mean"),
+ log=rjd3toolkit::.proc_bool(jrslt,"log"),
+ missingOrNegative = rjd3toolkit::.proc_vector(jrslt, "missing")
+ )
+
+ estimation <- list(parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
+ score = rjd3toolkit::.proc_vector(jrslt, "score"),
+ covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"))
+
+ likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
+
+ return(structure(list(model = model,
+ estimation = estimation,
+ likelihood = likelihood),
+ class = "JDFractionalAirlineEstimation"))
}
-.proc_variable_outlier_names<-function(var_out_names,nX) {
- o<-.jevalArray(var_out_names)
- nO<-length(o)
-
- regvar_outliers<-rep(NA,nX-nO)
- for(j in 1:nX-nO) {
- regvar_outliers[j]=paste("x-", j)}
-
- if(nO>0){
- for (j in 1:nO) {
- regvar_outliers[nX-nO+j]<-o[[j]]$toString()}}
- return(regvar_outliers)
+.proc_variable_outlier_names <- function(var_out_names, nX) {
+
+ if (nX == 0) return(NULL)
+
+ o <- .jevalArray(var_out_names)
+ nO <- length(o)
+
+ regvar_outliers <- rep(NA_character_, nX)
+
+ # External regressors
+ for(j in seq_len(nX - nO)) {
+ regvar_outliers[j] <- paste0("x-", j)
+ }
+
+ # Outliers
+ for (j in seq_len(nO)) {
+ regvar_outliers[nX - nO + j] <- o[[j]]$toString()
+ }
+ return(regvar_outliers)
}
#' Title
#'
-#' @param y
-#' @param periods
-#' @param ndiff
-#' @param stde
-#' @param nbcasts
-#' @param nfcasts
+#' @param y
+#' @param periods
+#' @param ndiff
+#' @param stde
+#' @param nbcasts
+#' @param nfcasts
#'
#' @return
#' @export
#'
#' @examples
-#'
-multiAirlineDecomposition_raw<-function(y, periods, ndiff=2, ar=F, stde=F, nbcasts=0, nfcasts=0){
- checkmate::assertNumeric(y, null.ok = F)
-
- jrslt<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
- "decompose", as.numeric(y),
- .jarray(periods), as.integer(ndiff), ar, stde, as.integer(nbcasts), as.integer(nfcasts))
-
- return (jrslt)
+#'
+multiAirlineDecomposition_raw<-function(y, periods, ndiff=2, ar=FALSE, stde=FALSE, nbcasts=0, nfcasts=0) {
+ checkmate::assertNumeric(y, null.ok = FALSE)
+
+ jrslt<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
+ "decompose", as.numeric(y),
+ .jarray(periods), as.integer(ndiff), ar, stde, as.integer(nbcasts), as.integer(nfcasts))
+
+ return (jrslt)
}
#' Title
#'
-#' @param jdecomp
+#' @param jdecomp
#'
#' @return
#' @export
#'
#' @examples
-multiAirlineDecomposition_ssf<-function(jdecomp){
- jssf<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/ssf/extractors/SsfUcarimaEstimation;", "ssfDetails", jdecomp)
- return (rjd3toolkit::.jd3_object(jssf, result=T))
+multiAirlineDecomposition_ssf<-function(jdecomp) {
+ jssf<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ "Ljdplus/highfreq/base/core/ssf/extractors/SsfUcarimaEstimation;", "ssfDetails", jdecomp)
+ return (rjd3toolkit::.jd3_object(jssf, result=TRUE))
}
#' Title
#'
-#' @param y
-#' @param period
-#' @param sn
-#' @param stde
-#' @param nbcasts
-#' @param nfcasts
+#' @param y
+#' @param period
+#' @param sn
+#' @param stde
+#' @param nbcasts
+#' @param nfcasts
#'
#' @return
#' @export
#'
#' @examples
-#'
-fractionalAirlineDecomposition_raw<-function(y, period, sn=F, stde=F, nbcasts=0, nfcasts=0){
- checkmate::assertNumeric(y, null.ok = F)
- checkmate::assertNumeric(period, len = 1, null.ok = F)
- checkmate::assertLogical(sn, len = 1, null.ok = F)
- jrslt<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
- "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
- "decompose", as.numeric(y),
- period, sn, stde, as.integer(nbcasts), as.integer(nfcasts))
- return (jrslt)
+#'
+fractionalAirlineDecomposition_raw<-function(y, period, sn=FALSE, stde=FALSE, nbcasts=0, nfcasts=0) {
+ checkmate::assertNumeric(y, null.ok = FALSE)
+ checkmate::assertNumeric(period, len = 1, null.ok = FALSE)
+ checkmate::assertLogical(sn, len = 1, null.ok = FALSE)
+ jrslt<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor",
+ "Ljdplus/highfreq/base/core/extendedairline/decomposition/LightExtendedAirlineDecomposition;",
+ "decompose", as.numeric(y),
+ period, sn, stde, as.integer(nbcasts), as.integer(nfcasts))
+ return (jrslt)
}
#' Title
#'
-#' @param jdecomp
+#' @param jdecomp
#'
#' @return
#' @export
#'
#' @examples
-fractionalAirlineDecomposition_ssf<-function(jdecomp){
- jssf<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor", "Ljdplus/highfreq/base/core/ssf/extractors/SsfUcarimaEstimation;", "ssfDetails", jdecomp)
- return (rjd3toolkit::.jd3_object(jssf, result=T))
+fractionalAirlineDecomposition_ssf<-function(jdecomp) {
+ jssf<-.jcall("jdplus/highfreq/base/r/FractionalAirlineProcessor", "Ljdplus/highfreq/base/core/ssf/extractors/SsfUcarimaEstimation;", "ssfDetails", jdecomp)
+ return (rjd3toolkit::.jd3_object(jssf, result=TRUE))
}
#' Title
#'
-#' @param jrslt
-#' @param stde
+#' @param jrslt
+#' @param stde
#' @param log
#' @param y_time vector of times at which the time series is indexed
#'
@@ -273,59 +332,58 @@ fractionalAirlineDecomposition_ssf<-function(jdecomp){
#' @export
#'
#' @examples
-#'
-jd2r_multiAirlineDecomposition <- function(jrslt, stde = F, periods,
- log = FALSE, y_time = NULL)
-{
- ncmps <- rjd3toolkit::.proc_int(jrslt, "ucarima.size")
- model <- rjd3highfreq:::arima_extract(jrslt, "ucarima_model")
- cmps <- lapply(1:ncmps, function(cmp) {
- return(rjd3highfreq:::ucm_extract(jrslt, cmp))
- })
- ucarima <- rjd3toolkit::ucarima_model(model, cmps)
- yc <- rjd3toolkit::.proc_vector(jrslt, "y")
- estimation <- list(
- parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
- score = rjd3toolkit::.proc_vector(jrslt, "score"),
- covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"),
- periods = periods,
- log = log)
- likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
-
- ncmps <- rjd3toolkit::.proc_int(jrslt, "ncmps")
