From e034ef0b48e8933c9aeddd825191df8c8f12447c Mon Sep 17 00:00:00 2001 From: Jean Palate <8858782+palatej@users.noreply.github.com> Date: Thu, 18 Apr 2024 17:13:37 +0200 Subject: [PATCH 01/27] Update DESCRIPTION --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 6588889..4bafc33 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: rjd3filters Type: Package Title: Trend-Cycle Extraction with Linear Filters -Version: 2.1.0 +Version: 2.1.1 Authors@R: c( person("Jean", "Palate", role = c("aut", "cre"), email = "jean.palate@nbb.be"), From e2d443b76484dbb7c9f8b99755abbed77bf2fc51 Mon Sep 17 00:00:00 2001 From: Anna Smyk <43274870+annasmyk@users.noreply.github.com> Date: Sat, 20 Apr 2024 17:03:07 +0200 Subject: [PATCH 02/27] Update README.Rmd --- README.Rmd | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.Rmd b/README.Rmd index e273632..a194f93 100644 --- a/README.Rmd +++ b/README.Rmd @@ -29,7 +29,9 @@ Some quality criteria defined by Wildi and McElroy (2019) can also be computed. ## Installation -rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and Java SE 17 or later version is required. +rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#installing-the-software) + + To get the current stable version (from the latest release): From 311cb9c7112bc3d9935ab10fb27923bf5783a128 Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 15:58:03 +0200 Subject: [PATCH 03/27] new functions and parameters --- NAMESPACE | 10 ++ NEWS.md | 13 ++ R/1_moving_average.R | 5 +- R/2_finite_filters.R | 6 + R/crossvalidation.R | 233 +++++++++++++++++++++++++++++++--- _pkgdown.yml | 9 +- man/confint_filter.Rd | 57 +++++++++ man/cross_validation.Rd | 36 ------ man/deprecated-rjd3filters.Rd | 19 +++ man/diagnostics-fit.Rd | 99 +++++++++++++++ man/filters_operations.Rd | 3 + man/finite_filters.Rd | 5 + man/var_estimator.Rd | 12 +- rjd3filters.Rproj | 2 +- 14 files changed, 450 insertions(+), 59 deletions(-) create mode 100644 man/confint_filter.Rd delete mode 100644 man/cross_validation.Rd create mode 100644 man/deprecated-rjd3filters.Rd create mode 100644 man/diagnostics-fit.Rd diff --git a/NAMESPACE b/NAMESPACE index 41b479b..b4184cd 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -38,7 +38,11 @@ S3method(sum,moving_average) S3method(to_seasonal,default) S3method(to_seasonal,finite_filters) export(.jd2r_finitefilters) +export(confint_filter) +export(cp) export(cross_validation) +export(cv) +export(cve) export(dfa_filter) export(diagnostic_matrix) export(filter) @@ -50,9 +54,11 @@ export(get_moving_average) export(get_properties_function) export(implicit_forecast) export(impute_last_obs) +export(is.finite_filters) export(is.moving_average) export(is_symmetric) export(localpolynomials) +export(loocve) export(lower_bound) export(lp_filter) export(mirror) @@ -65,6 +71,7 @@ export(rkhs_filter) export(rkhs_kernel) export(rkhs_optimal_bw) export(rkhs_optimization_fun) +export(rt) export(simple_ma) export(to_seasonal) export(upper_bound) @@ -91,8 +98,11 @@ importFrom(rjd3toolkit,.r2jd_matrix) importFrom(stats,coef) importFrom(stats,coefficients) importFrom(stats,deltat) +importFrom(stats,end) importFrom(stats,frequency) importFrom(stats,is.ts) +importFrom(stats,qnorm) importFrom(stats,start) importFrom(stats,time) importFrom(stats,ts) +importFrom(stats,ts.union) diff --git a/NEWS.md b/NEWS.md index e14ad43..01dea13 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,6 +7,19 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] +### Changed + +* `cross_validation()` function renamed to `cve()`, `cross_validation()` is now deprecated. + +### News + +* New functions to compute functions to compute diagnostics and goodness of fit of filtered series: cross validation (`cv()`) and cross validate estimate (`cve()`), leave-one-out cross validation estimate (`loocve`), CP statistic (`cp()`) and Rice's T statistics (`rt()`). + +* New function `confint_filter()` to compute confidence intervals for filtered series. + +* New function `is.finite_filters()`. + +* New parameter `zero_as_na` in `cbind.moving_average`, boolean indicating if trealing and leading zeros added to have a matrix form should be replaced by `NA`. ## [2.0.0] - 2023-12-12 diff --git a/R/1_moving_average.R b/R/1_moving_average.R index ab2e302..7194a19 100644 --- a/R/1_moving_average.R +++ b/R/1_moving_average.R @@ -13,6 +13,9 @@ setClass("moving_average", #' @param i,j,value indices specifying elements to extract or replace and the new value #' #' @param ...,drop,na.rm other parameters. +#' @param zero_as_na boolean indicating if, when merging several moving averages (`cbind`) +#' if trealing and leading zeros added to have a matrix form should be replaced by `NA`. +#' #' @name filters_operations NULL @@ -113,7 +116,7 @@ moving_average <- function(x, lags = -length(x), trailing_zero = FALSE, leading_ is.moving_average <- function(x){ is(x, "moving_average") } -#' @importFrom stats coef coefficients +#' @importFrom stats coef coefficients end qnorm ts.union #' @export coef.moving_average <- function(object, ...){ coefs <- object@coefficients diff --git a/R/2_finite_filters.R b/R/2_finite_filters.R index e8e2134..72bf370 100644 --- a/R/2_finite_filters.R +++ b/R/2_finite_filters.R @@ -12,6 +12,7 @@ setClass("finite_filters", #' @param first_to_last boolean indicating if the first element of `rfilters` is the #' first asymmetric filter (when only one observation is missing) or the last one (real-time estimates). #' @param object `finite_filters` object. +#' @param x object to test the class. #' #' @examples #' ff_lp <- lp_filter() @@ -81,6 +82,11 @@ finite_filters.matrix <- function(sfilter, finite_filters(coefs, first_to_last = first_to_last) } +#' @rdname finite_filters +#' @export +is.finite_filters <- function(x){ + is(x, "finite_filters") +} #' @export .jd2r_finitefilters <- function(jf, first_to_last){ diff --git a/R/crossvalidation.R b/R/crossvalidation.R index 4dd83fb..24149ee 100644 --- a/R/crossvalidation.R +++ b/R/crossvalidation.R @@ -1,30 +1,79 @@ -#' Cross-Validation +#' Diagnostics and goodness of fit of filtered series #' -#' Computes the cross-validation statistic of a moving-average on a time series +#' Set of functions to compute diagnostics and goodness of fit of filtered series: +#' cross validation (`cv()`) and cross validate estimate (`cve()`), +#' leave-one-out cross validation estimate (`loocve`), +#' CP statistic (`cp()`) and Rice's T statistics (`rt()`). #' #' @param x input time series. #' @param coef vector of coefficients or a moving-average ([moving_average()]). #' @param ... other arguments passed to the function [moving_average()] to convert `coef` to a `"moving_average"` object. +#' @param var variance used to compute the CP statistic (`cp()`). #' -#' @details The cross-validation statistics of a moving average \eqn{(\theta_i)_{-p\leq i \leq p}} on a time series \eqn{(y_i)_{1\leq i \leq n}} is: +#' @details Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +#' to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +#' Let denote \eqn{\hat{\mu}_t} the filtered series computed at time \eqn{t} as: #' \deqn{ -#' \frac{1}{n-(p+q)} -#' \sum_{t=p+1}^{n-q} -#' \left( -#' \frac{y_t - \sum_{i=-p}^q \theta_i y_{t+i}}{ -#' 1-\theta_0 +#' \hat{\mu}_t = \sum_{i=-p}^q \theta_i y_{t+i}. +#' } +#' +#' The cross validation estimate (`cve()`) is defined as the time series \eqn{Y_t-\hat{\mu}_{-t}} where +#' \eqn{\hat{\mu}_{-t}} is the leave-one-out cross validation estimate (`loocve()`) defined as the filtered series +#' computed deleting the observation \eqn{t} and remaining all the other points. +#' The cross validation statistics (`cv()`) is defined as: +#' \deqn{ +#' CV=\frac{1}{n-(p+q)} +#' \sum_{t=p+1}^{n-q} \left(y_t - \hat{\mu}_{-t}\right)^2. +#' } +#' In the case of filtering with a moving average, we can show that: +#' \deqn{ +#' \hat{\mu}_{-t}= \frac{\hat{\mu}_t - \theta_0 y_t}{1-\theta_0} #' } -#' \right)^2. +#' and +#' \deqn{ +#' CV=\frac{1}{n-(p+q)} +#' \sum_{t=p+1}^{n-q} \left(\frac{y_t - \hat{\mu}_{t}}{1-\theta_0}\right)^2. #' } -#' The function `cross_validation()` returns the time series \eqn{(x_t)}) with +#' +#' In the case of filtering with a moving average, +#' the CP estimate of risk (introduced by Mallows (1973); `cp()`) can be defined as: #' \deqn{ -#' x_t = \frac{y_t - \sum_{i=-p}^q \theta_i y_{t+i}}{ -#' 1-\theta_0 -#' }. +#' CP=\frac{1}{\sigma^2} +#' \sum_{t=p+1}^{n-q} \left(y_t - \hat{\mu}_{t}\right)^2 +#' -(n-(p+q))(1-2\theta_0). +#' } +#' The CP method requires an estimate of \eqn{\sigma^2} (`var` parameter). +#' The usual use of CP is to compare several different fits (for example different bandwidths): +#' one should use the same estimate of \eqn{\hat{\sigma}^2} for all fits (using for example [var_estimator()]). +#' The recommendation of Cleveland and Devlin (1988) is to compute \eqn{\hat{\sigma}^2} +#' from a fit at the smallest bandwidth under consideration, +#' at which one should be willing to assume that bias is negligible. +#' +#' The Rice's T statistic (`rt()`) is defined as: +#'\deqn{ +#' \frac{1}{n-(p+q)} +#' \sum_{t=p+1}^{n-q} +#' \frac{ +#' \left(y_t - \hat{\mu}_{t}\right)^2 +#' }{ +#' 1-2\theta_0 #' } +#'} +#' @references +#' Loader, Clive. 1999. +#' Local regression and likelihood. +#' New York: Springer-Verlag. +#' +#' Mallows, C. L. (1973). Some comments on Cp. +#' Technometrics 15, 661– 675. #' +#' Cleveland, W. S. and S. J. Devlin (1988). +#' Locally weighted regression: An approach to regression analysis by local fitting. +#' Journal of the American Statistical Association 83, 596–610. +#' @name diagnostics-fit +#' @rdname diagnostics-fit #' @export -cross_validation <- function(x, coef, ...){ +cve <- function(x, coef, ...){ coef <- moving_average(coef, ...) if (lower_bound(coef) > 0 || upper_bound(coef) < 0) return(NA) @@ -32,16 +81,64 @@ cross_validation <- function(x, coef, ...){ coef0 <- coef(coef)["t"] (x-sc)/(1-coef0) } +#' @rdname diagnostics-fit +#' @export +cv <- function(x, coef, ...){ + mean(cve(x, coef, ...)^2, na.rm = TRUE) +} +#' @rdname diagnostics-fit +#' @export +loocve <- function(x, coef, ...){ + coef <- moving_average(coef, ...) + if (lower_bound(coef) > 0 || upper_bound(coef) < 0) + return(NA) + sc <- filter(x, coef) + coef0 <- coef(coef)["t"] + (sc - coef0 * x)/(1-coef0) +} + +#' @rdname diagnostics-fit +#' @export +rt <- function(x, coef, ...){ + coef <- moving_average(coef, ...) + if (lower_bound(coef) > 0 || upper_bound(coef) < 0) + return(NA) + sc <- filter(x, coef) + coef0 <- coef(coef)["t"] + mean((x-sc)^2, na.rm = TRUE)/(1-2*coef0) +} + +#' @rdname diagnostics-fit +#' @export +cp <- function(x, coef, var, ...){ + mean(cve(x, coef, ...)^2, na.rm = TRUE) + coef <- moving_average(coef, ...) + if (lower_bound(coef) > 0 || upper_bound(coef) < 0) + return(NA) + sc <- filter(x, coef) + coef0 <- coef(coef)["t"] + mse <- (x-sc)^2 + nb_obs <- sum(!is.na(mse)) + (1 / var) * sum(mse, na.rm = TRUE) - nb_obs * (1 - 2 * coef0) +} #' Variance Estimator #' -#' @inheritParams cross_validation +#' @inheritParams diagnostics-fit #' #' @details +#' Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +#' to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +#' It is equivalent to a local regression and the associated error variance \eqn{\sigma^2} +#' can be estimated using the normalized residual sum of squares, which can be simplified as: #' \deqn{ -#' \hat\sigma^2=\frac{1}{n-2h}\sum_{t=h+1}^{n-h}\frac{(y_t-\hat \mu_t)^2}{1-2w_0^2+\sum w_i^2} +#' \hat\sigma^2=\frac{1}{n-(p+q)}\sum_{t=p+1}^{n-q} +#' \frac{(y_t-\hat \mu_t)^2}{1-2w_0^2+\sum_{i=-p}^q w_i^2} #' } -#' +#' @references +#' Loader, Clive. 1999. +#' Local regression and likelihood. +#' New York: Springer-Verlag. #' @export var_estimator <- function(x, coef, ...) { coef <- moving_average(coef, ...) @@ -50,8 +147,108 @@ var_estimator <- function(x, coef, ...) { sc <- filter(x, coef) coef0 <- coefficients(coef)["t"] sigma2 <- mean((x - sc)^2, - na.rm = TRUE) + na.rm = TRUE) sigma2 <- sigma2/(1- 2 * coef0 + sum(coefficients(coef)^2)) names(sigma2) <- NULL sigma2 } + +#' Confidence intervals +#' +#' @inheritParams diagnostics-fit +#' @param coef moving-average ([moving_average()]) or finite filter ([finite_filters()]) used to filter the series. +#' @param coef_var moving-average ([moving_average()]) or finite filter ([finite_filters()]) used compute the variance (throw [var_estimator()]). +#' By default equal to `coef`. +#' @param level confidence level. +#' @param ... other arguments passed to the function [moving_average()] to convert `coef` to a `"moving_average"` object. +# +#' @details +#' Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +#' to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +#' Let denote \eqn{\hat{\mu}_t} the filtered series computed at time \eqn{t} as: +#' \deqn{ +#' \hat{\mu}_t = \sum_{i=-p}^q \theta_i y_{t+i}. +#' } +#' If \eqn{\hat{\mu}_t} is unbiased, a approximate confidence for the true mean is: +#' \deqn{ +#' \left[\hat{\mu}_t - z_{1-\alpha/2} \hat{\sigma} \sqrt{\sum_{i=-p}^q\theta_i^2}; +#' \hat{\mu}_t + z_{1-\alpha/2} \hat{\sigma} \sqrt{\sum_{i=-p}^q\theta_i^2} +#' \right], +#' } +#' where \eqn{z_{1-\alpha/2}} is the quantile \eqn{1-\alpha/2} of the standard normal distribution. +#' +#' The estimate of the variance \eqn{\hat{\sigma}} is obtained using [var_estimator()] with the parameter `coef_var`. +#' The assumption that \eqn{\hat{\mu}_t} is unbiased is rarely exactly true, so variance estimates and confidence intervals are usually computed at small bandwidths where bias is small. +#' +#' When `coef` (or `coef_var`) is a finite filter, the last points of the confidence interval are +#' computed using the corresponding asymmetric filters +#' +#' @references +#' Loader, Clive. 