- yc <- rjd3toolkit::.proc_vector(jrslt, "y")
- sa <- rjd3toolkit::.proc_vector(jrslt, "sa")
- tsi_component <- lapply(X = 1:ncmps, FUN = function(j) {
- return(rjd3toolkit::.proc_vector(jrslt, paste0("cmp(", j, ")")))
- })
- names(tsi_component) <- c("t", paste0("s_", periods) , "i")
-
- decomposition <- c(
- list(y = yc, y_time = y_time, sa = sa),
- tsi_component)
-
- if (stde) {
- tsi_stde_component <- lapply(X = 1:ncmps, FUN = function(j) {
- return(rjd3toolkit::.proc_vector(jrslt, paste0("cmp_stde(", j, ")")))
+#'
+jd2r_multiAirlineDecomposition <- function(jrslt, stde = FALSE, periods,
+ log = FALSE, y_time = NULL) {
+ ncmps <- rjd3toolkit::.proc_int(jrslt, "ucarima.size")
+ model <- rjd3highfreq:::arima_extract(jrslt, "ucarima_model")
+ cmps <- lapply(1:ncmps, function(cmp) {
+ return(rjd3highfreq:::ucm_extract(jrslt, cmp))
+ })
+ ucarima <- rjd3toolkit::ucarima_model(model, cmps)
+ yc <- rjd3toolkit::.proc_vector(jrslt, "y")
+ estimation <- list(
+ parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
+ score = rjd3toolkit::.proc_vector(jrslt, "score"),
+ covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"),
+ periods = periods,
+ log = log)
+ likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
+
+ ncmps <- rjd3toolkit::.proc_int(jrslt, "ncmps")
+ yc <- rjd3toolkit::.proc_vector(jrslt, "y")
+ sa <- rjd3toolkit::.proc_vector(jrslt, "sa")
+ tsi_component <- lapply(X = 1:ncmps, FUN = function(j) {
+ return(rjd3toolkit::.proc_vector(jrslt, paste0("cmp(", j, ")")))
})
- names(tsi_stde_component) <- c("t.stde", paste0("stde_", periods) , "i.stde")
-
- decomposition <- c(decomposition, tsi_stde_component)
- }
-
- return(structure(list(ucarima = ucarima,
- decomposition = decomposition,
- estimation = estimation,
- likelihood = likelihood),
- class = "JDFractionalAirlineDecomposition"))
+ names(tsi_component) <- c("t", paste0("s_", periods) , "i")
+
+ decomposition <- c(
+ list(y = yc, y_time = y_time, sa = sa),
+ tsi_component)
+
+ if (stde) {
+ tsi_stde_component <- lapply(X = 1:ncmps, FUN = function(j) {
+ return(rjd3toolkit::.proc_vector(jrslt, paste0("cmp_stde(", j, ")")))
+ })
+ names(tsi_stde_component) <- c("t.stde", paste0("stde_", periods) , "i.stde")
+
+ decomposition <- c(decomposition, tsi_stde_component)
+ }
+
+ return(structure(list(ucarima = ucarima,
+ decomposition = decomposition,
+ estimation = estimation,
+ likelihood = likelihood),
+ class = "JDFractionalAirlineDecomposition"))
}
#' Title
#'
-#' @param jrslt
-#' @param sn
-#' @param stde
+#' @param jrslt
+#' @param sn
+#' @param stde
#' @param log
#' @param y_time vector of times at which the time series is indexed
#'
@@ -333,51 +391,56 @@ jd2r_multiAirlineDecomposition <- function(jrslt, stde = F, periods,
#' @export
#'
#' @examples
-#'
-jd2r_fractionalAirlineDecomposition <- function(jrslt, sn = F, stde = F,
- period, log = FALSE, y_time = NULL)
-{
- ncmps <- rjd3toolkit::.proc_int(jrslt, "ucarima.size")
- model <- rjd3highfreq:::arima_extract(jrslt, "ucarima_model")
- cmps <- lapply(1:ncmps, function(cmp) {
- return(rjd3highfreq:::ucm_extract(jrslt, cmp))
- })
- ucarima <- rjd3toolkit::ucarima_model(model, cmps)
- yc <- rjd3toolkit::.proc_vector(jrslt, "y")
- sa <- rjd3toolkit::.proc_vector(jrslt, "sa")
- s <- rjd3toolkit::.proc_vector(jrslt, "s")
-
- decomposition <- list(y = yc, y_time = y_time, sa = sa, s = s)
-
- if (!sn) {
- tc <- rjd3toolkit::.proc_vector(jrslt, "t")
- ic <- rjd3toolkit::.proc_vector(jrslt, "i")
- decomposition <- c(decomposition, list(t = tc, i = ic))
- }
-
- if (stde) {
- s.stde = rjd3toolkit::.proc_vector(jrslt, "s_stde")
- decomposition <- c(decomposition, list(s.stde = s.stde))
-
+#'
+jd2r_fractionalAirlineDecomposition <- function(jrslt,
+ sn = FALSE,
+ stde = FALSE,
+ period,
+ log = FALSE,
+ y_time = NULL) {
+ ncmps <- rjd3toolkit::.proc_int(jrslt, "ucarima.size")
+ model <- rjd3highfreq:::arima_extract(jrslt, "ucarima_model")
+ cmps <- lapply(
+ X = 1:ncmps,
+ FUN = function(cmp) rjd3highfreq:::ucm_extract(jrslt, cmp)
+ )
+ ucarima <- rjd3toolkit::ucarima_model(model, cmps)
+ yc <- rjd3toolkit::.proc_vector(jrslt, "y")
+ sa <- rjd3toolkit::.proc_vector(jrslt, "sa")
+ s <- rjd3toolkit::.proc_vector(jrslt, "s")
+
+ decomposition <- list(y = yc, y_time = y_time, sa = sa, s = s)
+
if (!sn) {
- t.stde = rjd3toolkit::.proc_vector(jrslt, "t_stde")
- i.stde = rjd3toolkit::.proc_vector(jrslt, "i_stde")
- decomposition <- c(decomposition, list(t.stde = t.stde, i.stde = i.stde))
+ tc <- rjd3toolkit::.proc_vector(jrslt, "t")
+ ic <- rjd3toolkit::.proc_vector(jrslt, "i")
+ decomposition <- c(decomposition, list(t = tc, i = ic))
}
- }
-
- estimation <- list(
- parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
- score = rjd3toolkit::.proc_vector(jrslt, "score"),
- covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"),
- periods = period,
- log = log)
-
- likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
-
- return(structure(list(ucarima = ucarima,
- decomposition = decomposition,
- estimation = estimation,
- likelihood = likelihood),
- class = "JDFractionalAirlineDecomposition"))
+
+ if (stde) {
+ s.stde <- rjd3toolkit::.proc_vector(jrslt, "s_stde")
+ decomposition <- c(decomposition, list(s.stde = s.stde))
+
+ if (!sn) {
+ t.stde <- rjd3toolkit::.proc_vector(jrslt, "t_stde")
+ i.stde <- rjd3toolkit::.proc_vector(jrslt, "i_stde")
+ decomposition <- c(decomposition,
+ list(t.stde = t.stde, i.stde = i.stde))
+ }
+ }
+
+ estimation <- list(
+ parameters = rjd3toolkit::.proc_vector(jrslt, "parameters"),
+ score = rjd3toolkit::.proc_vector(jrslt, "score"),
+ covariance = rjd3toolkit::.proc_matrix(jrslt, "pcov"),
+ periods = period,
+ log = log)
+
+ likelihood <- rjd3toolkit::.proc_likelihood(jrslt, "likelihood.")
+
+ return(structure(list(ucarima = ucarima,
+ decomposition = decomposition,
+ estimation = estimation,
+ likelihood = likelihood),
+ class = "JDFractionalAirlineDecomposition"))
}
diff --git a/R/plot.R b/R/plot.R
index 0c1cc53..3763bf1 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -2,7 +2,7 @@
#' Custom Plot Function on JD+ template
#'
#' This function creates a customized plot in the same template as JD+ GUI color and forms.
-#'
+#'
#' @param x Numeric vector, x-axis values.
#' @param y List of numeric vectors, y-axis values for different series.
#' @param col Vector of colors for different series.
@@ -11,50 +11,50 @@
#'
#' @return `NULL` (invisible).
#'
-plot_jd <- function(x, y, col, legend_txt = NULL, ...){
-
- col_bg <- "#f5f4e7"
- col_grid <-"#dadad3"
- y_range <- range(do.call(c, y))
-
- plot.new()
- rect(xleft = par("usr")[1], xright = par("usr")[2],
- ytop = par("usr")[4], ybottom = par("usr")[3], col = col_bg)
- par(new = TRUE)
- plot(y = y[[1]], x = x,
- col = col[1], type = "l", xlab = "", ylab = "", ylim = y_range,
- main = "", xaxt = "n", yaxt = "n")
- x_breaks <- Axis(x, side = 1)
- par(xaxp = c(x_breaks[1], x_breaks[length(x_breaks)], length(x_breaks) - 1))
- grid(nx = NULL, ny = NULL, col = col_grid)
- par(new = TRUE)
-
- plot(y = y[[1]],
- x = x,
- col = col[1], type = "l", xlab = "Time", ylim = y_range,
- xaxt = "n", ...)
-
- for (k in (seq_along(y[-1]) + 1)) {
- lines(y = y[[k]],
- x = x,
- col = col[k], ...)
- }
-
- box(col = col_grid)
-
- if (!is.null(legend_txt)) {
- legend("bottomleft", legend = legend_txt,
- pch = 16, col = col, horiz = TRUE, xpd = TRUE,
- inset = c(0, 1), bty = "n")
- }
-
- return(invisible(NULL))
+plot_jd <- function(x, y, col, legend_txt = NULL, ...) {
+
+ col_bg <- "#f5f4e7"
+ col_grid <-"#dadad3"
+ y_range <- range(do.call(c, y))
+
+ plot.new()
+ rect(xleft = par("usr")[1], xright = par("usr")[2],
+ ytop = par("usr")[4], ybottom = par("usr")[3], col = col_bg)
+ par(new = TRUE)
+ plot(y = y[[1]], x = x,
+ col = col[1], type = "l", xlab = "", ylab = "", ylim = y_range,
+ main = "", xaxt = "n", yaxt = "n")
+ x_breaks <- Axis(x, side = 1)
+ par(xaxp = c(x_breaks[1], x_breaks[length(x_breaks)], length(x_breaks) - 1))
+ grid(nx = NULL, ny = NULL, col = col_grid)
+ par(new = TRUE)
+
+ plot(y = y[[1]],
+ x = x,
+ col = col[1], type = "l", xlab = "Time", ylim = y_range,
+ xaxt = "n", ...)
+
+ for (k in (seq_along(y[-1]) + 1)) {
+ lines(y = y[[k]],
+ x = x,
+ col = col[k], ...)
+ }
+
+ box(col = col_grid)
+
+ if (!is.null(legend_txt)) {
+ legend("bottomleft", legend = legend_txt,
+ pch = 16, col = col, horiz = TRUE, xpd = TRUE,
+ inset = c(0, 1), bty = "n")
+ }
+
+ return(invisible(NULL))
}
#' Plot Function for JDFractionalAirlineEstimation Objects
#'
#' This function creates a plot for the result of fractional airline model (class `JDFractionalAirlineEstimation`). It shows the raw data and linearized series.
-#'
+#'
#' @param x An object of class 'JDFractionalAirlineEstimation'.
#' @param from `Date` or `POSIXt` object, optional starting point for x-axis.
#' @param to `Date` or `POSIXt` object, optional ending point for x-axis.
@@ -64,57 +64,57 @@ plot_jd <- function(x, y, col, legend_txt = NULL, ...){
#'
#' @export
plot.JDFractionalAirlineEstimation <- function(x, from, to, ...) {
-
- col_y <- "#f1ba1d"
- col_t <- "#1e6c0b"
- col_sa <- "#00488c"
-
- col_s1 <- "#ffab78"
- col_s2 <- "#9169be"
-
- y <- x$model$y
- y_lin <- x$model$linearized
- if (x$model$log) {
- y_lin <- exp(y_lin)
- }
-
- vect_x <- x$model$y_time
- if (is.null(vect_x)) {
- vect_x <- seq_along(y)
- } else {
- if (!missing(from)) {
- vect_x <- vect_x[vect_x >= from]
+
+ col_y <- "#f1ba1d"
+ col_t <- "#1e6c0b"
+ col_sa <- "#00488c"
+
+ col_s1 <- "#ffab78"
+ col_s2 <- "#9169be"
+
+ y <- x$model$y
+ y_lin <- x$model$linearized
+ if (x$model$log) {
+ y_lin <- exp(y_lin)
}
- if (!missing(to)) {
- vect_x <- vect_x[vect_x <= to]
+
+ vect_x <- x$model$y_time
+ if (is.null(vect_x)) {
+ vect_x <- seq_along(y)
+ } else {
+ if (!missing(from)) {
+ vect_x <- vect_x[vect_x >= from]
+ }
+ if (!missing(to)) {
+ vect_x <- vect_x[vect_x <= to]
+ }
+ y <- y[which(x$model$y_time %in% vect_x)]
+ y_lin <- y_lin[which(x$model$y_time %in% vect_x)]
}
- y <- y[which(x$model$y_time %in% vect_x)]
- y_lin <- y_lin[which(x$model$y_time %in% vect_x)]
- }
-
- list_args <- list(...)
- list_args$main <- ifelse("main" %in% names(list_args),
- yes = paste0("Raw data and linearised series", " - ", list_args$main),
- no = "Raw data and linearised series")
- list_args$ylab <- ifelse("ylab" %in% names(list_args),
- yes = list_args$ylab, no = "")
- list_args$col <- c(col_y, col_t)
-
- do.call(plot_jd,
- c(list(
- x = vect_x, y = list(y, y_lin),
- legend_txt = c("Raw data", "Linearised series")),
- list_args)
- )
-
- return(invisible(NULL))
+
+ list_args <- list(...)