1999. +#' Local regression and likelihood. +#' New York: Springer-Verlag. +#' +#' @examples +#' x <- retailsa$DrinkingPlaces +#' coef <- lp_filter(6) +#' confit <- confint_filter(x, coef) +#' plot(confit, plot.type = "single", +#' col = c("red", "black", "black"), +#' lty = c(1, 2, 2)) +#' @export +confint_filter <- function(x, coef, coef_var = coef, level = 0.95, ...){ + filtered <- filter(x, coef) + c <- (1 - level) / 2 + c <- c(c, 1 - c) + qnc <- qnorm(c) + if (is.moving_average(coef)) { + corr_f <- sqrt(sum(coefficients(coef)^2)) + } else if (is.finite_filters(coef)) { + corr_f <- ts(sqrt(sum(coefficients(coef@sfilter)^2)), + start = start(filtered), end = end(filtered), + frequency = frequency(filtered)) + lfilters <- coef@lfilters + rfilters <- coef@rfilters + for (i in seq_along(lfilters)){ + corr_f[i] <- sqrt(sum(coefficients(lfilters[[i]])^2)) + } + for (i in seq_along(rfilters)){ + corr_f[length(corr_f) - length(rfilters) + i] <- + sqrt(sum(coefficients(rfilters[[i]])^2)) + } + } + + if (is.moving_average(coef_var)) { + var <- var_estimator(x, coef_var) + } else if (is.finite_filters(coef_var)) { + var <- ts(var_estimator(x, coef_var@sfilter), + start = start(filtered), end = end(filtered), + frequency = frequency(filtered)) + lfilters <- coef_var@lfilters + rfilters <- coef_var@rfilters + for (i in seq_along(lfilters)){ + var[i] <- var_estimator(x, lfilters[[i]]) + } + for (i in seq_along(rfilters)){ + var[length(var) - length(rfilters) + i] <- + var_estimator(x, rfilters[[i]]) + } + } + + inf <- filtered + qnc[1] * sqrt(var) * corr_f + sup <- filtered + qnc[2] * sqrt(var) * corr_f + res <- ts.union(filtered, inf, sup) + colnames(res) <- c("filtered", sprintf("%.1f%%", c * 100)) + res +} + +#' Deprecated function +#' +#' @inheritParams diagnostics-fit +#' @name deprecated-rjd3filters +#' @rdname deprecated-rjd3filters +#' @export +cross_validation <- function(x, coef, ...){ + .Deprecated("cve") + cve(x, coef, ...) +} diff --git a/_pkgdown.yml b/_pkgdown.yml index e3f2193..b479540 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -17,13 +17,14 @@ reference: - '`get_kernel`' - title: Stastics on Moving Averages and estimates contents: - - '`cross_validation`' + - '`diagnostics-fit`' - '`diagnostic_matrix`' - '`fst`' - '`implicit_forecast`' - '`get_properties_function`' - '`mse`' - '`var_estimator`' + - '`confint_filter`' - title: Manipulation of Moving Averages and Finite Filters contents: @@ -36,4 +37,8 @@ reference: - '`plot_filters`' - title: Data contents: - - '`retailsa`' \ No newline at end of file + - '`retailsa`' + +- title: Deprecated functions + contents: + - '`deprecated-rjd3filters`' diff --git a/man/confint_filter.Rd b/man/confint_filter.Rd new file mode 100644 index 0000000..d0629c8 --- /dev/null +++ b/man/confint_filter.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/crossvalidation.R +\name{confint_filter} +\alias{confint_filter} +\title{Confidence intervals} +\usage{ +confint_filter(x, coef, coef_var = coef, level = 0.95, ...) +} +\arguments{ +\item{x}{input time series.} + +\item{coef}{moving-average (\code{\link[=moving_average]{moving_average()}}) or finite filter (\code{\link[=finite_filters]{finite_filters()}}) used to filter the series.} + +\item{coef_var}{moving-average (\code{\link[=moving_average]{moving_average()}}) or finite filter (\code{\link[=finite_filters]{finite_filters()}}) used compute the variance (throw \code{\link[=var_estimator]{var_estimator()}}). +By default equal to \code{coef}.} + +\item{level}{confidence level.} + +\item{...}{other arguments passed to the function \code{\link[=moving_average]{moving_average()}} to convert \code{coef} to a \code{"moving_average"} object.} +} +\description{ +Confidence intervals +} +\details{ +Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +Let denote \eqn{\hat{\mu}_t} the filtered series computed at time \eqn{t} as: +\deqn{ +\hat{\mu}_t = \sum_{i=-p}^q \theta_i y_{t+i}. +} +If \eqn{\hat{\mu}_t} is unbiased, a approximate confidence for the true mean is: +\deqn{ +\left[\hat{\mu}_t - z_{1-\alpha/2} \hat{\sigma} \sqrt{\sum_{i=-p}^q\theta_i^2}; +\hat{\mu}_t + z_{1-\alpha/2} \hat{\sigma} \sqrt{\sum_{i=-p}^q\theta_i^2} +\right], +} +where \eqn{z_{1-\alpha/2}} is the quantile \eqn{1-\alpha/2} of the standard normal distribution. + +The estimate of the variance \eqn{\hat{\sigma}} is obtained using \code{\link[=var_estimator]{var_estimator()}} with the parameter \code{coef_var}. +The assumption that \eqn{\hat{\mu}_t} is unbiased is rarely exactly true, so variance estimates and confidence intervals are usually computed at small bandwidths where bias is small. + +When \code{coef} (or \code{coef_var}) is a finite filter, the last points of the confidence interval are +computed using the corresponding asymmetric filters +} +\examples{ +x <- retailsa$DrinkingPlaces +coef <- lp_filter(6) +confit <- confint_filter(x, coef) +plot(confit, plot.type = "single", + col = c("red", "black", "black"), + lty = c(1, 2, 2)) +} +\references{ +Loader, Clive. 1999. +Local regression and likelihood. +New York: Springer-Verlag. +} diff --git a/man/cross_validation.Rd b/man/cross_validation.Rd deleted file mode 100644 index 9a081de..0000000 --- a/man/cross_validation.Rd +++ /dev/null @@ -1,36 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/crossvalidation.R -\name{cross_validation} -\alias{cross_validation} -\title{Cross-Validation} -\usage{ -cross_validation(x, coef, ...) -} -\arguments{ -\item{x}{input time series.} - -\item{coef}{vector of coefficients or a moving-average (\code{\link[=moving_average]{moving_average()}}).} - -\item{...}{other arguments passed to the function \code{\link[=moving_average]{moving_average()}} to convert \code{coef} to a \code{"moving_average"} object.} -} -\description{ -Computes the cross-validation statistic of a moving-average on a time series -} -\details{ -The cross-validation statistics of a moving average \eqn{(\theta_i)_{-p\leq i \leq p}} on a time series \eqn{(y_i)_{1\leq i \leq n}} is: -\deqn{ -\frac{1}{n-(p+q)} -\sum_{t=p+1}^{n-q} -\left( -\frac{y_t - \sum_{i=-p}^q \theta_i y_{t+i}}{ -1-\theta_0 -} -\right)^2. -} -The function \code{cross_validation()} returns the time series \eqn{(x_t)}) with -\deqn{ -x_t = \frac{y_t - \sum_{i=-p}^q \theta_i y_{t+i}}{ -1-\theta_0 -}. -} -} diff --git a/man/deprecated-rjd3filters.Rd b/man/deprecated-rjd3filters.Rd new file mode 100644 index 0000000..416bd5f --- /dev/null +++ b/man/deprecated-rjd3filters.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/crossvalidation.R +\name{deprecated-rjd3filters} +\alias{deprecated-rjd3filters} +\alias{cross_validation} +\title{Deprecated function} +\usage{ +cross_validation(x, coef, ...) +} +\arguments{ +\item{x}{input time series.} + +\item{coef}{vector of coefficients or a moving-average (\code{\link[=moving_average]{moving_average()}}).} + +\item{...}{other arguments passed to the function \code{\link[=moving_average]{moving_average()}} to convert \code{coef} to a \code{"moving_average"} object.} +} +\description{ +Deprecated function +} diff --git a/man/diagnostics-fit.Rd b/man/diagnostics-fit.Rd new file mode 100644 index 0000000..eccfbe6 --- /dev/null +++ b/man/diagnostics-fit.Rd @@ -0,0 +1,99 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/crossvalidation.R +\name{diagnostics-fit} +\alias{diagnostics-fit} +\alias{cve} +\alias{cv} +\alias{loocve} +\alias{rt} +\alias{cp} +\title{Diagnostics and goodness of fit of filtered series} +\usage{ +cve(x, coef, ...) + +cv(x, coef, ...) + +loocve(x, coef, ...) + +rt(x, coef, ...) + +cp(x, coef, var, ...) +} +\arguments{ +\item{x}{input time series.} + +\item{coef}{vector of coefficients or a moving-average (\code{\link[=moving_average]{moving_average()}}).} + +\item{...}{other arguments passed to the function \code{\link[=moving_average]{moving_average()}} to convert \code{coef} to a \code{"moving_average"} object.} + +\item{var}{variance used to compute the CP statistic (\code{cp()}).} +} +\description{ +Set of functions to compute diagnostics and goodness of fit of filtered series: +cross validation (\code{cv()}) and cross validate estimate (\code{cve()}), +leave-one-out cross validation estimate (\code{loocve}), +CP statistic (\code{cp()}) and Rice's T statistics (\code{rt()}). +} +\details{ +Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +Let denote \eqn{\hat{\mu}_t} the filtered series computed at time \eqn{t} as: +\deqn{ +\hat{\mu}_t = \sum_{i=-p}^q \theta_i y_{t+i}. +} + +The cross validation estimate (\code{cve()}) is defined as the time series \eqn{Y_t-\hat{\mu}_{-t}} where +\eqn{\hat{\mu}_{-t}} is the leave-one-out cross validation estimate (\code{loocve()}) defined as the filtered series +computed deleting the observation \eqn{t} and remaining all the other points. +The cross validation statistics (\code{cv()}) is defined as: +\deqn{ +CV=\frac{1}{n-(p+q)} +\sum_{t=p+1}^{n-q} \left(y_t - \hat{\mu}_{-t}\right)^2. +} +In the case of filtering with a moving average, we can show that: +\deqn{ +\hat{\mu}_{-t}= \frac{\hat{\mu}_t - \theta_0 y_t}{1-\theta_0} +} +and +\deqn{ +CV=\frac{1}{n-(p+q)} +\sum_{t=p+1}^{n-q} \left(\frac{y_t - \hat{\mu}_{t}}{1-\theta_0}\right)^2. +} + +In the case of filtering with a moving average, +the CP estimate of risk (introduced by Mallows (1973); \code{cp()}) can be defined as: +\deqn{ +CP=\frac{1}{\sigma^2} +\sum_{t=p+1}^{n-q} \left(y_t - \hat{\mu}_{t}\right)^2 +-(n-(p+q))(1-2\theta_0). +} +The CP method requires an estimate of \eqn{\sigma^2} (\code{var} parameter). +The usual use of CP is to compare several different fits (for example different bandwidths): +one should use the same estimate of \eqn{\hat{\sigma}^2} for all fits (using for example \code{\link[=var_estimator]{var_estimator()}}). +The recommendation of Cleveland and Devlin (1988) is to compute \eqn{\hat{\sigma}^2} +from a fit at the smallest bandwidth under consideration, +at which one should be willing to assume that bias is negligible. + +The Rice's T statistic (\code{rt()}) is defined as: +\deqn{ +\frac{1}{n-(p+q)} +\sum_{t=p+1}^{n-q} +\frac{ + \left(y_t - \hat{\mu}_{t}\right)^2 +}{ + 1-2\theta_0 +} +} +} +\references{ +Loader, Clive. 1999. +Local regression and likelihood. +New York: Springer-Verlag. + +Mallows, C. L. (1973). Some comments on Cp. +Technometrics 15, 661– 675. + +Cleveland, W. S. and S. J. Devlin (1988). +Locally weighted regression: An approach to regression analysis by local fitting. +Journal of the American Statistical Association 83, 596–610. +} diff --git a/man/filters_operations.Rd b/man/filters_operations.Rd index 58cb284..e141604 100644 --- a/man/filters_operations.Rd +++ b/man/filters_operations.Rd @@ -136,6 +136,9 @@ \item{x, e1, e2}{object} \item{i, j, value}{indices specifying elements to extract or replace and the new value} + +\item{zero_as_na}{boolean indicating if, when merging several moving averages (\code{cbind}) +if trealing and leading zeros added to have a matrix form should be replaced by \code{NA}.} } \description{ Manipulation of \code{\link[=moving_average]{moving_average()}} or \code{\link[=finite_filters]{finite_filters()}} objects diff --git a/man/finite_filters.Rd b/man/finite_filters.Rd index 80b71a4..5c67d63 100644 --- a/man/finite_filters.Rd +++ b/man/finite_filters.Rd @@ -2,6 +2,7 @@ % Please edit documentation in R/2_finite_filters.R, R/print.R \name{finite_filters} \alias{finite_filters} +\alias{is.finite_filters} \alias{show,finite_filters-method} \title{Manipulating Finite Filters} \usage{ @@ -12,6 +13,8 @@ finite_filters( first_to_last = FALSE ) +is.finite_filters(x) + \S4method{show}{finite_filters}(object) } \arguments{ @@ -25,6 +28,8 @@ a \code{matrix} or \code{list} with all the coefficients.} \item{first_to_last}{boolean indicating if the first element of \code{rfilters} is the first asymmetric filter (when only one observation is missing) or the last one (real-time estimates).} +\item{x}{object to test the class.} + \item{object}{\code{finite_filters} object.} } \description{ diff --git a/man/var_estimator.Rd b/man/var_estimator.Rd index 0d4c69e..5e4d107 100644 --- a/man/var_estimator.Rd +++ b/man/var_estimator.Rd @@ -17,7 +17,17 @@ var_estimator(x, coef, ...) Variance Estimator } \details{ +Let \eqn{(\theta_i)_{-p\leq i \leq q}} be a moving average of length \eqn{p+q+1} used +to filter a time series \eqn{(y_i)_{1\leq i \leq n}}. +It is equivalent to a local regression and the associated error variance \eqn{\sigma^2} +can be estimated using the normalized residual sum of squares, which can be simplified as: \deqn{ -\hat\sigma^2=\frac{1}{n-2h}\sum_{t=h+1}^{n-h}\frac{(y_t-\hat \mu_t)^2}{1-2w_0^2+\sum w_i^2} +\hat\sigma^2=\frac{1}{n-(p+q)}\sum_{t=p+1}^{n-q} +\frac{(y_t-\hat \mu_t)^2}{1-2w_0^2+\sum_{i=-p}^q w_i^2} } } +\references{ +Loader, Clive. 