+ list_args$main <- ifelse("main" %in% names(list_args),
+ yes = paste0("Raw data and linearised series", " - ", list_args$main),
+ no = "Raw data and linearised series")
+ list_args$ylab <- ifelse("ylab" %in% names(list_args),
+ yes = list_args$ylab, no = "")
+ list_args$col <- c(col_y, col_t)
+
+ do.call(plot_jd,
+ c(list(
+ x = vect_x, y = list(y, y_lin),
+ legend_txt = c("Raw data", "Linearised series")),
+ list_args)
+ )
+
+ return(invisible(NULL))
}
#' Plot Function for JDFractionalAirlineDecomposition Objects
#'
#' This function creates a plot for the result of an Arima Model Based (AMB) decomposition of one or several frequencies (class `JDFractionalAirlineDecomposition`). It shows the decomposition and the component of the model.
-#'
-#'
+#'
+#'
#' @param x An object of class 'JDFractionalAirlineDecomposition'.
#' @param from `Date` or `POSIXt` object, optional starting point for x-axis.
#' @param to `Date` or `POSIXt` object, optional ending point for x-axis.
@@ -125,99 +125,99 @@ plot.JDFractionalAirlineEstimation <- function(x, from, to, ...) {
#'
#' @export
plot.JDFractionalAirlineDecomposition <- function(
- x, from, to, type_chart = c("y-sa-trend", "cal-seas-irr"), ...) {
-
- if ("y-sa-trend" %in% type_chart) {
-
- col_y <- "#f1ba1d"
- col_t <- "#1e6c0b"
- col_sa <- "#00488c"
-
- y <- x$decomposition$y
- sa <- x$decomposition$sa
- tc <- x$decomposition$t
- if (x$estimation$log) {
- y <- exp(y)
- sa <- exp(sa)
- tc <- exp(tc)
- }
-
- vect_x <- x$decomposition$y_time
- if (is.null(vect_x)) {
- vect_x <- seq_along(y)
- } else {
- if (!missing(from)) {
- vect_x <- vect_x[vect_x >= from]
- }
- if (!missing(to)) {
- vect_x <- vect_x[vect_x <= to]
- }
- time_lim <- which(x$decomposition$y_time %in% vect_x)
- y <- y[time_lim]
- sa <- sa[time_lim]
- tc <- tc[time_lim]
- }
-
- list_args <- list(...)
- list_args$main <- ifelse("main" %in% names(list_args),
- paste0("Decomposition AMB", " - ", list_args$main),
- "Decomposition AMB")
- list_args$ylab <- ifelse("ylab" %in% names(list_args),
- yes = list_args$ylab, no = "")
- list_args$col <- c(col_y, col_sa, col_t)
-
- do.call(plot_jd,
- c(list(
- x = vect_x, y = list(y, sa, tc),
- legend_txt = c("Raw data", "Seasonnal adjusted", "Trend")),
- list_args)
- )
- }
-
- if ("cal-seas-irr" %in% type_chart) {
-
- col_s <- c("#ffab78", "#9169be", "#4d8076", "#c34a36", "#00c9a7")
- s_variables <- names(x$decomposition)
- s_variables <- s_variables[grepl("^s(?!a)", s_variables, perl = TRUE)]
-
- s <- x$decomposition[s_variables]
- ic <- x$decomposition$i
- if (x$estimation$log) {
- s <- lapply(X = s, FUN = exp)
- ic <- exp(ic)
+ x, from, to, type_chart = c("y-sa-trend", "cal-seas-irr"), ...) {
+
+ if ("y-sa-trend" %in% type_chart) {
+
+ col_y <- "#f1ba1d"
+ col_t <- "#1e6c0b"
+ col_sa <- "#00488c"
+
+ y <- x$decomposition$y
+ sa <- x$decomposition$sa
+ tc <- x$decomposition$t
+ if (x$estimation$log) {
+ y <- exp(y)
+ sa <- exp(sa)
+ tc <- exp(tc)
+ }
+
+ vect_x <- x$decomposition$y_time
+ if (is.null(vect_x)) {
+ vect_x <- seq_along(y)
+ } else {
+ if (!missing(from)) {
+ vect_x <- vect_x[vect_x >= from]
+ }
+ if (!missing(to)) {
+ vect_x <- vect_x[vect_x <= to]
+ }
+ time_lim <- which(x$decomposition$y_time %in% vect_x)
+ y <- y[time_lim]
+ sa <- sa[time_lim]
+ tc <- tc[time_lim]
+ }
+
+ list_args <- list(...)
+ list_args$main <- ifelse("main" %in% names(list_args),
+ paste0("Decomposition AMB", " - ", list_args$main),
+ "Decomposition AMB")
+ list_args$ylab <- ifelse("ylab" %in% names(list_args),
+ yes = list_args$ylab, no = "")
+ list_args$col <- c(col_y, col_sa, col_t)
+
+ do.call(plot_jd,
+ c(list(
+ x = vect_x, y = list(y, sa, tc),
+ legend_txt = c("Raw data", "Seasonnal adjusted", "Trend")),
+ list_args)
+ )
}
-
- vect_x <- x$decomposition$y_time
- if (is.null(vect_x)) {
- vect_x <- seq_along(ic)
- } else {
- if (!missing(from)) {
- vect_x <- vect_x[vect_x >= from]
- }
- if (!missing(to)) {
- vect_x <- vect_x[vect_x <= to]
- }
- time_lim <- which(x$decomposition$y_time %in% vect_x)
-
- s <- lapply(X = s, FUN = base::`[`, time_lim)
- ic <- ic[time_lim]
+
+ if ("cal-seas-irr" %in% type_chart) {
+
+ col_s <- c("#ffab78", "#9169be", "#4d8076", "#c34a36", "#00c9a7")
+ s_variables <- names(x$decomposition)
+ s_variables <- s_variables[grepl("^s(?!a)", s_variables, perl = TRUE)]
+
+ s <- x$decomposition[s_variables]
+ ic <- x$decomposition$i
+ if (x$estimation$log) {
+ s <- lapply(X = s, FUN = exp)
+ ic <- exp(ic)
+ }
+
+ vect_x <- x$decomposition$y_time
+ if (is.null(vect_x)) {
+ vect_x <- seq_along(ic)
+ } else {
+ if (!missing(from)) {
+ vect_x <- vect_x[vect_x >= from]
+ }
+ if (!missing(to)) {
+ vect_x <- vect_x[vect_x <= to]
+ }
+ time_lim <- which(x$decomposition$y_time %in% vect_x)
+
+ s <- lapply(X = s, FUN = base::`[`, time_lim)
+ ic <- ic[time_lim]
+ }
+
+ list_args <- list(...)
+ list_args$main <- ifelse("main" %in% names(list_args),
+ paste0("Irregular and Seasonal components", " - ", list_args$main),
+ "Irregular and Seasonal components")
+ list_args$ylab <- ifelse("ylab" %in% names(list_args),
+ yes = list_args$ylab, no = "")
+ list_args$col <- col_s[seq_len(length(s) + 1)]
+
+ do.call(plot_jd,
+ c(list(
+ x = vect_x, y = c(s, list(ic)),
+ legend_txt = c(s_variables, "Irregular")),
+ list_args)
+ )
}
-
- list_args <- list(...)