1999. +Local regression and likelihood. +New York: Springer-Verlag. +} diff --git a/rjd3filters.Rproj b/rjd3filters.Rproj index 49a8d8d..386570e 100644 --- a/rjd3filters.Rproj +++ b/rjd3filters.Rproj @@ -6,7 +6,7 @@ AlwaysSaveHistory: No EnableCodeIndexing: Yes UseSpacesForTab: Yes -NumSpacesForTab: 4 +NumSpacesForTab: 2 Encoding: UTF-8 RnwWeave: knitr From 4d0edbd2212772d4e03395438c900a7f4c651c7a Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 15:58:17 +0200 Subject: [PATCH 04/27] doc correction --- R/RKHS.R | 7 +------ R/lp_filters.R | 13 +++---------- man/localpolynomials.Rd | 2 +- man/lp_filter.Rd | 11 ++--------- man/rkhs_filter.Rd | 7 +------ 5 files changed, 8 insertions(+), 32 deletions(-) diff --git a/R/RKHS.R b/R/RKHS.R index f7910b0..91e531e 100644 --- a/R/RKHS.R +++ b/R/RKHS.R @@ -13,12 +13,7 @@ #' @examples #' rkhs <- rkhs_filter(horizon = 6, asymmetricCriterion = "Timeliness") #' plot_coef(rkhs) -#' @return An object of class \code{"rkhs_filter"}, which is a list of 4 elements:\itemize{ -#' \item{\code{"internal"}}{Java object used for internal functions} -#' \item{\code{"filters.coef"}}{The coefficients of the selected filter} -#' \item{\code{"filters.gain"}}{The gain function between 0 and pi (601 observations)} -#' \item{\code{"filters.phase"}}{The phase function between 0 and pi (601 observations)} -#' } +#' @return a [finite_filters()] object. #' @export rkhs_filter <- function(horizon = 6, degree = 2, kernel = c("BiWeight", "Henderson", "Epanechnikov", "Triangular", "Uniform", "TriWeight"), diff --git a/R/lp_filters.R b/R/lp_filters.R index 8f82eec..60f63c2 100644 --- a/R/lp_filters.R +++ b/R/lp_filters.R @@ -19,7 +19,7 @@ NULL #' plot(x) #' lines(trend, col = "red") #' @references Proietti, Tommaso and Alessandra Luati (2008). “Real time estimation in local polynomial regression, with application to trend-cycle analysis”. -#' @seealso \code{\link{lp_filter}} +#' @seealso [lp_filter()]. #' @export localpolynomials<-function(x, horizon = 6, @@ -66,19 +66,12 @@ localpolynomials<-function(x, #' * "DAF": Direct Asymmetric filter #' * "CN": Cut and Normalized Filter #' -#' @return \code{list} with coefficients, gain and phase values -#' +#' @return a [finite_filters()] object. +#' @seealso [localpolynomials()]. #' @examples #' henderson_f <- lp_filter(horizon = 6, kernel = "Henderson") #' plot_coef(henderson_f) #' @references Proietti, Tommaso and Alessandra Luati (2008). “Real time estimation in local polynomial regression, with application to trend-cycle analysis”. -#' @seealso \code{\link{lp_filter}} -#' @return An object of class \code{"rkhs_filter"}, which is a list of 4 elements:\itemize{ -#' \item{\code{"internal"}}{Java object used for internal functions} -#' \item{\code{"filters.coef"}}{The coefficients of the selected filter} -#' \item{\code{"filters.gain"}}{The gain function between 0 and pi (601 observations)} -#' \item{\code{"filters.phase"}}{The phase function between 0 and pi (601 observations)} -#' } #' @export lp_filter <- function(horizon = 6, degree = 3, kernel = c("Henderson", "Uniform", "Biweight", "Trapezoidal", "Triweight", "Tricube", "Gaussian", "Triangular", "Parabolic"), diff --git a/man/localpolynomials.Rd b/man/localpolynomials.Rd index c8da1f7..b4611f2 100644 --- a/man/localpolynomials.Rd +++ b/man/localpolynomials.Rd @@ -49,5 +49,5 @@ lines(trend, col = "red") Proietti, Tommaso and Alessandra Luati (2008). “Real time estimation in local polynomial regression, with application to trend-cycle analysis”. } \seealso{ -\code{\link{lp_filter}} +\code{\link[=lp_filter]{lp_filter()}}. } diff --git a/man/lp_filter.Rd b/man/lp_filter.Rd index 19bd629..50ac419 100644 --- a/man/lp_filter.Rd +++ b/man/lp_filter.Rd @@ -31,14 +31,7 @@ lp_filter( \item{passband}{passband threshold.} } \value{ -\code{list} with coefficients, gain and phase values - -An object of class \code{"rkhs_filter"}, which is a list of 4 elements:\itemize{ -\item{\code{"internal"}}{Java object used for internal functions} -\item{\code{"filters.coef"}}{The coefficients of the selected filter} -\item{\code{"filters.gain"}}{The gain function between 0 and pi (601 observations)} -\item{\code{"filters.phase"}}{The phase function between 0 and pi (601 observations)} -} +a \code{\link[=finite_filters]{finite_filters()}} object. } \description{ Local Polynomials Filters @@ -61,5 +54,5 @@ plot_coef(henderson_f) Proietti, Tommaso and Alessandra Luati (2008). “Real time estimation in local polynomial regression, with application to trend-cycle analysis”. } \seealso{ -\code{\link{lp_filter}} +\code{\link[=localpolynomials]{localpolynomials()}}. } diff --git a/man/rkhs_filter.Rd b/man/rkhs_filter.Rd index 4b2b63e..84148fb 100644 --- a/man/rkhs_filter.Rd +++ b/man/rkhs_filter.Rd @@ -41,12 +41,7 @@ If \code{optimalbw = FALSE} then the bandwith specified in \code{bandwidth} will \item{bandwidth}{the bandwidth to use if \code{optimalbw = FALSE}.} } \value{ -An object of class \code{"rkhs_filter"}, which is a list of 4 elements:\itemize{ -\item{\code{"internal"}}{Java object used for internal functions} -\item{\code{"filters.coef"}}{The coefficients of the selected filter} -\item{\code{"filters.gain"}}{The gain function between 0 and pi (601 observations)} -\item{\code{"filters.phase"}}{The phase function between 0 and pi (601 observations)} -} +a \code{\link[=finite_filters]{finite_filters()}} object. } \description{ Estimation of a filter using Reproducing Kernel Hilbert Space (RKHS) From 36fb138903bd5ac6b600e51dde7f8ee80f3e908f Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 18:53:51 +0200 Subject: [PATCH 05/27] update maintainer --- DESCRIPTION | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 4bafc33..3688ed5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,12 +3,12 @@ Type: Package Title: Trend-Cycle Extraction with Linear Filters Version: 2.1.1 Authors@R: c( - person("Jean", "Palate", role = c("aut", "cre"), - email = "jean.palate@nbb.be"), - person("Alain", "Quartier-la-Tente", role = c("aut"), - email = "alain.quartier@yahoo.fr", - comment = c(ORCID = "0000-0001-7890-3857")) - ) + person("Jean", "Palate", role = c("aut"), + email = "jean.palate@nbb.be"), + person("Alain", "Quartier-la-Tente", role = c("aut", "cre"), + email = "alain.quartier@yahoo.fr", + comment = c(ORCID = "0000-0001-7890-3857")) + ) Description: This package provides functions to build and apply symmetric and asymmetric moving averages (= linear filters) for trend-cycle extraction. In particular, it implements several moderns approaches for real-time estimates From d53463413a12b5a484b68ac9c8e58d87512587ca Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 18:58:59 +0200 Subject: [PATCH 06/27] example correction --- R/crossvalidation.R | 4 ++-- man/confint_filter.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/crossvalidation.R b/R/crossvalidation.R index 24149ee..978c8e5 100644 --- a/R/crossvalidation.R +++ b/R/crossvalidation.R @@ -191,8 +191,8 @@ var_estimator <- function(x, coef, ...) { #' @examples #' x <- retailsa$DrinkingPlaces #' coef <- lp_filter(6) -#' confit <- confint_filter(x, coef) -#' plot(confit, plot.type = "single", +#' confint <- confint_filter(x, coef) +#' plot(confint, plot.type = "single", #' col = c("red", "black", "black"), #' lty = c(1, 2, 2)) #' @export diff --git a/man/confint_filter.Rd b/man/confint_filter.Rd index d0629c8..c824e2a 100644 --- a/man/confint_filter.Rd +++ b/man/confint_filter.Rd @@ -45,8 +45,8 @@ computed using the corresponding asymmetric filters \examples{ x <- retailsa$DrinkingPlaces coef <- lp_filter(6) -confit <- confint_filter(x, coef) -plot(confit, plot.type = "single", +confint <- confint_filter(x, coef) +plot(confint, plot.type = "single", col = c("red", "black", "black"), lty = c(1, 2, 2)) } From 6cf1a39cb63dd66041b2044f5e3c88b6f4406e7b Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 18:59:10 +0200 Subject: [PATCH 07/27] README update --- README.Rmd | 18 +++++++++++++++--- README.md | 22 ++++++++++++++++++++-- man/figures/README-confint-plot-1.png | Bin 0 -> 43386 bytes man/figures/README-diagnostic-plots-1.png | Bin 9255 -> 61570 bytes man/figures/README-mm-plots-1.png | Bin 7925 -> 47732 bytes man/figures/README-plot-forecast-1.png | Bin 8069 -> 46828 bytes man/figures/README-plot-global-1.png | Bin 6934 -> 40300 bytes man/figures/README-plot-q0-1.png | Bin 8425 -> 53149 bytes 8 files changed, 35 insertions(+), 5 deletions(-) create mode 100644 man/figures/README-confint-plot-1.png diff --git a/README.Rmd b/README.Rmd index a194f93..d31c419 100644 --- a/README.Rmd +++ b/README.Rmd @@ -29,9 +29,8 @@ Some quality criteria defined by Wildi and McElroy (2019) can also be computed. ## Installation -rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#installing-the-software) - - +rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. +If you have troubles with the installation, check the [installation manual of `RJDemetra`](https://github.com/jdemetra/rjdemetra/wiki/Installation-manual). To get the current stable version (from the latest release): @@ -166,6 +165,19 @@ par(def.par) ```{r, diagnostic-plots, eval = TRUE, echo=FALSE} ``` +Confidence intervals can also be computed with the `confint_filter` function: + +```{r, confint-plot, eval = TRUE} +confint <- confint_filter(y, musgrave) + +plot(confint, plot.type = "single", + col = c("red", "black", "black"), + lty = c(1, 2, 2), xlab = NULL, ylab = NULL) +lines(y, col = "grey") +legend("topleft", legend = c("y", "Smoothed", "CI (95%)"), + col= c("grey", "red", "black"), lty = c(1, 1, 2)) +``` + ### Manipulate moving averages You can also create and manipulate moving averages with the class `moving_average`. diff --git a/README.md b/README.md index 4112d99..e2f8643 100644 --- a/README.md +++ b/README.md @@ -22,8 +22,10 @@ computed. ## Installation rjd3filters relies on the -[rJava](https://CRAN.R-project.org/package=rJava) package and Java SE 17 -or later version is required. +[rJava](https://CRAN.R-project.org/package=rJava) package and running +rjd3 packages requires Java 17 or higher. If you have troubles with the +installation, check the [installation manual of +`RJDemetra`](https://github.com/jdemetra/rjdemetra/wiki/Installation-manual). To get the current stable version (from the latest release): @@ -185,6 +187,22 @@ par(def.par) +Confidence intervals can also be computed with the `confint_filter` +function: + +``` r +confint <- confint_filter(y, musgrave) + +plot(confint, plot.type = "single", + col = c("red", "black", "black"), + lty = c(1, 2, 2), xlab = NULL, ylab = NULL) +lines(y, col = "grey") +legend("topleft", legend = c("y", "Smoothed", "CI (95%)"), + col= c("grey", "red", "black"), lty = c(1, 1, 2)) +``` + + + ### Manipulate moving averages You can also create and manipulate moving averages with the class diff --git a/man/figures/README-confint-plot-1.png b/man/figures/README-confint-plot-1.png new file mode 100644 index 0000000000000000000000000000000000000000..cf0878b3c28fbf3e9e065cde9f5c29ef470e8e48 GIT binary patch literal 43386 zcmeEu^;eYb_V+M=gh+~XilBg`bV`UQB}hnjcXvoCr2+!dBHhwGG?Jop4h_=XGw;QD z)bpJG;Qisd7A{$W%*}o8Ywu6(d84Eta|@ds8v=pcl6@+r41u76Pf_5QH^5(nn%MUs 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zbv+WiSb{0NiiU(d(Fpth@5nFXW8-au&1D9<&)@I}TTu|6HYHs|4S}`;jEx|H$Wa0f z%yL* zi4FAs2lD>;pF;(!#j}sxD!4I`2n{;It%xP_VDZokSwQ4MK?pQJ+G{L`LLfNDfzFKd xL4?t+_hT%7fCo7>>c5Xx{a>(S1(xPK%ttSzuK+Ze9z1H4zn>ENzW@}4{$T(B From b1b2af2202b6a7312e5dc3bb73f8e34a63f95489 Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 1 May 2024 19:05:51 +0200 Subject: [PATCH 08/27] NEWS update --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 01dea13..88b39d9 100644 --- a/NEWS.md +++ b/NEWS.md @@ -11,7 +11,7 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). * `cross_validation()` function renamed to `cve()`, `cross_validation()` is now deprecated. -### News +### Added * New functions to compute functions to compute diagnostics and goodness of fit of filtered series: cross validation (`cv()`) and cross validate estimate (`cve()`), leave-one-out cross validation estimate (`loocve`), CP statistic (`cp()`) and Rice's T statistics (`rt()`). From 4c79e7872cccd248d66563ef4b35b20daf60fb8c Mon Sep 17 00:00:00 2001 From: Anna SMYK Date: Sun, 5 May 2024 13:51:09 +0200 Subject: [PATCH 09/27] descp file and readme update --- DESCRIPTION | 28 ++++++++++++---------- README.Rmd | 15 ++++++------ README.md | 19 ++++++++------- man/figures/README-confint-plot-1.png | Bin 43386 -> 6957 bytes man/figures/README-diagnostic-plots-1.png | Bin 61570 -> 9255 bytes man/figures/README-mm-plots-1.png | Bin 47732 -> 7925 bytes man/figures/README-plot-forecast-1.png | Bin 46828 -> 8069 bytes man/figures/README-plot-global-1.png | Bin 40300 -> 6934 bytes man/figures/README-plot-q0-1.png | Bin 53149 -> 8425 bytes 9 files changed, 33 insertions(+), 29 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3688ed5..465c735 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,26 +1,28 @@ Package: rjd3filters Type: Package -Title: Trend-Cycle Extraction with Linear Filters +Title: Trend-Cycle Extraction with Linear Filters based on JDemetra+ v3.x Version: 2.1.1 Authors@R: c( person("Jean", "Palate", role = c("aut"), - email = "jean.palate@nbb.be"), - person("Alain", "Quartier-la-Tente", role = c("aut", "cre"), - email = "alain.quartier@yahoo.fr", - comment = c(ORCID = "0000-0001-7890-3857")) - ) + email = "palatejean@gmail.com"), + person("Alain", "Quartier-la-Tente", role = c("aut"), + email = "alain.quartier@yahoo.fr", + comment = c(ORCID = "0000-0001-7890-3857")), + person("Tanguy", "Barthelemy", role = c("aut","cre"), + email ="tanguy.barthelemy@insee.fr"), + person("Anna", "Smyk", role = c("aut"), + email ="anna.smyk@insee.fr") + ) Description: This package provides functions to build and apply symmetric and asymmetric moving averages (= linear filters) for trend-cycle extraction. - In particular, it implements several moderns approaches for real-time estimates + In particular, it implements several modern approaches for real-time estimates from the viewpoint of revisions and time delay in detecting turning points. It includes the local polynomial approach of Proietti and Luati (2008), the Reproducing Kernel Hilbert Space (RKHS) of Dagum and Bianconcini (2008) and the Fidelity-Smoothness-Timeliness approach of Grun-Rehomme, Guggemos, and Ladiray (2018). - It is based on Java libraries developped around 'JDemetra+' (), - the seasonal adjustment software officially recommended to the members - of the European Statistical System (ESS) and the European System of Central Banks. + It is based on Java libraries developped in 'JDemetra+' (), time series analysis software. Depends: - R (>= 3.6.0) + R (>= 4.1.0) Imports: rJava (>= 1.0-6), methods, @@ -29,11 +31,11 @@ Imports: stats, rjd3toolkit (>= 3.2.2) Remotes: - github::rjdemetra/rjd3toolkit + github::rjdverse/rjd3toolkit SystemRequirements: Java (>= 17) License: EUPL LazyData: TRUE -URL: https://github.com/rjdemetra/rjd3filters, https://rjdemetra.github.io/rjd3filters/ +URL: https://github.com/rjdverse/rjd3filters, https://rjdverse.github.io/rjd3filters/ Suggests: knitr, rmarkdown diff --git a/README.Rmd b/README.Rmd index d31c419..00cb68b 100644 --- a/README.Rmd +++ b/README.Rmd @@ -16,7 +16,7 @@ knitr::opts_chunk$set( # rjd3filters -rjd3filters is R package on linear filters for real-time trend-cycle estimates. +rjd3filters is an R package on linear filters for real-time trend-cycle estimates. It allows to create symmetric and asymmetric moving averages with: - local polynomial filters, as defined by Proietti and Luati (2008); @@ -29,15 +29,16 @@ Some quality criteria defined by Wildi and McElroy (2019) can also be computed. ## Installation -rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. -If you have troubles with the installation, check the [installation manual of `RJDemetra`](https://github.com/jdemetra/rjdemetra/wiki/Installation-manual). +rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package. + +Running rjd3 packages requires **Java 17 or higher**. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#Rconfig) To get the current stable version (from the latest release): ```{r, eval = FALSE} # install.packages("remotes") -remotes::install_github("rjdemetra/rjd3toolkit@*release") -remotes::install_github("rjdemetra/rjd3filters@*release") +remotes::install_github("rjdverse/rjd3toolkit@*release") +remotes::install_github("rjdverse/rjd3filters@*release") ``` To get the current development version from GitHub: @@ -45,8 +46,8 @@ To get the current development version from GitHub: ```{r, eval = FALSE} # install.packages("remotes") # Install development version from GitHub -remotes::install_github("rjdemetra/rjd3toolkit") -remotes::install_github("rjdemetra/rjd3filters") +remotes::install_github("rjdverse/rjd3toolkit") +remotes::install_github("rjdverse/rjd3filters") ``` diff --git a/README.md b/README.md index e2f8643..b991fa7 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ # rjd3filters -rjd3filters is R package on linear filters for real-time trend-cycle +rjd3filters is an R package on linear filters for real-time trend-cycle estimates. It allows to create symmetric and asymmetric moving averages with: @@ -22,17 +22,18 @@ computed. ## Installation rjd3filters relies on the -[rJava](https://CRAN.R-project.org/package=rJava) package and running -rjd3 packages requires Java 17 or higher. If you have troubles with the -installation, check the [installation manual of -`RJDemetra`](https://github.com/jdemetra/rjdemetra/wiki/Installation-manual). +[rJava](https://CRAN.R-project.org/package=rJava) package. + +Running rjd3 packages requires **Java 17 or higher**. How to set up such +a configuration in R is explained +[here](https://jdemetra-new-documentation.netlify.app/#Rconfig) To get the current stable version (from the latest release): ``` r # install.packages("remotes") -remotes::install_github("rjdemetra/rjd3toolkit@*release") -remotes::install_github("rjdemetra/rjd3filters@*release") +remotes::install_github("rjdverse/rjd3toolkit@*release") +remotes::install_github("rjdverse/rjd3filters@*release") ``` To get the current development version from GitHub: @@ -40,8 +41,8 @@ To get the current development version from GitHub: ``` r # install.packages("remotes") # Install development version from GitHub -remotes::install_github("rjdemetra/rjd3toolkit") -remotes::install_github("rjdemetra/rjd3filters") +remotes::install_github("rjdverse/rjd3toolkit") +remotes::install_github("rjdverse/rjd3filters") ``` ## Basic example diff --git a/man/figures/README-confint-plot-1.png b/man/figures/README-confint-plot-1.png index 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z`3oFEE-qNmhbqjU{Jo%?fHB}F<@(?36@-!pd^LAkyX Date: Mon, 6 May 2024 11:10:18 +0200 Subject: [PATCH 10/27] update readme with new link for EUPL --- README.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.Rmd b/README.Rmd index a194f93..f3a675d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -29,7 +29,7 @@ Some quality criteria defined by Wildi and McElroy (2019) can also be computed. ## Installation -rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#installing-the-software) +rjd3filters relies on the [rJava](https://CRAN.R-project.org/package=rJava) package and running rjd3 packages requires Java 17 or higher. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#installing-the-software). @@ -231,4 +231,4 @@ pull requests should include **updated tests** and **updated documentation**. If ## 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). +The code of this project is licensed under the [European Union Public Licence (EUPL)](https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12). From 1c0e70d4c1f38034878a70fbd2bd97552a6df057 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Mon, 6 May 2024 11:10:51 +0200 Subject: [PATCH 11/27] lint space if ( --- R/2_finite_filters.R | 2 +- R/filter.R | 2 +- R/fst_filters.R | 2 +- R/get_properties_function.R | 2 +- R/kernels.R | 4 ++-- R/lp_filters.R | 4 ++-- R/plots.R | 30 +++++++++++++++--------------- R/zzz.R | 2 +- 8 files changed, 24 insertions(+), 24 deletions(-) diff --git a/R/2_finite_filters.R b/R/2_finite_filters.R index e8e2134..9fe6b1c 100644 --- a/R/2_finite_filters.R +++ b/R/2_finite_filters.R @@ -100,7 +100,7 @@ finite_filters.matrix <- function(sfilter, rfilters <- rev(rfilters) } } else { - if(first_to_last) { + if (first_to_last) { lfilters <- rev(lfilters) rfilters <- rev(rfilters) } diff --git a/R/filter.R b/R/filter.R index a5ae053..dfcb2ca 100644 --- a/R/filter.R +++ b/R/filter.R @@ -122,7 +122,7 @@ ff_ma <- function(x, coefs, remove_missing = TRUE) { result <- c(rep(NA, data_clean$leading), result, rep(NA, data_clean$trailing)) } - if(is.ts(x)) + if (is.ts(x)) result <- ts(result,start = start(x), frequency = frequency(x)) result } diff --git a/R/fst_filters.R b/R/fst_filters.R index 8322c7e..a9f85d8 100644 --- a/R/fst_filters.R +++ b/R/fst_filters.R @@ -176,7 +176,7 @@ mse.default<-function(aweights, sweights, density=c("uniform", "rw"), passband = } else { sweights <- coef(sweights) } - } else if(length(sweights)>length(aweights)){ + } else if (length(sweights)>length(aweights)){ # we asume sweights were specify from [-n to n] instead of [0,n] n <- (length(sweights)-1)/2 sweights <- sweights[-seq_len(n)] diff --git a/R/get_properties_function.R b/R/get_properties_function.R index 0a96de5..0d949c4 100644 --- a/R/get_properties_function.R +++ b/R/get_properties_function.R @@ -131,7 +131,7 @@ diagnostic_matrix <- function(x, lags, passband = pi/6, results <- c(sum(x)-1, sum(coef(x) * seq(lower_bound(x), upper_bound(x), by = 1)), sum(coef(x) * seq(lower_bound(x), upper_bound(x), by = 1)^2), fst(x, lags, passband = passband)) - if(!missing(sweights)){ + if (!missing(sweights)){ results <- c(results, mse(x, sweights, diff --git a/R/kernels.R b/R/kernels.R index 2a4e7ce..0580420 100644 --- a/R/kernels.R +++ b/R/kernels.R @@ -21,10 +21,10 @@ get_kernel <- function(kernel = c("Henderson","Uniform", "Triangular", choices = c("henderson", "uniform", "triangular", "epanechnikov", "parabolic", "biweight", "triweight", "tricube", "trapezoidal", "gaussian" )) - if(kernel == "parabolic") + if (kernel == "parabolic") kernel <- "epanechnikov" h <- as.integer(horizon) - if(kernel == "gaussian"){ + if (kernel == "gaussian"){ jkernel <- .jcall("jdplus/toolkit/base/core/data/analysis/DiscreteKernel", "Ljava/util/function/IntToDoubleFunction;", tolower(kernel), h, sd_gauss) diff --git a/R/lp_filters.R b/R/lp_filters.R index 8f82eec..57c93cd 100644 --- a/R/lp_filters.R +++ b/R/lp_filters.R @@ -28,7 +28,7 @@ localpolynomials<-function(x, endpoints = c("LC", "QL", "CQ", "CC", "DAF"), ic = 4.5, tweight = 0, passband = pi/12){ - if(2*horizon < degree) + if (2*horizon < degree) stop("You need more observation (2 * horizon + 1) than variables (degree + 1) to estimate the filter.") d <- 2 / (sqrt(pi) * ic) @@ -85,7 +85,7 @@ lp_filter <- function(horizon = 6, degree = 3, endpoints = c("LC", "QL", "CQ", "CC", "DAF", "CN"), ic = 4.5, tweight = 0, passband = pi/12){ - if(2*horizon < degree) + if (2*horizon < degree) stop("You need more observation (2 * horizon + 1) than variables (degree + 1) to estimate the filter.") d <- 2 / (sqrt(pi) * ic) diff --git a/R/plots.R b/R/plots.R index aa01988..9b94497 100644 --- a/R/plots.R +++ b/R/plots.R @@ -31,13 +31,13 @@ plot_coef <- function(x, nxlab = 7, add = FALSE, ...){ plot_coef.default <- function(x, nxlab = 7, add = FALSE, zero_as_na = TRUE, q = 0, legend = FALSE, legend.pos = "topright", ...){ - if(zero_as_na) + if (zero_as_na) x <- apply(x,2, trailingzero_as_na) col_to_plot <- sprintf("q=%i",q) col_to_plot <- col_to_plot[col_to_plot %in% colnames(x)] horizon <- (nrow(x)-1)/2 if (length(col_to_plot) == 0) { - if(!add){ + if (!add){ plot(1, type="n",xaxt = "n", xlab = "", ylab = "coefficient", xlim=c(-horizon, horizon), ylim=c(0, 1), ...) @@ -48,10 +48,10 @@ plot_coef.default <- function(x, nxlab = 7, add = FALSE, matplot(seq(-horizon, horizon, by = 1),x[,col_to_plot], xaxt = "n", xlab = "", type = "o", pch = 20, ylab = "coefficient", add = add, ...) - if(legend) + if (legend) legend(legend.pos,col_to_plot, col = seq_along(col_to_plot), lty=seq_along(col_to_plot), lwd=2) - if(!add) + if (!add) axis(1, at=seq(-horizon, horizon, by = 1), labels = rownames(x)) } @@ -62,7 +62,7 @@ plot_coef.moving_average <- function(x, nxlab = 7, add = FALSE, ...){ matplot(seq(lower_bound(x), upper_bound(x), by = 1), x_plot, xaxt = "n", xlab = "", type = "o", pch = 20, ylab = "coefficient", add = add, ...) - if(!add) + if (!add) axis(1, at=seq(lower_bound(x), upper_bound(x), by = 1), labels = names(x_plot)) } @@ -91,7 +91,7 @@ plot_gain.moving_average<- function(x, nxlab = 7, add = FALSE, plot(g, type = "l", xaxt = "n", xlab = "", ylab = "gain", add = add, xlim = xlim, ...) - if(!add){ + if (!add){ x_lab_at <- seq(xlim[1]/pi, xlim[2]/pi, length.out = nxlab) axis(1, at = x_lab_at * pi, labels = xlabel(x_lab_at)) } @@ -112,7 +112,7 @@ plot_gain.finite_filters <- function(x, nxlab = 7, add = FALSE, all_g_f <- all_g_f[col_to_plot] y_val <- sapply(all_g_f, function(f) f(x_values)) if (length(col_to_plot) == 0) { - if(!add){ + if (!add){ plot(1, type="n",xaxt = "n", xlab = "", ylab = "gain", xlim=xlim, ylim=c(0, 1), ...) @@ -157,7 +157,7 @@ plot_phase.moving_average<- function(x, nxlab = 7, add = FALSE, plot(p_plot, type = "l", xaxt = "n", xlab = "", ylab = "phase", add = add, xlim = xlim, ...) - if(!add){ + if (!add){ x_lab_at <- seq(xlim[1]/pi, xlim[2]/pi, length.out = nxlab) axis(1, at = x_lab_at * pi, labels = xlabel(x_lab_at)) } @@ -180,11 +180,11 @@ plot_phase.finite_filters <- function(x, nxlab = 7, add = FALSE, all_p_f <- all_p_f[col_to_plot] y_val <- sapply(all_p_f, function(f) f(x_values)) - if(normalized){ + if (normalized){ y_val[-1,] <- y_val[-1,] / x_values[-1] } if (length(col_to_plot) == 0) { - if(!add){ + if (!add){ plot(1, type="n",xaxt = "n", xlab = "", ylab = "phase", xlim=xlim, ylim=c(0, 1), ...) @@ -222,7 +222,7 @@ trailingzero_as_na <- function(x){ i <- i - 1 } x - # if(x[length(x)]==0) + # if (x[length(x)]==0) # x [seq(from = tail(which(!sapply(x, function(y) isTRUE(all.equal(y,0)))),1)+1, # to = length(x), # by = 1)] <- NA @@ -237,7 +237,7 @@ rm_leading_zero_or_na <- function(x){ remove_i <- c(i, remove_i) i <- i + 1 } - if(is.null(remove_i)){ + if (is.null(remove_i)){ x } else{ x[-remove_i] @@ -252,7 +252,7 @@ rm_trailing_zero_or_na <- function(x){ remove_i <- c(i, remove_i) i <- i - 1 } - if(is.null(remove_i)){ + if (is.null(remove_i)){ x } else{ x[-remove_i] @@ -267,7 +267,7 @@ rm_trailing_zero <- function(x){ remove_i <- c(i, remove_i) i <- i - 1 } - if(is.null(remove_i)){ + if (is.null(remove_i)){ x } else{ x[-remove_i] @@ -288,7 +288,7 @@ remove_bound_NA <- function(x) { j <- j + 1 } - if(is.null(remove_i_first) & is.null(remove_i_last)){ + if (is.null(remove_i_first) & is.null(remove_i_last)){ # list(data = x, leading = 0, # trailing = 0) } else{ diff --git a/R/zzz.R b/R/zzz.R index e074c91..882fe7e 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -5,7 +5,7 @@ NULL .onLoad <- function(libname, pkgname) { - if (! requireNamespace("rjd3toolkit", quietly = TRUE)) stop("Loading rjd3 libraries failed") + if (!requireNamespace("rjd3toolkit", quietly = TRUE)) stop("Loading rjd3 libraries failed") # For debugts_ging: to see if Jars are effectively loaded # options(java.parameters = "-verbose:class") From a8004b9a132421f54bc0ee4c0ab6861a8fe3435d Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Mon, 6 May 2024 14:13:28 +0200 Subject: [PATCH 12/27] change rjdemetra3 --> rjd3workspace and rjdemetra --> rjdverse --- NEWS.md | 6 +- README.html | 878 +++++++++++++++++++++++++++++++++++++++++++++++++++ _pkgdown.yml | 2 +- 3 files changed, 882 insertions(+), 4 deletions(-) create mode 100644 README.html diff --git a/NEWS.md b/NEWS.md index 88b39d9..e75b03c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -36,6 +36,6 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). * New Jars -[Unreleased]: https://github.com/rjdemetra/rjd3filters/compare/v2.0.0...HEAD -[2.0.0]: https://github.com/rjdemetra/rjd3filters/releases/tag/v1.0.0...v2.0.0 -[1.0.0]: https://github.com/rjdemetra/rjd3filters/releases/tag/v1.0.0 +[Unreleased]: https://github.com/rjdverse/rjd3filters/compare/v2.0.0...HEAD +[2.0.0]: https://github.com/rjdverse/rjd3filters/releases/tag/v1.0.0...v2.0.0 +[1.0.0]: https://github.com/rjdverse/rjd3filters/releases/tag/v1.0.0 diff --git a/README.html b/README.html new file mode 100644 index 0000000..6266c90 --- /dev/null +++ b/README.html @@ -0,0 +1,878 @@ + + + + + + + + + + + + + + + + + + + + + +