- list_args$main <- ifelse("main" %in% names(list_args),
- paste0("Irregular and Seasonal components", " - ", list_args$main),
- "Irregular and Seasonal components")
- list_args$ylab <- ifelse("ylab" %in% names(list_args),
- yes = list_args$ylab, no = "")
- list_args$col <- col_s[seq_len(length(s) + 1)]
-
- do.call(plot_jd,
- c(list(
- x = vect_x, y = c(s, list(ic)),
- legend_txt = c(s_variables, "Irregular")),
- list_args)
- )
- }
-
- return(invisible(NULL))
+
+ return(invisible(NULL))
}
diff --git a/R/print.R b/R/print.R
index ee66bcd..e03c4f9 100644
--- a/R/print.R
+++ b/R/print.R
@@ -1,78 +1,78 @@
#' Print method for 'JDFractionalAirlineDecomposition' objects
#'
#' This function prints informations on the result of a Fractional Airline model (classe JDFractionalAirlineDecomposition).
-#'
+#'
#' @param x An object of class 'JDFractionalAirlineDecomposition'.
#' @param digits Number of digits to round numerical values (default is 3 or digits - 3 from options).
#'
#' @return The original object 'x'.
#'
#' @export
-print.JDFractionalAirlineDecomposition <- function(x, digits = max(3L, getOption("digits") - 3L), ...)
-{
- cat("Number of observations:", formatC(x$likelihood$nobs, digits = digits))
- cat("\n")
-
- if (!is.null(x$decomposition$y_time)) {
- cat("Start:", format(x$decomposition$y_time[1]),"\n")
- cat("End:", format(x$decomposition$y_time[length(x$decomposition$y_time)]), "\n")
- }
-
- # Estimated MA parameters (coefs, se, student)
- nb_freq <- length(x$estimation$parameters) - 1L
- est_ma_params <- data.frame(
- MA_parameter = c("Theta(1)",
- paste0("Theta(", paste0("period = ",
- x$estimation$periods), ")")),
- Coef = x$estimation$parameters,
- Coef_SE = sqrt(diag(x$estimation$covariance)),
- check.names = FALSE)
- est_ma_params$Tstat <- est_ma_params$Coef / est_ma_params$Coef_SE
-
- cat("\n")
- cat("Estimate MA parameters:")
- cat("\n")
- print(est_ma_params, row.names = FALSE)
- cat(ifelse(x$estimation$log, "Multiplicative", "Additive"), "model\n")
-
- cat("\n")
- cat("Decomposition:")
- cat("\n")
- decompo_table <- do.call(cbind, x$decomposition)
- if (x$estimation$log) {
- decompo_table <- exp(decompo_table)
- }
- decompo_table <- subset(decompo_table, select = -y_time)
- if (!is.null(x$decomposition$y_time)) {
- rownames(decompo_table) <- format(x$decomposition$y_time)
- }
- print(tail(decompo_table, n = 10))
- cat("\n")
-
- cat("Sum of square residuals:", formatC(x$likelihood$ssq, digits = digits),
- "on", x$likelihood$df, "degrees of freedom",
- sep = " ")
- cat("\n")
-
- cat("Log likelihood = ", formatC(x$likelihood$ll, digits = digits),
- ", \n\taic = ", formatC(x$likelihood$aic, digits = digits),
- ", \n\taicc = ", formatC(x$likelihood$aicc, digits = digits),
- ", \n\tbic(corrected for length) = ",
- formatC(x$likelihood$bicc, digits = digits), sep = "")
- cat("\n")
-
- cat("Hannan–Quinn information criterion = ",
- formatC(x$likelihood$hannanquinn, digits = digits), sep = "")
-
- cat("\n\n")
-
- return(invisible(x))
+print.JDFractionalAirlineDecomposition <- function(x, digits = max(3L, getOption("digits") - 3L), ...) {
+ cat("Number of observations:", formatC(x$likelihood$nobs, digits = digits))
+ cat("\n")
+
+ if (!is.null(x$decomposition$y_time)) {
+ cat("Start:", format(x$decomposition$y_time[1]),"\n")
+ cat("End:", format(x$decomposition$y_time[length(x$decomposition$y_time)]), "\n")
+ }
+
+ # Estimated MA parameters (coefs, se, student)
+ nb_freq <- length(x$estimation$parameters) - 1L
+ est_ma_params <- data.frame(
+ MA_parameter = c("Theta(1)",
+ paste0("Theta(", paste0("period = ",
+ x$estimation$periods), ")")),
+ Coef = x$estimation$parameters,
+ Coef_SE = sqrt(diag(x$estimation$covariance)),
+ check.names = FALSE)
+ est_ma_params$Tstat <- est_ma_params$Coef / est_ma_params$Coef_SE
+
+ cat("\n")
+ cat("Estimate MA parameters:")
+ cat("\n")
+ print(est_ma_params, row.names = FALSE)
+ cat(ifelse(x$estimation$log, "Multiplicative", "Additive"), "model\n")
+
+ cat("\n")
+ cat("Decomposition:")
+ cat("\n")
+ decompo_table <- do.call(cbind, x$decomposition)
+ if (x$estimation$log) {
+ decompo_table <- exp(decompo_table)
+ }
+
+ if (!is.null(x$decomposition$y_time)) {
+ decompo_table <- subset(decompo_table, select = -y_time)
+ rownames(decompo_table) <- format(x$decomposition$y_time)
+ }
+ print(tail(decompo_table, n = 10))
+ cat("\n")
+
+ cat("Sum of square residuals:", formatC(x$likelihood$ssq, digits = digits),
+ "on", x$likelihood$df, "degrees of freedom",
+ sep = " ")
+ cat("\n")
+
+ cat("Log likelihood = ", formatC(x$likelihood$ll, digits = digits),
+ ", \n\taic = ", formatC(x$likelihood$aic, digits = digits),
+ ", \n\taicc = ", formatC(x$likelihood$aicc, digits = digits),
+ ", \n\tbic(corrected for length) = ",
+ formatC(x$likelihood$bicc, digits = digits), sep = "")
+ cat("\n")
+
+ cat("Hannan–Quinn information criterion = ",
+ formatC(x$likelihood$hannanquinn, digits = digits), sep = "")
+
+ cat("\n\n")
+
+ return(invisible(x))
}
#' Print method for 'JDFractionalAirlineEstimation' objects
#'
#' This function prints informations on the result of a Fractional Airline model (classe JDFractionalAirlineEstimation).
-#'
+#'
#' @param x An object of class 'JDFractionalAirlineEstimation'.
#' @param digits Number of digits to round numerical values (default is 3 or digits - 3 from options).
#'
@@ -80,94 +80,88 @@ print.JDFractionalAirlineDecomposition <- function(x, digits = max(3L, getOption
#'
#' @export
print.JDFractionalAirlineEstimation <- function(x, digits = max(3L, getOption("digits") - 3L), ...) {
-
- cat("Number of observations:", formatC(x$likelihood$nobs, digits = digits))
- cat("\n")
-
- if (!is.null(x$model$y_time)) {
- cat("Start:", format(x$model$y_time[1]),"\n")
- cat("End:", format(x$model$y_time[length(x$model$y_time)]), "\n")
- }
-
- nb_outliers <- sum(toupper(substr(x$model$variables, 1L, 2L)) %in%
- c("AO", "WO", "LS"))
- nb_reg_cjo <- length(x$model$variables) - nb_outliers
-
- summary_coeff <- data.frame(
- "Variable" = x$model$variables,
- "Coef" = x$model$b,
- "Coef_SE" = sqrt(diag(x$model$bcov)))
- summary_coeff$Tstat <- round(summary_coeff$Coef / summary_coeff$Coef_SE, digits)
- summary_coeff$Coef <- round(summary_coeff$Coef, digits)
- summary_coeff$Coef_SE <- round(summary_coeff$Coef_SE, digits)
-
- if (nb_outliers > 0) {
- outliers_coeff <- summary_coeff[(nb_reg_cjo + 1L):nrow(summary_coeff), ]
-
+
+ cat("Number of observations:", formatC(x$likelihood$nobs, digits = digits))
+ cat("\n")
+
if (!is.null(x$model$y_time)) {
- outliers_type <- substr(outliers_coeff$Variable, start = 1, stop = 2)
- outliers_date <- x$model$y_time[as.integer(substr(outliers_coeff$Variable, start = 4, stop = 50))]
- outliers_coeff$Variable <- paste0(outliers_type, ".", outliers_date)
+ cat("Start:", format(x$model$y_time[1]),"\n")
+ cat("End:", format(x$model$y_time[length(x$model$y_time)]), "\n")
}
- }
-
- if (nb_reg_cjo > 0) {
- reg_cjo_coeff <- summary_coeff[seq_len(nb_reg_cjo), ]
- }
-
- # Estimated MA parameters (coefs, se, student)
- nb_freq <- length(x$estimation$parameters) - 1L
- est_ma_params <- data.frame(
- MA_parameter = c("Theta(1)",
- paste0("Theta(", paste0("period = ",
- x$model$periods), ")")),
- Coef = x$estimation$parameters,
- Coef_SE = sqrt(diag(x$estimation$covariance)),
- check.names = FALSE)
- est_ma_params$Tstat <- est_ma_params$Coef / est_ma_params$Coef_SE
-
- cat("\n")
- cat("Estimate MA parameters:")
- cat("\n")
- print(est_ma_params, row.names = FALSE)
-
- cat("\n")
- cat("Number of calendar regressors:", nb_reg_cjo, ", Number of outliers :", nb_outliers)
- cat("\n\n")
-
- if(nb_reg_cjo > 0) {
- cat("TD regressors coefficients:")
+
+ nb_outliers <- sum(toupper(substr(x$model$variables, 1L, 2L)) %in%
+ c("AO", "WO", "LS"))
+ nb_reg_cjo <- length(x$model$variables) - nb_outliers
+
+ summary_coeff <- data.frame(
+ "Variable" = x$model$variables,
+ "Coef" = x$model$b,
+ "Coef_SE" = sqrt(diag(x$model$bcov)))
+ summary_coeff$Tstat <- round(summary_coeff$Coef / summary_coeff$Coef_SE, digits)
+ summary_coeff$Coef <- round(summary_coeff$Coef, digits)
+ summary_coeff$Coef_SE <- round(summary_coeff$Coef_SE, digits)
+
+ if (nb_outliers > 0) {
+ outliers_coeff <- summary_coeff[(nb_reg_cjo + 1L):nrow(summary_coeff), ]
+ }
+
+ if (nb_reg_cjo > 0) {
+ reg_cjo_coeff <- summary_coeff[seq_len(nb_reg_cjo), ]
+ }
+
+ # Estimated MA parameters (coefs, se, student)
+ nb_freq <- length(x$estimation$parameters) - 1L
+ est_ma_params <- data.frame(
+ MA_parameter = c("Theta(1)",
+ paste0("Theta(period = ", x$model$periods, ")")),
+ Coef = x$estimation$parameters,
+ Coef_SE = sqrt(diag(x$estimation$covariance)),
+ check.names = FALSE
+ )
+ est_ma_params$Tstat <- est_ma_params$Coef / est_ma_params$Coef_SE
+
cat("\n")
- print(reg_cjo_coeff, row.names = FALSE)
- # print(head(reg_cjo_coeff, 10), row.names = FALSE)
- # if (nb_reg_cjo > 10) cat("...\n")
+ cat("Estimate MA parameters:")
cat("\n")
- }
-
- if(nb_outliers > 0) {
- cat("Outliers coefficients:")
+ print(est_ma_params, row.names = FALSE)
+
cat("\n")
- print(outliers_coeff, row.names = FALSE)
- # print(head(outliers_coeff, 10), row.names = FALSE)
- # if (nb_outliers > 10) cat("...\n")
+ cat("Number of calendar regressors:", nb_reg_cjo, ", Number of outliers :", nb_outliers)
+ cat("\n\n")
+
+ if(nb_reg_cjo > 0) {
+ cat("TD regressors coefficients:")
+ cat("\n")
+ print(reg_cjo_coeff, row.names = FALSE)
+ # print(head(reg_cjo_coeff, 10), row.names = FALSE)
+ # if (nb_reg_cjo > 10) cat("...\n")
+ cat("\n")
+ }
+
+ if(nb_outliers > 0) {
+ cat("Outliers coefficients:")
+ cat("\n")
+ print(outliers_coeff, row.names = FALSE)
+ # print(head(outliers_coeff, 10), row.names = FALSE)
+ # if (nb_outliers > 10) cat("...\n")
+ cat("\n")
+ }
+
+ cat("Sum of square residuals:", formatC(x$likelihood$ssq, digits = digits),
+ "on", x$likelihood$df, "degrees of freedom",
+ sep = " ")
+ cat("\n")
+
+ cat("Log likelihood = ", formatC(x$likelihood$ll, digits = digits),
+ ", \n\taic = ", formatC(x$likelihood$aic, digits = digits),
+ ", \n\taicc = ", formatC(x$likelihood$aicc, digits = digits),
+ ", \n\tbic(corrected for length) = ",
+ formatC(x$likelihood$bicc, digits = digits), sep = "")
cat("\n")
- }
-
- cat("Sum of square residuals:", formatC(x$likelihood$ssq, digits = digits),
- "on", x$likelihood$df, "degrees of freedom",
- sep = " ")
- cat("\n")
-
- cat("Log likelihood = ", formatC(x$likelihood$ll, digits = digits),
- ", \n\taic = ", formatC(x$likelihood$aic, digits = digits),
- ", \n\taicc = ", formatC(x$likelihood$aicc, digits = digits),
- ", \n\tbic(corrected for length) = ",
- formatC(x$likelihood$bicc, digits = digits), sep = "")
- cat("\n")
-
- cat("Hannan–Quinn information criterion = ",
- formatC(x$likelihood$hannanquinn, digits = digits), sep = "")
-
- cat("\n\n")
- return(invisible(x))
+
+ cat("Hannan–Quinn information criterion = ",
+ formatC(x$likelihood$hannanquinn, digits = digits), sep = "")
+
+ cat("\n\n")
+ return(invisible(x))
}
diff --git a/R/utils.R b/R/utils.R
index d8fa7a0..4eec7b5 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,6 +1,6 @@
#' @importFrom rJava .jpackage .jcall .jnull .jarray .jevalArray .jcast .jcastToArray .jinstanceof is.jnull .jnew .jclass
#' @importFrom RProtoBuf readProtoFiles2
#' @importFrom stats frequency is.ts pt start ts
-#' @importFrom rjd3toolkit result dictionary
-#' @importFrom methods new
+#' @importFrom rjd3toolkit result dictionary
+#' @importFrom methods new
NULL
diff --git a/R/zzz.R b/R/zzz.R
index 4518ca3..4f475b0 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -2,15 +2,14 @@
#' @import rjd3sts
.onLoad <- function(libname, pkgname) {
- if (! requireNamespace("rjd3sts", quietly=T)) stop("Loading rjd3 libraries failed")