rjd3filters

+

rjd3filters is an R package on linear filters for real-time +trend-cycle estimates. It allows to create symmetric and asymmetric +moving averages with:

+
    +
  • local polynomial filters, as defined by Proietti and Luati +(2008);

  • +
  • the FST approach of Grun-Rehomme, Guggemos, and Ladiray (2018), +based on the optimization of the three criteria Fidelity, Smoothness and +Timeliness;

  • +
  • the Reproducing Kernel Hilbert Space (RKHS) of Dagum and +Bianconcini (2008).

  • +
+

Some quality criteria defined by Wildi and McElroy (2019) can also be +computed.

+

Installation

+

rjd3filters relies on the rJava package.

+

Running rjd3 packages requires Java 17 or higher. +How to set up such a configuration in R is explained here

+

To get the current stable version (from the latest release):

+
# install.packages("remotes")
+remotes::install_github("rjdverse/rjd3toolkit@*release")
+remotes::install_github("rjdverse/rjd3filters@*release")
+

To get the current development version from GitHub:

+
# install.packages("remotes")
+# Install development version from GitHub
+remotes::install_github("rjdverse/rjd3toolkit")
+remotes::install_github("rjdverse/rjd3filters")
+

Basic example

+

In this example we use the same symmetric moving average (Henderson), +but we use three different methods to compute asymmetric filters. As a +consequence, the filtered time series is the same, except at the +boundaries.

+
library("rjd3filters")
+
+y <- window(retailsa$AllOtherGenMerchandiseStores, start = 2000)
+musgrave <- lp_filter(horizon = 6, kernel = "Henderson", endpoints = "LC")
+
+# we put a large weight on the timeliness criteria
+fst_notimeliness_filter <- lapply(0:6, fst_filter, 
+                                  lags = 6, smoothness.weight = 1/1000, 
+                                  timeliness.weight = 1-1/1000, pdegree =2)
+fst_notimeliness <- finite_filters(sfilter = fst_notimeliness_filter[[7]], 
+                                   rfilters = fst_notimeliness_filter[-7], 
+                                   first_to_last = TRUE)
+# RKHS filters minimizing timeliness
+rkhs_timeliness <- rkhs_filter(horizon = 6, asymmetricCriterion = "Timeliness")
+
+trend_musgrave <- filter(y, musgrave)
+trend_fst <- filter(y, fst_notimeliness)
+trend_rkhs <- filter(y, rkhs_timeliness)
+plot(ts.union(y, trend_musgrave, trend_fst, trend_rkhs), plot.type = "single", 
+     col = c("black", "orange", "lightblue", "red"), 
+     main = "Filtered time series", ylab=NULL)
+legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS"), 
+       col= c("black", "orange", "lightblue", "red"), lty = 1)
+ + +

The last estimates can also be analysed with the +implicit_forecast function that retreive the implicit +forecasts corresponding to the asymmetric filters (i.e., the forecasts +needed to have the same end-points estimates but using the symmetric +filter).

+
f_musgrave <- implicit_forecast(y, musgrave)
+f_fst <- implicit_forecast(y, fst_notimeliness)
+f_rkhs <- implicit_forecast(y, rkhs_timeliness)
+
+plot(window(y, start = 2007), 
+     xlim = c(2007, 2012), ylim = c(3600, 4600), 
+     main = "Last estimates and implicit forecast", ylab=NULL)
+lines(trend_musgrave, 
+      col = "orange")
+lines(trend_fst, 
+      col = "lightblue")
+lines(trend_rkhs, 
+      col = "red")
+lines(ts(c(tail(y, 1), f_musgrave), frequency = frequency(y), start = end(y)), 
+      col = "orange", lty = 2)
+lines(ts(c(tail(y, 1), f_fst), frequency = frequency(y), start = end(y)), 
+      col = "lightblue", lty = 2)
+lines(ts(c(tail(y, 1), f_rkhs), frequency = frequency(y), start = end(y)), 
+      col = "red", lty = 2)
+legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS", "Forecasts"), 
+       col= c("black", "orange", "lightblue", "red", "black"), 
+       lty = c(1, 1, 1, 1, 2))
+ + +

The real-time estimates (when no future points are available) can +also be compared:

+
trend_henderson<- filter(y, musgrave[, "q=6"])
+trend_musgrave_q0 <- filter(y, musgrave[, "q=0"])
+trend_fst_q0 <- filter(y, fst_notimeliness[, "q=0"])
+trend_rkhs_q0 <- filter(y, rkhs_timeliness[, "q=0"])
+plot(window(ts.union(y, trend_musgrave_q0, trend_fst_q0, trend_rkhs_q0), 
+            start = 2007), 
+     plot.type = "single", 
+     col = c("black", "orange", "lightblue", "red"), 
+     main = "Real time estimates of the trend", ylab=NULL)
+legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS"), 
+       col= c("black", "orange", "lightblue", "red"), lty = 1)
+ + +

Comparison of the filters

+

Different quality criteria from Grun-Rehomme et al (2018) +and Wildi and McElroy(2019) can also be computed with the function +diagnostic_matrix():

+
q_0_coefs <- list(Musgrave = musgrave[, "q=0"], 
+                  fst_notimeliness = fst_notimeliness[, "q=0"], 
+                  rkhs_timeliness = rkhs_timeliness[, "q=0"])
+
+sapply(X = q_0_coefs, 
+       FUN = diagnostic_matrix, 
+       lags = 6, 
+       sweights = musgrave[, "q=6"])
+#>         Musgrave fst_notimeliness rkhs_timeliness
+#> b_c  0.000000000     2.220446e-16     0.000000000
+#> b_l -0.575984377    -1.554312e-15    -0.611459167
+#> b_q -1.144593858     1.554312e-15     0.027626749
+#> F_g  0.357509832     9.587810e-01     0.381135700
+#> S_g  1.137610871     2.402400e+00     1.207752284
+#> T_g  0.034088260     4.676398e-04     0.023197411
+#> A_w  0.008306348     1.823745e-02     0.003677964
+#> S_w  0.449956378     3.575634e+00     0.628156109
+#> T_w  0.061789932     7.940547e-04     0.043540181
+#> R_w  0.299548665     1.721377e-01     0.219948644
+

The filters can also be compared by plotting there coefficients +(plot_coef), gain function (plot_gain) and +phase function (plot_phase):

+
def.par <- par(no.readonly = TRUE)
+par(mai = c(0.3, 0.3, 0.2, 0))
+layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
+
+plot_coef(fst_notimeliness, q = 0, col = "lightblue")
+plot_coef(musgrave, q = 0, add = TRUE, col = "orange")
+plot_coef(rkhs_timeliness, q = 0, add = TRUE, col = "red")
+legend("topleft", legend = c("Musgrave", "FST", "RKHS"), 
+       col= c("orange", "lightblue", "red"), lty = 1)
+
+plot_gain(fst_notimeliness, q = 0, col = "lightblue")
+plot_gain(musgrave, q = 0, col = "orange", add = TRUE)
+plot_gain(rkhs_timeliness, q = 0, add = TRUE, col = "red")
+legend("topright", legend = c("Musgrave", "FST", "RKHS"), 
+       col= c("orange", "lightblue", "red"), lty = 1)
+
+plot_phase(fst_notimeliness, q = 0, col = "lightblue")
+plot_phase(musgrave, q = 0, col = "orange", add = TRUE)
+plot_phase(rkhs_timeliness, q = 0, add = TRUE, col = "red")
+legend("topright", legend = c("Musgrave", "FST", "RKHS"), 
+       col= c("orange", "lightblue", "red"), lty = 1)
+par(def.par)
+ + +

Confidence intervals can also be computed with the +confint_filter function:

+
confint <- confint_filter(y, musgrave)
+
+plot(confint, plot.type = "single",
+     col = c("red", "black", "black"),
+     lty = c(1, 2, 2), xlab = NULL, ylab = NULL)
+lines(y, col = "grey")
+legend("topleft", legend = c("y", "Smoothed", "CI (95%)"), 
+       col= c("grey", "red", "black"), lty = c(1, 1, 2))
+ + +

Manipulate moving averages

+

You can also create and manipulate moving averages with the class +moving_average. In the next examples we show how to create +the M2X12 moving average, the first moving average used to extract the +trend-cycle in X-11, and the M3X3 moving average, applied to each months +to extract seasonal component.

+
e1 <- moving_average(rep(1, 12), lags = -6)
+e1 <- e1/sum(e1)
+e2 <- moving_average(rep(1/12, 12), lags = -5)
+M2X12 <- (e1 + e2)/2
+coef(M2X12)
+#>        t-6        t-5        t-4        t-3        t-2        t-1          t 
+#> 0.04166667 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 
+#>        t+1        t+2        t+3        t+4        t+5        t+6 
+#> 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.04166667
+M3 <- moving_average(rep(1/3, 3), lags = -1)
+M3X3 <- M3 * M3
+# M3X3 moving average applied to each month
+M3X3
+#> [1] "0.1111 B^2 + 0.2222 B + 0.3333 + 0.2222 F + 0.1111 F^2"
+M3X3_seasonal <- to_seasonal(M3X3, 12)
+# M3X3_seasonal moving average applied to the global series
+M3X3_seasonal
+#> [1] "0.1111 B^24 + 0.2222 B^12 + 0.3333 + 0.2222 F^12 + 0.1111 F^24"
+
+def.par <- par(no.readonly = TRUE)
+par(mai = c(0.5, 0.8, 0.3, 0))
+layout(matrix(c(1, 2), nrow = 1))
+plot_gain(M3X3, main = "M3X3 applied to each month")
+plot_gain(M3X3_seasonal, main = "M3X3 applied to the global series")
+ + +
par(def.par)
+
+# To apply the moving average
+t <- y * M2X12
+si <- y - t
+s <- si * M3X3_seasonal
+# or equivalently:
+s_mm <- M3X3_seasonal * (1 - M2X12)
+s <- y * s_mm
+

Manipulate finite filters

+

finite_filters object are a combination of a central +filter (used for the final estimates) and different asymmetric filters +used for intermediate estimates at the beginning/end of the series when +the central filter cannot be applied.

+
musgrave 
+#>             q=6          q=5          q=4          q=3          q=2
+#> t-6 -0.01934985 -0.016609040 -0.011623676 -0.009152423 -0.016139228
+#> t-5 -0.02786378 -0.025914479 -0.022541271 -0.020981640 -0.024948087
+#> t-4  0.00000000  0.001157790  0.002918842  0.003566851  0.002620762
+#> t-3  0.06549178  0.065858066  0.066006963  0.065743350  0.067817618
+#> t-2  0.14735651  0.146931288  0.145468029  0.144292794  0.149387420
+#> t-1  0.21433675  0.213120014  0.210044599  0.207957742  0.216072726
+#> t    0.24005716  0.238048915  0.233361346  0.230362866  0.241498208
+#>              q=1         q=0
+#> t-6 -0.037925830 -0.07371504
+#> t-5 -0.035216813 -0.04601336
+#> t-4  0.003869912  0.01806602
+#> t-3  0.080584644  0.11977342
+#> t-2  0.173672322  0.23785375
+#> t-1  0.251875504  0.34104960
+#> t    0.288818862  0.40298562
+#>  [ getOption("max.print") est atteint -- 6 lignes omises ]
+musgrave * M3X3
+#>              q=6          q=5          q=4           q=3          q=2
+#> t-8 -0.002149983 -0.001845449 -0.001291520 -0.0010169359 -0.001793248
+#> t-7 -0.007395941 -0.006570284 -0.005087625 -0.0043651651 -0.006358505
+#> t-6 -0.012641899 -0.011166476 -0.008559414 -0.0073170774 -0.010632566
+#> t-5 -0.006311026 -0.004754208 -0.002114058 -0.0009303015 -0.003784842
+#> t-4  0.022584742  0.023742532  0.025503585  0.0261515939  0.025205505
+#> t-3  0.075295705  0.075661988  0.075810884  0.0755472713  0.077621540
+#> t-2  0.137975973  0.137550748  0.136087489  0.1349122536  0.140006880
+#>              q=1         q=0
+#> t-8 -0.004213981 -0.00819056
+#> t-7 -0.012340941 -0.02149372
+#> t-6 -0.020037912 -0.03278954
+#> t-5 -0.010353070 -0.01439608
+#> t-4  0.026454654  0.04065076
+#> t-3  0.090388566  0.12957734
+#> t-2  0.164291782  0.22847321
+#>  [ getOption("max.print") est atteint -- 10 lignes omises ]
+

Bibliography

+

Dagum, Estela Bee and Silvia Bianconcini (2008). “The Henderson +Smoother in Reproducing Kernel Hilbert Space”. In: Journal of +Business & Economic Statistics 26, pp. 536–545. URL: https://ideas.repec.org/a/bes/jnlbes/v26y2008p536-545.html.

+

Grun-Rehomme, Michel, Fabien Guggemos, and Dominique Ladiray (2018). +“Asymmetric Moving Averages Minimizing Phase Shift”. In: Handbook on +Seasonal Adjustment. URL: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-001.

+

Proietti, Tommaso and Alessandra Luati (Dec. 2008). “Real time +estimation in local polynomial regression, with application to +trend-cycle analysis”. In: Ann. Appl. Stat. 2.4, pp. 1523–1553. +URL: https://doi.org/10.1214/08-AOAS195.

+

Wildi, Marc and Tucker McElroy (2019). “The trilemma between +accuracy, timeliness and smoothness in real-time signal extraction”. In: +International Journal of Forecasting 35.3, pp. 1072–1084. URL: +https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:3:p:1072-1084.

+

Package Maintenance and +contributing

+

Any contribution is welcome and should be done through pull requests +and/or issues. pull requests should include updated +tests and updated documentation. If +functionality is changed, docstrings should be added or updated.

+

Licensing

+

The code of this project is licensed under the European +Union Public Licence (EUPL).

+ + + diff --git a/_pkgdown.yml b/_pkgdown.yml index b479540..2dd69de 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,4 @@ -url: https://rjdemetra.github.io/rjd3filters/ +url: https://rjdverse.github.io/rjd3filters/ template: bootstrap: 5 From b121cd317f82008f484aeeef88d6608c3d0cebb3 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Tue, 7 May 2024 14:21:07 +0200 Subject: [PATCH 13/27] harmonisation of .gitignore and .Rbuildignore --- .Rbuildignore | 34 +- .gitignore | 56 +++- README.html | 878 -------------------------------------------------- 3 files changed, 72 insertions(+), 896 deletions(-) delete mode 100644 README.html diff --git a/.Rbuildignore b/.Rbuildignore index 55b2e91..89416d3 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,19 +1,27 @@ -^appveyor\.yml$ -^docs$ -^_pkgdown\.yml$ ^.*\.Rproj$ ^\.Rproj\.user$ -^.lintr$ -^README.Rmd$ + +^\.git$ +^\.github$ + +^README\.Rmd$ ^README-.*\.png$ -.travis.yml -^pkgdown$ + +^Meta$ +^docs$ ^doc$ +^pkgdown$ +^_pkgdown\.yml$ + +^\.lintr$ + +^appveyor\.yml$ +.travis.yml + cran-comments.md -TODO.R + +^\.DS_Store$ + ^revdep$ -workspace -workspace.xml -R/test_X11.R -^\.github$ -^LICENSE$ +^reconf\.sh$ +^rjd3toolkit-java-deps\.xml$ diff --git a/.gitignore b/.gitignore index ee10e6d..de7c8b2 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,55 @@ -.Rproj.user +# History files .Rhistory +.Rapp.history + +# Session Data files .RData +.RDataTmp + +# User-specific files .Ruserdata + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf +Meta/ +inst/doc/ +doc/ + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md + +# R Environment Variables +.Renviron + +# pkgdown site +docs/ + +# translation temp files +po/*~ + +# RStudio Connect folder +rsconnect/ + +# Hidden file from mac-os .DS_Store -workspace -workspace.xml -inst/doc -docs diff --git a/README.html b/README.html deleted file mode 100644 index 6266c90..0000000 --- a/README.html +++ /dev/null @@ -1,878 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - -

rjd3filters

-

rjd3filters is an R package on linear filters for real-time -trend-cycle estimates. It allows to create symmetric and asymmetric -moving averages with:

-
    -
  • local polynomial filters, as defined by Proietti and Luati -(2008);

  • -
  • the FST approach of Grun-Rehomme, Guggemos, and Ladiray (2018), -based on the optimization of the three criteria Fidelity, Smoothness and -Timeliness;

  • -
  • the Reproducing Kernel Hilbert Space (RKHS) of Dagum and -Bianconcini (2008).

  • -
-

Some quality criteria defined by Wildi and McElroy (2019) can also be -computed.

-

Installation

-

rjd3filters relies on the rJava package.

-

Running rjd3 packages requires Java 17 or higher. -How to set up such a configuration in R is explained here

-

To get the current stable version (from the latest release):

-
# install.packages("remotes")
-remotes::install_github("rjdverse/rjd3toolkit@*release")
-remotes::install_github("rjdverse/rjd3filters@*release")
-

To get the current development version from GitHub:

-
# install.packages("remotes")
-# Install development version from GitHub
-remotes::install_github("rjdverse/rjd3toolkit")
-remotes::install_github("rjdverse/rjd3filters")
-

Basic example

-

In this example we use the same symmetric moving average (Henderson), -but we use three different methods to compute asymmetric filters. As a -consequence, the filtered time series is the same, except at the -boundaries.

-
library("rjd3filters")
-
-y <- window(retailsa$AllOtherGenMerchandiseStores, start = 2000)
-musgrave <- lp_filter(horizon = 6, kernel = "Henderson", endpoints = "LC")
-
-# we put a large weight on the timeliness criteria
-fst_notimeliness_filter <- lapply(0:6, fst_filter, 
-                                  lags = 6, smoothness.weight = 1/1000, 
-                                  timeliness.weight = 1-1/1000, pdegree =2)
-fst_notimeliness <- finite_filters(sfilter = fst_notimeliness_filter[[7]], 
-                                   rfilters = fst_notimeliness_filter[-7], 
-                                   first_to_last = TRUE)
-# RKHS filters minimizing timeliness
-rkhs_timeliness <- rkhs_filter(horizon = 6, asymmetricCriterion = "Timeliness")
-
-trend_musgrave <- filter(y, musgrave)
-trend_fst <- filter(y, fst_notimeliness)
-trend_rkhs <- filter(y, rkhs_timeliness)
-plot(ts.union(y, trend_musgrave, trend_fst, trend_rkhs), plot.type = "single", 
-     col = c("black", "orange", "lightblue", "red"), 
-     main = "Filtered time series", ylab=NULL)
-legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS"), 
-       col= c("black", "orange", "lightblue", "red"), lty = 1)
- - -

The last estimates can also be analysed with the -implicit_forecast function that retreive the implicit -forecasts corresponding to the asymmetric filters (i.e., the forecasts -needed to have the same end-points estimates but using the symmetric -filter).

-
f_musgrave <- implicit_forecast(y, musgrave)
-f_fst <- implicit_forecast(y, fst_notimeliness)
-f_rkhs <- implicit_forecast(y, rkhs_timeliness)
-
-plot(window(y, start = 2007), 
-     xlim = c(2007, 2012), ylim = c(3600, 4600), 
-     main = "Last estimates and implicit forecast", ylab=NULL)
-lines(trend_musgrave, 
-      col = "orange")
-lines(trend_fst, 
-      col = "lightblue")
-lines(trend_rkhs, 
-      col = "red")
-lines(ts(c(tail(y, 1), f_musgrave), frequency = frequency(y), start = end(y)), 
-      col = "orange", lty = 2)
-lines(ts(c(tail(y, 1), f_fst), frequency = frequency(y), start = end(y)), 
-      col = "lightblue", lty = 2)
-lines(ts(c(tail(y, 1), f_rkhs), frequency = frequency(y), start = end(y)), 
-      col = "red", lty = 2)
-legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS", "Forecasts"), 
-       col= c("black", "orange", "lightblue", "red", "black"), 
-       lty = c(1, 1, 1, 1, 2))
- - -

The real-time estimates (when no future points are available) can -also be compared:

-
trend_henderson<- filter(y, musgrave[, "q=6"])
-trend_musgrave_q0 <- filter(y, musgrave[, "q=0"])
-trend_fst_q0 <- filter(y, fst_notimeliness[, "q=0"])
-trend_rkhs_q0 <- filter(y, rkhs_timeliness[, "q=0"])
-plot(window(ts.union(y, trend_musgrave_q0, trend_fst_q0, trend_rkhs_q0), 
-            start = 2007), 
-     plot.type = "single", 
-     col = c("black", "orange", "lightblue", "red"), 
-     main = "Real time estimates of the trend", ylab=NULL)
-legend("topleft", legend = c("y", "Musgrave", "FST", "RKHS"), 
-       col= c("black", "orange", "lightblue", "red"), lty = 1)
- - -

Comparison of the filters

-

Different quality criteria from Grun-Rehomme et al (2018) -and Wildi and McElroy(2019) can also be computed with the function -diagnostic_matrix():

-
q_0_coefs <- list(Musgrave = musgrave[, "q=0"], 
-                  fst_notimeliness = fst_notimeliness[, "q=0"], 
-                  rkhs_timeliness = rkhs_timeliness[, "q=0"])
-
-sapply(X = q_0_coefs, 
-       FUN = diagnostic_matrix, 
-       lags = 6, 
-       sweights = musgrave[, "q=6"])
-#>         Musgrave fst_notimeliness rkhs_timeliness
-#> b_c  0.000000000     2.220446e-16     0.000000000
-#> b_l -0.575984377    -1.554312e-15    -0.611459167
-#> b_q -1.144593858     1.554312e-15     0.027626749
-#> F_g  0.357509832     9.587810e-01     0.381135700
-#> S_g  1.137610871     2.402400e+00     1.207752284
-#> T_g  0.034088260     4.676398e-04     0.023197411
-#> A_w  0.008306348     1.823745e-02     0.003677964
-#> S_w  0.449956378     3.575634e+00     0.628156109
-#> T_w  0.061789932     7.940547e-04     0.043540181
-#> R_w  0.299548665     1.721377e-01     0.219948644
-

The filters can also be compared by plotting there coefficients -(plot_coef), gain function (plot_gain) and -phase function (plot_phase):

-
def.par <- par(no.readonly = TRUE)
-par(mai = c(0.3, 0.3, 0.2, 0))
-layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
-
-plot_coef(fst_notimeliness, q = 0, col = "lightblue")
-plot_coef(musgrave, q = 0, add = TRUE, col = "orange")
-plot_coef(rkhs_timeliness, q = 0, add = TRUE, col = "red")
-legend("topleft", legend = c("Musgrave", "FST", "RKHS"), 
-       col= c("orange", "lightblue", "red"), lty = 1)
-
-plot_gain(fst_notimeliness, q = 0, col = "lightblue")
-plot_gain(musgrave, q = 0, col = "orange", add = TRUE)
-plot_gain(rkhs_timeliness, q = 0, add = TRUE, col = "red")
-legend("topright", legend = c("Musgrave", "FST", "RKHS"), 
-       col= c("orange", "lightblue", "red"), lty = 1)
-
-plot_phase(fst_notimeliness, q = 0, col = "lightblue")
-plot_phase(musgrave, q = 0, col = "orange", add = TRUE)
-plot_phase(rkhs_timeliness, q = 0, add = TRUE, col = "red")
-legend("topright", legend = c("Musgrave", "FST", "RKHS"), 
-       col= c("orange", "lightblue", "red"), lty = 1)
-par(def.par)
- - -

Confidence intervals can also be computed with the -confint_filter function:

-
confint <- confint_filter(y, musgrave)
-
-plot(confint, plot.type = "single",
-     col = c("red", "black", "black"),
-     lty = c(1, 2, 2), xlab = NULL, ylab = NULL)
-lines(y, col = "grey")
-legend("topleft", legend = c("y", "Smoothed", "CI (95%)"), 
-       col= c("grey", "red", "black"), lty = c(1, 1, 2))
- - -

Manipulate moving averages

-

You can also create and manipulate moving averages with the class -moving_average. In the next examples we show how to create -the M2X12 moving average, the first moving average used to extract the -trend-cycle in X-11, and the M3X3 moving average, applied to each months -to extract seasonal component.

-
e1 <- moving_average(rep(1, 12), lags = -6)
-e1 <- e1/sum(e1)
-e2 <- moving_average(rep(1/12, 12), lags = -5)
-M2X12 <- (e1 + e2)/2
-coef(M2X12)
-#>        t-6        t-5        t-4        t-3        t-2        t-1          t 
-#> 0.04166667 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 
-#>        t+1        t+2        t+3        t+4        t+5        t+6 
-#> 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.04166667
-M3 <- moving_average(rep(1/3, 3), lags = -1)
-M3X3 <- M3 * M3
-# M3X3 moving average applied to each month
-M3X3
-#> [1] "0.1111 B^2 + 0.2222 B + 0.3333 + 0.2222 F + 0.1111 F^2"
-M3X3_seasonal <- to_seasonal(M3X3, 12)
-# M3X3_seasonal moving average applied to the global series
-M3X3_seasonal
-#> [1] "0.1111 B^24 + 0.2222 B^12 + 0.3333 + 0.2222 F^12 + 0.1111 F^24"
-
-def.par <- par(no.readonly = TRUE)
-par(mai = c(0.5, 0.8, 0.3, 0))
-layout(matrix(c(1, 2), nrow = 1))
-plot_gain(M3X3, main = "M3X3 applied to each month")
-plot_gain(M3X3_seasonal, main = "M3X3 applied to the global series")
- - -
par(def.par)
-
-# To apply the moving average
-t <- y * M2X12
-si <- y - t
-s <- si * M3X3_seasonal
-# or equivalently:
-s_mm <- M3X3_seasonal * (1 - M2X12)
-s <- y * s_mm
-

Manipulate finite filters

-

finite_filters object are a combination of a central -filter (used for the final estimates) and different asymmetric filters -used for intermediate estimates at the beginning/end of the series when -the central filter cannot be applied.

-
musgrave 
-#>             q=6          q=5          q=4          q=3          q=2
-#> t-6 -0.01934985 -0.016609040 -0.011623676 -0.009152423 -0.016139228
-#> t-5 -0.02786378 -0.025914479 -0.022541271 -0.020981640 -0.024948087
-#> t-4  0.00000000  0.001157790  0.002918842  0.003566851  0.002620762
-#> t-3  0.06549178  0.065858066  0.066006963  0.065743350  0.067817618
-#> t-2  0.14735651  0.146931288  0.145468029  0.144292794  0.149387420
-#> t-1  0.21433675  0.213120014  0.210044599  0.207957742  0.216072726
-#> t    0.24005716  0.238048915  0.233361346  0.230362866  0.241498208
-#>              q=1         q=0
-#> t-6 -0.037925830 -0.07371504
-#> t-5 -0.035216813 -0.04601336
-#> t-4  0.003869912  0.01806602
-#> t-3  0.080584644  0.11977342
-#> t-2  0.173672322  0.23785375
-#> t-1  0.251875504  0.34104960
-#> t    0.288818862  0.40298562
-#>  [ getOption("max.print") est atteint -- 6 lignes omises ]
-musgrave * M3X3
-#>              q=6          q=5          q=4           q=3          q=2
-#> t-8 -0.002149983 -0.001845449 -0.001291520 -0.0010169359 -0.001793248
-#> t-7 -0.007395941 -0.006570284 -0.005087625 -0.0043651651 -0.006358505
-#> t-6 -0.012641899 -0.011166476 -0.008559414 -0.0073170774 -0.010632566
-#> t-5 -0.006311026 -0.004754208 -0.002114058 -0.0009303015 -0.003784842
-#> t-4  0.022584742  0.023742532  0.025503585  0.0261515939  0.025205505
-#> t-3  0.075295705  0.075661988  0.075810884  0.0755472713  0.077621540
-#> t-2  0.137975973  0.137550748  0.136087489  0.1349122536  0.140006880
-#>              q=1         q=0
-#> t-8 -0.004213981 -0.00819056
-#> t-7 -0.012340941 -0.02149372
-#> t-6 -0.020037912 -0.03278954
-#> t-5 -0.010353070 -0.01439608
-#> t-4  0.026454654  0.04065076
-#> t-3  0.090388566  0.12957734
-#> t-2  0.164291782  0.22847321
-#>  [ getOption("max.print") est atteint -- 10 lignes omises ]
-

Bibliography

-

Dagum, Estela Bee and Silvia Bianconcini (2008). “The Henderson -Smoother in Reproducing Kernel Hilbert Space”. In: Journal of -Business & Economic Statistics 26, pp. 536–545. URL: https://ideas.repec.org/a/bes/jnlbes/v26y2008p536-545.html.