+ if (! requireNamespace("rjd3sts", quietly=TRUE)) stop("Loading rjd3 libraries failed")
result <- .jpackage(pkgname, lib.loc=libname)
if (!result) stop("Loading java packages failed")
#proto.dir <- system.file("proto", package = pkgname)
#RProtoBuf::readProtoFiles2(protoPath = proto.dir)
-
+
# reload extractors
.jcall("jdplus/toolkit/base/api/information/InformationExtractors", "V", "reloadExtractors")
}
-
diff --git a/README.Rmd b/README.Rmd
index f7130b3..3b1eff6 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -8,7 +8,7 @@ output: github_document
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
- fig.path = "man/figures/README-",
+ fig.path = "man/figures/README-",
fig.align = "center",
fig.dim = c(7, 4) * 1.4,
out.width = "100%"
@@ -55,8 +55,8 @@ Plot of the raw series:
```{r raw data plot, echo = FALSE, eval = TRUE}
# Plot of the raw data ----------------------------------------------------
-rjd3highfreq:::plot_jd(x = df_daily$date, y = list(df_daily$births),
- col = "#f1ba1d", main = "French daily birth",
+rjd3highfreq:::plot_jd(x = df_daily$date, y = list(df_daily$births),
+ col = "#f1ba1d", main = "French daily birth",
ylab = "Nbr birth")
```
@@ -84,8 +84,8 @@ Creation of the calendar regressor in a matrix with the package **rjd3toolkit**:
```{r regressor matrix creation, echo = TRUE, eval = TRUE}
# Calendar regressor matrix
cal_reg <- rjd3toolkit::holidays(
- calendar = frenchCalendar,
- start = "1968-01-01", length = nrow(df_daily),
+ calendar = frenchCalendar,
+ start = "1968-01-01", length = nrow(df_daily),
type = "All", nonworking = 7L)
colnames(cal_reg) <- c("14th_july", "8th_may", "1st_jan", "1st_may",
@@ -97,8 +97,8 @@ Preprocessing with the function `fractionalAirlineEstimation`:
```{r preprocessing, echo = TRUE, eval = TRUE}
pre_pro <- fractionalAirlineEstimation(
- y = df_daily$births,
- x = cal_reg,
+ y = df_daily$births,
+ x = cal_reg,
periods = 7, # weekly frequency
outliers = c("ao", "wo"), log = TRUE, y_time = df_daily$date)
@@ -107,8 +107,8 @@ print(pre_pro)
```{r preprocessing plots, echo = TRUE, eval = TRUE}
plot(pre_pro, main = "French births")
-plot(x = pre_pro,
- from = as.Date("2000-01-01"), to = as.Date("2000-12-31"),
+plot(x = pre_pro,
+ from = as.Date("2000-01-01"), to = as.Date("2000-12-31"),
main = "French births in 2000")
```
@@ -119,7 +119,7 @@ Decomposition with the AMB (Arima Model Based) algorithm:
# Decomposition with weekly pattern
amb.dow <- rjd3highfreq::fractionalAirlineDecomposition(
y = pre_pro$model$linearized, # linearized series from preprocessing
- period = 7,
+ period = 7,
log = TRUE, y_time = df_daily$date)
# Extract day-of-year pattern from day-of-week-adjusted linearised data
@@ -137,8 +137,8 @@ plot(amb.dow, main = "Weekly pattern")
```
```{r amb plot 2, echo = TRUE, eval = TRUE}
-plot(amb.dow, main = "Weekly pattern - January 2018",
- from = as.Date("2018-01-01"),
+plot(amb.dow, main = "Weekly pattern - January 2018",
+ from = as.Date("2018-01-01"),
to = as.Date("2018-01-31"))
```
@@ -147,8 +147,8 @@ plot(amb.doy, main = "Yearly pattern")
```
```{r amb plot 4, echo = TRUE, eval = TRUE}
-plot(amb.doy, main = "Weekly pattern - 2000 - 2002",
- from = as.Date("2000-01-01"),
+plot(amb.doy, main = "Weekly pattern - 2000 - 2002",
+ from = as.Date("2000-01-01"),
to = as.Date("2002-12-31"))
```
@@ -169,10 +169,19 @@ plot(amb.multi)
```
```{r plot amb.multi 2, echo = TRUE, eval = TRUE}
-plot(amb.multi, main = "2012",
- from = as.Date("2012-01-01"),
+plot(amb.multi, main = "2012",
+ from = as.Date("2012-01-01"),
to = as.Date("2012-12-31"))
```
With the package [**rjd3x11plus**](https://github.com/rjdemetra/rjd3x11plus), you can perform an X-11 like decomposition with any (non integer) periodicity.
+
+
+## Contributing
+
+Any contribution is welcome and should be done through pull requests and/or issues.
+
+## Licensing
+
+The code of this project is licensed under the [European Union Public Licence (EUPL)](https://joinup.ec.europa.eu/page/eupl-text-11-12).
diff --git a/README.md b/README.md
index 5d258b6..a193bf6 100644
--- a/README.md
+++ b/README.md
@@ -63,8 +63,8 @@ Creation of the calendar regressor in a matrix with the package
``` r
# Calendar regressor matrix
cal_reg <- rjd3toolkit::holidays(
- calendar = frenchCalendar,
- start = "1968-01-01", length = nrow(df_daily),
+ calendar = frenchCalendar,
+ start = "1968-01-01", length = nrow(df_daily),
type = "All", nonworking = 7L)
colnames(cal_reg) <- c("14th_july", "8th_may", "1st_jan", "1st_may",
@@ -76,8 +76,8 @@ Preprocessing with the function `fractionalAirlineEstimation`:
``` r
pre_pro <- fractionalAirlineEstimation(
- y = df_daily$births,
- x = cal_reg,
+ y = df_daily$births,
+ x = cal_reg,
periods = 7, # weekly frequency
outliers = c("ao", "wo"), log = TRUE, y_time = df_daily$date)
@@ -132,8 +132,8 @@ plot(pre_pro, main = "French births")
``` r
-plot(x = pre_pro,
- from = as.Date("2000-01-01"), to = as.Date("2000-12-31"),
+plot(x = pre_pro,
+ from = as.Date("2000-01-01"), to = as.Date("2000-12-31"),
main = "French births in 2000")
```
@@ -145,7 +145,7 @@ Decomposition with the AMB (Arima Model Based) algorithm:
# Decomposition with weekly pattern
amb.dow <- rjd3highfreq::fractionalAirlineDecomposition(
y = pre_pro$model$linearized, # linearized series from preprocessing
- period = 7,
+ period = 7,
log = TRUE, y_time = df_daily$date)
# Extract day-of-year pattern from day-of-week-adjusted linearised data
@@ -164,8 +164,8 @@ plot(amb.dow, main = "Weekly pattern")
``` r
-plot(amb.dow, main = "Weekly pattern - January 2018",
- from = as.Date("2018-01-01"),
+plot(amb.dow, main = "Weekly pattern - January 2018",
+ from = as.Date("2018-01-01"),
to = as.Date("2018-01-31"))
```
@@ -178,8 +178,8 @@ plot(amb.doy, main = "Yearly pattern")
``` r
-plot(amb.doy, main = "Weekly pattern - 2000 - 2002",
- from = as.Date("2000-01-01"),
+plot(amb.doy, main = "Weekly pattern - 2000 - 2002",
+ from = as.Date("2000-01-01"),
to = as.Date("2002-12-31"))
```
@@ -205,8 +205,8 @@ plot(amb.multi)
``` r
-plot(amb.multi, main = "2012",
- from = as.Date("2012-01-01"),
+plot(amb.multi, main = "2012",
+ from = as.Date("2012-01-01"),
to = as.Date("2012-12-31"))
```
@@ -215,3 +215,13 @@ plot(amb.multi, main = "2012",
With the package
[**rjd3x11plus**](https://github.com/rjdemetra/rjd3x11plus), you can
perform an X-11 like decomposition with any (non integer) periodicity.