-

Grun-Rehomme, Michel, Fabien Guggemos, and Dominique Ladiray (2018). -“Asymmetric Moving Averages Minimizing Phase Shift”. In: Handbook on -Seasonal Adjustment. URL: https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-GQ-18-001.

-

Proietti, Tommaso and Alessandra Luati (Dec. 2008). “Real time -estimation in local polynomial regression, with application to -trend-cycle analysis”. In: Ann. Appl. Stat. 2.4, pp. 1523–1553. -URL: https://doi.org/10.1214/08-AOAS195.

-

Wildi, Marc and Tucker McElroy (2019). “The trilemma between -accuracy, timeliness and smoothness in real-time signal extraction”. In: -International Journal of Forecasting 35.3, pp. 1072–1084. URL: -https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:3:p:1072-1084.

-

Package Maintenance and -contributing

-

Any contribution is welcome and should be done through pull requests -and/or issues. pull requests should include updated -tests and updated documentation. If -functionality is changed, docstrings should be added or updated.

-

Licensing

-

The code of this project is licensed under the European -Union Public Licence (EUPL).

- - - From 9469fa32f9d8c624cffb37153918aae329897fa5 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Wed, 15 May 2024 11:49:02 +0200 Subject: [PATCH 14/27] Remove automarted coverage test (currently useless) --- .github/workflows/test-coverage.yaml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml index 7d814af..972fad9 100644 --- a/.github/workflows/test-coverage.yaml +++ b/.github/workflows/test-coverage.yaml @@ -1,10 +1,7 @@ # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: - push: - branches: [main, develop] - pull_request: - branches: [main, develop] + workflow_dispatch name: test-coverage From d124555df382b30a2e992fbbf39b6d28a7bcfe87 Mon Sep 17 00:00:00 2001 From: Anna Smyk <43274870+annasmyk@users.noreply.github.com> Date: Wed, 15 May 2024 13:34:40 +0200 Subject: [PATCH 15/27] Update DESCRIPTION --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 465c735..744ccd9 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -5,10 +5,10 @@ Version: 2.1.1 Authors@R: c( person("Jean", "Palate", role = c("aut"), email = "palatejean@gmail.com"), - person("Alain", "Quartier-la-Tente", role = c("aut"), + person("Alain", "Quartier-la-Tente", role = c("aut","cre"), email = "alain.quartier@yahoo.fr", comment = c(ORCID = "0000-0001-7890-3857")), - person("Tanguy", "Barthelemy", role = c("aut","cre"), + person("Tanguy", "Barthelemy", role = c("aut"), email ="tanguy.barthelemy@insee.fr"), person("Anna", "Smyk", role = c("aut"), email ="anna.smyk@insee.fr") From 8978ac44b71c1df3104409467cb35e249c2841d3 Mon Sep 17 00:00:00 2001 From: Philippe Charles Date: Thu, 16 May 2024 12:05:53 +0200 Subject: [PATCH 16/27] Add copy-jars script --- pom.xml | 121 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 121 insertions(+) create mode 100644 pom.xml diff --git a/pom.xml b/pom.xml new file mode 100644 index 0000000..407c3eb --- /dev/null +++ b/pom.xml @@ -0,0 +1,121 @@ + + + 4.0.0 + + com.github.rjdverse.rjd3filters + rjd3filters + ignore_version + pom + + Usage: mvn -Pcopy-jars + + + 2.1.0 + + + + + eu.europa.ec.joinup.sat + jdplus-filters-base-r + ${jdplus-incubator.version} + + + * + jdplus-toolkit-base-api + + + * + jdplus-toolkit-base-core + + + * + jdplus-toolkit-base-r + + + + + + + ${project.basedir}/inst/java + + + + org.simplify4u.plugins + pgpverify-maven-plugin + 1.17.0 + + + org.apache.maven.plugins + maven-dependency-plugin + 3.6.1 + + + + + + + + copy-jars + + clean package + + + org.simplify4u.plugins + pgpverify-maven-plugin + + + check-signatures + + check + + + true + true + + + + + + org.apache.maven.plugins + maven-dependency-plugin + + + copy-jars + + copy-dependencies + + package + + ${project.build.directory} + + + + + + + + + + + + + + central + https://repo.maven.apache.org/maven2 + + false + + + + jdemetra-snapshots + https://oss.sonatype.org/content/repositories/snapshots/ + + false + + + true + + + + From c3f12fc7944db459d16d4b3b5c6211c3ca5d9119 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Thu, 16 May 2024 16:05:55 +0200 Subject: [PATCH 17/27] Add pom.xml to .RBuildIgnore and the produced README.html to .gitignore --- .Rbuildignore | 2 +- .gitignore | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.Rbuildignore b/.Rbuildignore index 89416d3..6e0d1c5 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -24,4 +24,4 @@ cran-comments.md ^revdep$ ^reconf\.sh$ -^rjd3toolkit-java-deps\.xml$ +^pom\.xml$ diff --git a/.gitignore b/.gitignore index de7c8b2..11424ef 100644 --- a/.gitignore +++ b/.gitignore @@ -53,3 +53,6 @@ rsconnect/ # Hidden file from mac-os .DS_Store + +# produced README.html +README.html From 3680f16aa0cb8632dc22a8068b2033f5f19f962a Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Tue, 21 May 2024 09:50:58 +0200 Subject: [PATCH 18/27] Correct bug in specifying the maximu line number to print --- README.Rmd | 3 ++- README.md | 34 ++++++++++++++++++++++++++++++++-- 2 files changed, 34 insertions(+), 3 deletions(-) diff --git a/README.Rmd b/README.Rmd index 5f8f4cc..61c6cc1 100644 --- a/README.Rmd +++ b/README.Rmd @@ -10,8 +10,9 @@ knitr::opts_chunk$set( comment = "#>", fig.path = "man/figures/README-", fig.align="center", - fig.width = 8 + fig.width = 8L ) +options(max.print = 1000L) ``` # rjd3filters diff --git a/README.md b/README.md index 018c102..e5da835 100644 --- a/README.md +++ b/README.md @@ -272,6 +272,12 @@ musgrave #> t-2 0.14735651 0.146931288 0.145468029 0.144292794 0.149387420 #> t-1 0.21433675 0.213120014 0.210044599 0.207957742 0.216072726 #> t 0.24005716 0.238048915 0.233361346 0.230362866 0.241498208 +#> t+1 0.21433675 0.211536998 0.205237273 0.201327171 0.215482871 +#> t+2 0.14735651 0.143765257 0.135853376 0.131031652 0.148207710 +#> t+3 0.06549178 0.061109020 0.051584983 0.045851637 0.000000000 +#> t+4 0.00000000 -0.005174272 -0.016310464 0.000000000 0.000000000 +#> t+5 -0.02786378 -0.033829557 0.000000000 0.000000000 0.000000000 +#> t+6 -0.01934985 0.000000000 0.000000000 0.000000000 0.000000000 #> q=1 q=0 #> t-6 -0.037925830 -0.07371504 #> t-5 -0.035216813 -0.04601336 @@ -280,7 +286,12 @@ musgrave #> t-2 0.173672322 0.23785375 #> t-1 0.251875504 0.34104960 #> t 0.288818862 0.40298562 -#> [ getOption("max.print") est atteint -- 6 lignes omises ] +#> t+1 0.274321400 0.00000000 +#> t+2 0.000000000 0.00000000 +#> t+3 0.000000000 0.00000000 +#> t+4 0.000000000 0.00000000 +#> t+5 0.000000000 0.00000000 +#> t+6 0.000000000 0.00000000 musgrave * M3X3 #> q=6 q=5 q=4 q=3 q=2 #> t-8 -0.002149983 -0.001845449 -0.001291520 -0.0010169359 -0.001793248 @@ -290,6 +301,16 @@ musgrave * M3X3 #> t-4 0.022584742 0.023742532 0.025503585 0.0261515939 0.025205505 #> t-3 0.075295705 0.075661988 0.075810884 0.0755472713 0.077621540 #> t-2 0.137975973 0.137550748 0.136087489 0.1349122536 0.140006880 +#> t-1 0.188629568 0.187412835 0.184337420 0.1822505629 0.190365547 +#> t 0.208025720 0.206017479 0.201329910 0.1983314300 0.209466772 +#> t+1 0.188629568 0.185829819 0.179530094 0.1756199919 0.182437019 +#> t+2 0.137975973 0.134384717 0.126472836 0.1242017153 0.124120786 +#> t+3 0.075295705 0.070912941 0.066564227 0.0667717095 0.056877588 +#> t+4 0.022584742 0.020311263 0.021121328 0.0247483251 0.016467523 +#> t+5 -0.006311026 -0.005636466 0.002107117 0.0050946263 0.000000000 +#> t+6 -0.012641899 -0.008092598 -0.001812274 0.0000000000 0.000000000 +#> t+7 -0.007395941 -0.003758840 0.000000000 0.0000000000 0.000000000 +#> t+8 -0.002149983 0.000000000 0.000000000 0.0000000000 0.000000000 #> q=1 q=0 #> t-8 -0.004213981 -0.00819056 #> t-7 -0.012340941 -0.02149372 @@ -298,7 +319,16 @@ musgrave * M3X3 #> t-4 0.026454654 0.04065076 #> t-3 0.090388566 0.12957734 #> t-2 0.164291782 0.22847321 -#> [ getOption("max.print") est atteint -- 10 lignes omises ] +#> t-1 0.226168325 0.26940011 +#> t 0.232502524 0.23654553 +#> t+1 0.183608603 0.12744676 +#> t+2 0.093051296 0.04477618 +#> t+3 0.030480156 0.00000000 +#> t+4 0.000000000 0.00000000 +#> t+5 0.000000000 0.00000000 +#> t+6 0.000000000 0.00000000 +#> t+7 0.000000000 0.00000000 +#> t+8 0.000000000 0.00000000 ``` ## Bibliography From 8a0e7e4ab126a6470df6e348b2e275a6fb006888 Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 22 May 2024 17:43:56 +0200 Subject: [PATCH 19/27] doc correction --- R/2_finite_filters.R | 6 +++++- man/impute_last_obs.Rd | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/R/2_finite_filters.R b/R/2_finite_filters.R index 7836720..a8428fe 100644 --- a/R/2_finite_filters.R +++ b/R/2_finite_filters.R @@ -504,7 +504,11 @@ to_seasonal.finite_filters <- function(x, s){ #' @details #' When combining finite filters and a moving average, the first and/or the last points cannot be computed. #' -#' For example, using the M2X12 moving average (symmetric moving average with coefficients \eqn{\theta = \begin{pmatrix} 1/24 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/24 \end{pmatrix}}), the first and last 6 points cannot be computed. +#' For example, using the M2X12 moving average, that is to say the symmetric moving average with coefficients +#' \deqn{ +#' \theta = \frac{1}{24}B^{6} + \frac{1}{12}B^{5}+\dots+\frac{1}{12}B^{-5}+\frac{1}{24}B^{-6}, +#' } +#' the first and last 6 points cannot be computed. #' #' `impute_last_obs()` allows to impute the first/last points using the `nperiod` previous filtered data. With `nperiod = 1`, the last filtered data is used for the imputation, with `nperiod = 12` and monthly data, the last year filtered data is used for the imputation, etc. #' diff --git a/man/impute_last_obs.Rd b/man/impute_last_obs.Rd index beaca05..147f8a8 100644 --- a/man/impute_last_obs.Rd +++ b/man/impute_last_obs.Rd @@ -24,7 +24,11 @@ Impute Incomplete Finite Filters \details{ When combining finite filters and a moving average, the first and/or the last points cannot be computed. -For example, using the M2X12 moving average (symmetric moving average with coefficients \eqn{\theta = \begin{pmatrix} 1/24 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/12 & 1/24 \end{pmatrix}}), the first and last 6 points cannot be computed. +For example, using the M2X12 moving average, that is to say the symmetric moving average with coefficients +\deqn{ +\theta = \frac{1}{24}B^{6} + \frac{1}{12}B^{5}+\dots+\frac{1}{12}B^{-5}+\frac{1}{24}B^{-6}, +} +the first and last 6 points cannot be computed. \code{impute_last_obs()} allows to impute the first/last points using the \code{nperiod} previous filtered data. With \code{nperiod = 1}, the last filtered data is used for the imputation, with \code{nperiod = 12} and monthly data, the last year filtered data is used for the imputation, etc. } From e9fc40a48f4914f95ec80ec7e606436d1c8a6281 Mon Sep 17 00:00:00 2001 From: AQLT Date: Wed, 22 May 2024 17:45:15 +0200 Subject: [PATCH 20/27] aut -> ctb --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 744ccd9..f770544 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,9 +8,9 @@ Authors@R: c( person("Alain", "Quartier-la-Tente", role = c("aut","cre"), email = "alain.quartier@yahoo.fr", comment = c(ORCID = "0000-0001-7890-3857")), - person("Tanguy", "Barthelemy", role = c("aut"), + person("Tanguy", "Barthelemy", role = c("ctb"), email ="tanguy.barthelemy@insee.fr"), - person("Anna", "Smyk", role = c("aut"), + person("Anna", "Smyk", role = c("ctb"), email ="anna.smyk@insee.fr") ) Description: This package provides functions to build and apply symmetric From 24f3aa20c4aa99d250ded11d0e565e74eb93e42e Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Fri, 7 Jun 2024 17:10:21 +0200 Subject: [PATCH 21/27] add new logo and badges --- R/1_moving_average.R | 4 +- R/2_finite_filters.R | 2 +- R/fst_filters.R | 6 +- README.Rmd | 12 +- README.md | 26 +- man/figures/logo.png | Bin 0 -> 213916 bytes man/figures/logo.svg | 556 +++++++++++++++++++ pkgdown/favicon/apple-touch-icon-120x120.png | Bin 0 -> 10252 bytes pkgdown/favicon/apple-touch-icon-152x152.png | Bin 0 -> 14018 bytes pkgdown/favicon/apple-touch-icon-180x180.png | Bin 0 -> 17557 bytes pkgdown/favicon/apple-touch-icon-60x60.png | Bin 0 -> 3955 bytes pkgdown/favicon/apple-touch-icon-76x76.png | Bin 0 -> 5335 bytes pkgdown/favicon/apple-touch-icon.png | Bin 0 -> 17557 bytes pkgdown/favicon/favicon-16x16.png | Bin 0 -> 1014 bytes pkgdown/favicon/favicon-32x32.png | Bin 0 -> 2063 bytes pkgdown/favicon/favicon.ico | Bin 0 -> 15086 bytes 16 files changed, 598 insertions(+), 8 deletions(-) create mode 100644 man/figures/logo.png create mode 100644 man/figures/logo.svg create mode 100644 pkgdown/favicon/apple-touch-icon-120x120.png create mode 100644 pkgdown/favicon/apple-touch-icon-152x152.png create mode 100644 pkgdown/favicon/apple-touch-icon-180x180.png create mode 100644 pkgdown/favicon/apple-touch-icon-60x60.png create mode 100644 pkgdown/favicon/apple-touch-icon-76x76.png create mode 100644 pkgdown/favicon/apple-touch-icon.png create mode 100644 pkgdown/favicon/favicon-16x16.png create mode 100644 pkgdown/favicon/favicon-32x32.png create mode 100644 pkgdown/favicon/favicon.ico diff --git a/R/1_moving_average.R b/R/1_moving_average.R index 7194a19..5a9a2c9 100644 --- a/R/1_moving_average.R +++ b/R/1_moving_average.R @@ -75,7 +75,7 @@ NULL #' @export moving_average <- function(x, lags = -length(x), trailing_zero = FALSE, leading_zero = FALSE){ if (inherits(x, "moving_average")) - return (x) + return(x) x <- as.numeric(x) if (trailing_zero) x <- rm_trailing_zero_or_na(x) @@ -193,7 +193,7 @@ setMethod("[", indices <- seq_along(coefs)[i] coefs[-indices] <- 0 if (all(coefs == 0)) - return (moving_average(0, lags = lower_bound(x) + indices - 1)) + return(moving_average(0, lags = lower_bound(x) + indices - 1)) moving_average(coefs, lags = lower_bound(x), leading_zero = TRUE, trailing_zero = TRUE) diff --git a/R/2_finite_filters.R b/R/2_finite_filters.R index 7836720..e2986e7 100644 --- a/R/2_finite_filters.R +++ b/R/2_finite_filters.R @@ -329,7 +329,7 @@ as.matrix.finite_filters <- function(x, sfilter = TRUE, rfilters = TRUE, lfilter sfilter_s <- rfilters_s <- lfilters_s <- index_s <- index_r <- index_l <- NULL if (!any(sfilter, rfilters, lfilters)) - return (NULL) + return(NULL) if (sfilter) { sfilter_s <- list(x@sfilter) index_s <- length(x@rfilters) diff --git a/R/fst_filters.R b/R/fst_filters.R index a9f85d8..4508ee5 100644 --- a/R/fst_filters.R +++ b/R/fst_filters.R @@ -117,7 +117,7 @@ fst.moving_average<-function(weights, lags, passband=pi/6, ...){ fst.finite_filters<-function(weights, lags, passband=pi/6, sfilter = TRUE, rfilters = TRUE, lfilters = FALSE, ...){ if (!any(sfilter, rfilters, lfilters)) - return (NULL) + return(NULL) sfilter_s <- rfilters_s <- lfilters_s <- index_s <- index_r <- index_l <- NULL @@ -184,13 +184,13 @@ mse.default<-function(aweights, sweights, density=c("uniform", "rw"), passband = spectral <- match.arg(density) rslt<-.jcall("jdplus/filters/base/core/AdvancedFiltersToolkit", "[D", "mseDecomposition", sweights, aweights, spectral, passband) - return (c(accuracy=rslt[1], smoothness=rslt[2], timeliness=rslt[3], residual=rslt[4])) + return(c(accuracy=rslt[1], smoothness=rslt[2], timeliness=rslt[3], residual=rslt[4])) } #' @export mse.finite_filters<-function(aweights, sweights = aweights@sfilter, density=c("uniform", "rw"), passband = pi/6, sfilter = TRUE, rfilters = TRUE, lfilters = FALSE, ...){ if (!any(sfilter, rfilters, lfilters)) - return (NULL) + return(NULL) sfilter_s <- rfilters_s <- lfilters_s <- index_s <- index_r <- index_l <- NULL diff --git a/README.Rmd b/README.Rmd index 61c6cc1..c521901 100644 --- a/README.Rmd +++ b/README.Rmd @@ -15,7 +15,17 @@ knitr::opts_chunk$set( options(max.print = 1000L) ``` -# rjd3filters +# `rjd3filters` + + +[![CRAN status](https://www.r-pkg.org/badges/version/rjd3filters)](https://CRAN.R-project.org/package=rjd3filters) + +[![R-CMD-check](https://github.com/rjdverse/rjd3filters/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/R-CMD-check.yaml) +[![lint](https://github.com/rjdverse/rjd3filters/actions/workflows/lint.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/lint.yaml) + +[![GH Pages built](https://github.com/rjdverse/rjd3filters/actions/workflows/pkgdown.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/pkgdown.yaml) + + rjd3filters is an R package on linear filters for real-time trend-cycle estimates. It allows to create symmetric and asymmetric moving averages with: diff --git a/README.md b/README.md index e5da835..529b94e 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,19 @@ -# rjd3filters +# `rjd3filters` + + + +[![CRAN +status](https://www.r-pkg.org/badges/version/rjd3filters)](https://CRAN.R-project.org/package=rjd3filters) + +[![R-CMD-check](https://github.com/rjdverse/rjd3filters/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/R-CMD-check.yaml) +[![lint](https://github.com/rjdverse/rjd3filters/actions/workflows/lint.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/lint.yaml) + +[![GH Pages +built](https://github.com/rjdverse/rjd3filters/actions/workflows/pkgdown.yaml/badge.svg)](https://github.com/rjdverse/rjd3filters/actions/workflows/pkgdown.yaml) + rjd3filters is an R package on linear filters for real-time trend-cycle estimates. It allows to create symmetric and asymmetric moving averages @@ -224,15 +236,24 @@ coef(M2X12) #> 0.04166667 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 #> t+1 t+2 t+3 t+4 t+5 t+6 #> 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.04166667 +``` + +``` r M3 <- moving_average(rep(1/3, 3), lags = -1) M3X3 <- M3 * M3 # M3X3 moving average applied to each month M3X3 #> [1] "0.1111 B^2 + 0.2222 B + 0.3333 + 0.2222 F + 0.1111 F^2" +``` + +``` r M3X3_seasonal <- to_seasonal(M3X3, 12) # M3X3_seasonal moving average applied to the global series M3X3_seasonal #> [1] "0.1111 B^24 + 0.2222 B^12 + 0.3333 + 0.2222 F^12 + 0.1111 F^24" +``` + +``` r def.par <- par(no.readonly = TRUE) par(mai = c(0.5, 0.8, 0.3, 0)) @@ -292,6 +313,9 @@ musgrave #> t+4 0.000000000 0.00000000 #> t+5 0.000000000 0.00000000 #> t+6 0.000000000 0.00000000 +``` + +``` r musgrave * M3X3 #> q=6 q=5 q=4 q=3 q=2 #> t-8 -0.002149983 -0.001845449 -0.001291520 -0.0010169359 -0.001793248 diff --git 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How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#Rconfig) + +### Latest release + To get the current stable version (from the latest release): -```{r, eval = FALSE} +- From GitHub: + +```{r, echo = TRUE, eval = FALSE} # install.packages("remotes") remotes::install_github("rjdverse/rjd3toolkit@*release") remotes::install_github("rjdverse/rjd3filters@*release") ``` +- From [r-universe](https://rjdverse.r-universe.dev/rjd3filters): + +```{r, echo = TRUE, eval = FALSE} +install.packages("rjd3filters", repos = c("https://rjdverse.r-universe.dev", "https://cloud.r-project.org")) +``` + + +### Development version + To get the current development version from GitHub: -```{r, eval = FALSE} +```{r, echo = TRUE, eval = FALSE} # install.packages("remotes") -# Install development version from GitHub -remotes::install_github("rjdverse/rjd3toolkit") remotes::install_github("rjdverse/rjd3filters") ``` diff --git a/README.md b/README.md index 529b94e..073f905 100644 --- a/README.md +++ b/README.md @@ -40,20 +40,30 @@ Running rjd3 packages requires **Java 17 or higher**. How to set up such a configuration in R is explained [here](https://jdemetra-new-documentation.netlify.app/#Rconfig) +### Latest release + To get the current stable version (from the latest release): +- From GitHub: + ``` r # install.packages("remotes") remotes::install_github("rjdverse/rjd3toolkit@*release") remotes::install_github("rjdverse/rjd3filters@*release") ``` +- From [r-universe](https://rjdverse.r-universe.dev/rjd3filters): + +``` r +install.packages("rjd3filters", repos = c("https://rjdverse.r-universe.dev", "https://cloud.r-project.org")) +``` + +### Development version + To get the current development version from GitHub: ``` r # install.packages("remotes") -# Install development version from GitHub -remotes::install_github("rjdverse/rjd3toolkit") remotes::install_github("rjdverse/rjd3filters") ``` @@ -236,24 +246,15 @@ coef(M2X12) #> 0.04166667 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 #> t+1 t+2 t+3 t+4 t+5 t+6 #> 0.08333333 0.08333333 0.08333333 0.08333333 0.08333333 0.04166667 -``` - -``` r M3 <- moving_average(rep(1/3, 3), lags = -1) M3X3 <- M3 * M3 # M3X3 moving average applied to each month M3X3 #> [1] "0.1111 B^2 + 0.2222 B + 0.3333 + 0.2222 F + 0.1111 F^2" -``` - -``` r M3X3_seasonal <- to_seasonal(M3X3, 12) # M3X3_seasonal moving average applied to the global series M3X3_seasonal #> [1] "0.1111 B^24 + 0.2222 B^12 + 0.3333 + 0.2222 F^12 + 0.1111 F^24" -``` - -``` r def.par <- par(no.readonly = TRUE) par(mai = c(0.5, 0.8, 0.3, 0)) @@ -313,9 +314,6 @@ musgrave #> t+4 0.000000000 0.00000000 #> t+5 0.000000000 0.00000000 #> t+6 0.000000000 0.00000000 -``` - -``` r musgrave * M3X3 #> q=6 q=5 q=4 q=3 q=2 #> t-8 -0.002149983 -0.001845449 -0.001291520 -0.0010169359 -0.001793248 From 63830095b64ae9157bfe70b7eacdd305d88b95b6 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Tue, 9 Jul 2024 19:57:45 +0200 Subject: [PATCH 25/27] lintr with vector logic --- R/2_finite_filters.R | 6 +++--- R/plots.R | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/2_finite_filters.R b/R/2_finite_filters.R index 96464be..8ffe2cb 100644 --- a/R/2_finite_filters.R +++ b/R/2_finite_filters.R @@ -34,17 +34,17 @@ finite_filters.moving_average <- function(sfilter, rfilters = NULL, lfilters = NULL, first_to_last = FALSE){ - if (is.null(lfilters) & !is.null(rfilters)) { + if (is.null(lfilters) && !is.null(rfilters)) { if (first_to_last) { rfilters <- rev(rfilters) } lfilters <- rev(lapply(rfilters, rev.moving_average)) - } else if (!is.null(lfilters) & is.null(rfilters)) { + } else if (!is.null(lfilters) && is.null(rfilters)) { if (!first_to_last) { lfilters <- rev(lfilters) } rfilters <- rev(lapply(lfilters, rev.moving_average)) - } else if (is.null(lfilters) & is.null(rfilters)) { + } else if (is.null(lfilters) && is.null(rfilters)) { rfilters <- lfilters <- list() } diff --git a/R/plots.R b/R/plots.R index 3def90e..1f5a36d 100644 --- a/R/plots.R +++ b/R/plots.R @@ -288,7 +288,7 @@ remove_bound_NA <- function(x) { j <- j + 1 } - if (is.null(remove_i_first) & is.null(remove_i_last)){ + if (is.null(remove_i_first) && is.null(remove_i_last)){ # list(data = x, leading = 0, # trailing = 0) } else { From 6c42fca71f02f66187aa2d766aa422cc8ca4b0c3 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Fri, 12 Jul 2024 10:59:36 +0200 Subject: [PATCH 26/27] add new jar --- NEWS.md | 1 + R/implicit_forecast.R | 2 +- ....jar => jdplus-filters-base-api-2.2.0.jar} | Bin 15693 -> 15684 bytes ...jar => jdplus-filters-base-core-2.2.0.jar} | Bin 68885 -> 68877 bytes ....0.jar => jdplus-filters-base-r-2.2.0.jar} | Bin 11449 -> 11440 bytes pom.xml | 2 +- 6 files changed, 3 insertions(+), 2 deletions(-) rename inst/java/{jdplus-filters-base-api-2.1.0.jar => jdplus-filters-base-api-2.2.0.jar} (76%) rename inst/java/{jdplus-filters-base-core-2.1.0.jar => jdplus-filters-base-core-2.2.0.jar} (86%) rename inst/java/{jdplus-filters-base-r-2.1.0.jar => jdplus-filters-base-r-2.2.0.jar} (74%) diff --git a/NEWS.md b/NEWS.md index e75b03c..90b57f4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -10,6 +10,7 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ### Changed * `cross_validation()` function renamed to `cve()`, `cross_validation()` is now deprecated. +* New .jar files added (related to [v2.2.0](https://github.com/jdemetra/jdplus-incubator/releases/tag/v2.2.0)) ### Added diff --git a/R/implicit_forecast.R b/R/implicit_forecast.R index 0c7a1ce..2ffb0d3 100644 --- a/R/implicit_forecast.R +++ b/R/implicit_forecast.R @@ -61,7 +61,7 @@ implicit_forecast.default <- function(x, coefs){ } #' @export implicit_forecast.matrix <- function(x, coefs){ - result <- do.call(cbind, lapply(seq_len(ncol(x)), function (i) implicit_forecast(x[,i], coefs = coefs))) + result <- do.call(cbind, lapply(seq_len(ncol(x)), function(i) implicit_forecast(x[,i], coefs = coefs))) colnames(result) <- colnames(x) result } diff --git a/inst/java/jdplus-filters-base-api-2.1.0.jar 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b/pom.xml @@ -11,7 +11,7 @@ Usage: mvn -Pcopy-jars - 2.1.0 + 2.2.0 From bcce59ddc574e3ef5884810f8b79c991d57c77c2 Mon Sep 17 00:00:00 2001 From: Tanguy BARTHELEMY Date: Fri, 12 Jul 2024 11:02:12 +0200 Subject: [PATCH 27/27] New release v2.1.1 --- DESCRIPTION | 4 ++-- NEWS.md | 6 +++++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 19973da..7cfecec 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,8 +30,8 @@ Imports: graphics, stats, rjd3toolkit (>= 3.2.2) -Remotes: - github::rjdverse/rjd3toolkit +Remotes: + github::rjdverse/rjd3toolkit@*release SystemRequirements: Java (>= 17) License: EUPL LazyData: TRUE diff --git a/NEWS.md b/NEWS.md index 90b57f4..e91a71d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,6 +7,9 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] +## [2.1.1] - 2024-07-12 + + ### Changed * `cross_validation()` function renamed to `cve()`, `cross_validation()` is now deprecated. @@ -37,6 +40,7 @@ to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). * New Jars -[Unreleased]: https://github.com/rjdverse/rjd3filters/compare/v2.0.0...HEAD +[Unreleased]: https://github.com/rjdverse/rjd3filters/compare/v2.1.1...HEAD +[2.1.1]: https://github.com/rjdverse/rjd3filters/releases/tag/v2.0.0...v2.1.1 [2.0.0]: https://github.com/rjdverse/rjd3filters/releases/tag/v1.0.0...v2.0.0 [1.0.0]: https://github.com/rjdverse/rjd3filters/releases/tag/v1.0.0