+
+## Contributing
+
+Any contribution is welcome and should be done through pull requests
+and/or issues.
+
+## Licensing
+
+The code of this project is licensed under the [European Union Public
+Licence (EUPL)](https://joinup.ec.europa.eu/page/eupl-text-11-12).
diff --git a/man/fractionalAirlineDecomposition.Rd b/man/fractionalAirlineDecomposition.Rd
index 99b46c8..a5b1866 100644
--- a/man/fractionalAirlineDecomposition.Rd
+++ b/man/fractionalAirlineDecomposition.Rd
@@ -7,8 +7,8 @@
fractionalAirlineDecomposition(
y,
period,
- sn = F,
- stde = F,
+ sn = FALSE,
+ stde = FALSE,
nbcasts = 0,
nfcasts = 0,
log = FALSE,
diff --git a/man/fractionalAirlineDecomposition_raw.Rd b/man/fractionalAirlineDecomposition_raw.Rd
index 1c7ca70..4266a7b 100644
--- a/man/fractionalAirlineDecomposition_raw.Rd
+++ b/man/fractionalAirlineDecomposition_raw.Rd
@@ -7,23 +7,13 @@
fractionalAirlineDecomposition_raw(
y,
period,
- sn = F,
- stde = F,
+ sn = FALSE,
+ stde = FALSE,
nbcasts = 0,
nfcasts = 0
)
}
\arguments{
-\item{y}{}
-
-\item{period}{}
-
-\item{sn}{}
-
-\item{stde}{}
-
-\item{nbcasts}{}
-
\item{nfcasts}{}
}
\description{
diff --git a/man/fractionalAirlineEstimation.Rd b/man/fractionalAirlineEstimation.Rd
index 636628a..d210a1f 100644
--- a/man/fractionalAirlineEstimation.Rd
+++ b/man/fractionalAirlineEstimation.Rd
@@ -9,12 +9,12 @@ fractionalAirlineEstimation(
periods,
x = NULL,
ndiff = 2,
- ar = F,
+ ar = FALSE,
mean = FALSE,
outliers = NULL,
criticalValue = 6,
precision = 1e-12,
- approximateHessian = F,
+ approximateHessian = FALSE,
nfcasts = 0,
log = FALSE,
y_time = NULL
diff --git a/man/jd2r_fractionalAirlineDecomposition.Rd b/man/jd2r_fractionalAirlineDecomposition.Rd
index 4b05e63..b676968 100644
--- a/man/jd2r_fractionalAirlineDecomposition.Rd
+++ b/man/jd2r_fractionalAirlineDecomposition.Rd
@@ -6,20 +6,14 @@
\usage{
jd2r_fractionalAirlineDecomposition(
jrslt,
- sn = F,
- stde = F,
+ sn = FALSE,
+ stde = FALSE,
period,
log = FALSE,
y_time = NULL
)
}
\arguments{
-\item{jrslt}{}
-
-\item{sn}{}
-
-\item{stde}{}
-
\item{y_time}{vector of times at which the time series is indexed}
}
\description{
diff --git a/man/jd2r_multiAirlineDecomposition.Rd b/man/jd2r_multiAirlineDecomposition.Rd
index 407e487..47b90c2 100644
--- a/man/jd2r_multiAirlineDecomposition.Rd
+++ b/man/jd2r_multiAirlineDecomposition.Rd
@@ -6,17 +6,13 @@
\usage{
jd2r_multiAirlineDecomposition(
jrslt,
- stde = F,
+ stde = FALSE,
periods,
log = FALSE,
y_time = NULL
)
}
\arguments{
-\item{jrslt}{}
-
-\item{stde}{}
-
\item{y_time}{vector of times at which the time series is indexed}
}
\description{
diff --git a/man/multiAirlineDecomposition.Rd b/man/multiAirlineDecomposition.Rd
index a938ac9..9c8dc04 100644
--- a/man/multiAirlineDecomposition.Rd
+++ b/man/multiAirlineDecomposition.Rd
@@ -8,8 +8,8 @@ multiAirlineDecomposition(
y,
periods,
ndiff = 2,
- ar = F,
- stde = F,
+ ar = FALSE,
+ stde = FALSE,
nbcasts = 0,
nfcasts = 0,
log = FALSE,
diff --git a/man/multiAirlineDecomposition_raw.Rd b/man/multiAirlineDecomposition_raw.Rd
index f35584e..ba74df5 100644
--- a/man/multiAirlineDecomposition_raw.Rd
+++ b/man/multiAirlineDecomposition_raw.Rd
@@ -8,23 +8,13 @@ multiAirlineDecomposition_raw(
y,
periods,
ndiff = 2,
- ar = F,
- stde = F,
+ ar = FALSE,
+ stde = FALSE,
nbcasts = 0,
nfcasts = 0
)
}
\arguments{
-\item{y}{}
-
-\item{periods}{}
-
-\item{ndiff}{}
-
-\item{stde}{}
-
-\item{nbcasts}{}
-
\item{nfcasts}{}
}
\description{
diff --git a/rjd3highfreq.Rproj b/rjd3highfreq.Rproj
index fde0cd9..62ab696 100644
--- a/rjd3highfreq.Rproj
+++ b/rjd3highfreq.Rproj
@@ -1,17 +1,20 @@
Version: 1.0
-RestoreWorkspace: Default
-SaveWorkspace: Default
-AlwaysSaveHistory: Default
+RestoreWorkspace: No
+SaveWorkspace: No
+AlwaysSaveHistory: No
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
-NumSpacesForTab: 2
+NumSpacesForTab: 4
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
BuildType: Package
PackageUseDevtools: Yes
PackageInstallArgs: --no-multiarch --with-keep.source
@@ -19,4 +22,6 @@ PackageBuildBinaryArgs: --no-multiarch
PackageCheckArgs: --no-multiarch
PackageRoxygenize: rd,collate,namespace,vignette
+UseNativePipeOperator: Yes
+
SpellingDictionary: en_GB