diff --git a/NAMESPACE b/NAMESPACE index a75bb864..df756cd7 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,6 +15,7 @@ S3method(as_draws_rvars,mvgam) S3method(coef,mvgam) S3method(conditional_effects,mvgam) S3method(ensemble,mvgam_forecast) +S3method(fevd,mvgam) S3method(find_predictors,mvgam) S3method(find_predictors,mvgam_prefit) S3method(fitted,mvgam) @@ -41,6 +42,7 @@ S3method(nuts_params,mvgam) S3method(pairs,mvgam) S3method(plot,mvgam) S3method(plot,mvgam_conditional_effects) +S3method(plot,mvgam_fevd) S3method(plot,mvgam_forecast) S3method(plot,mvgam_irf) S3method(plot,mvgam_lfo) @@ -91,6 +93,7 @@ export(eval_mvgam) export(eval_smoothDothilbertDotsmooth) export(eval_smoothDotmodDotsmooth) export(eval_smoothDotmoiDotsmooth) +export(fevd) export(forecast) export(get_mvgam_priors) export(gp) @@ -180,6 +183,11 @@ importFrom(brms,set_prior) importFrom(brms,stancode) importFrom(brms,standata) importFrom(brms,student) +importFrom(ggplot2,aes) +importFrom(ggplot2,facet_wrap) +importFrom(ggplot2,geom_bar) +importFrom(ggplot2,ggplot) +importFrom(ggplot2,labs) importFrom(ggplot2,scale_colour_discrete) importFrom(ggplot2,scale_fill_discrete) importFrom(ggplot2,theme_classic) diff --git a/NEWS.md b/NEWS.md index 01c5dfb9..ebb36c5c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,7 @@ # mvgam 1.1.4 (development version; not yet on CRAN) ## New functionalities * Added a `stability.mvgam` method to compute stability metrics from models fit with Vector Autoregressive dynamics (#21) +* Added a `fevd.mvgam` method to compute forecast error variance decompositions from models fit with Vector Autoregressive dynamics ## Bug fixes * Fixed a minor bug in the way `trend_map` recognises levels of the `series` factor diff --git a/R/fevd.mvgam.R b/R/fevd.mvgam.R new file mode 100644 index 00000000..4dbdc259 --- /dev/null +++ b/R/fevd.mvgam.R @@ -0,0 +1,153 @@ +#' Calculate latent VAR forecast error variance decompositions +#' +#' Compute forecast error variance decompositions from +#' \code{mvgam} models with Vector Autoregressive dynamics +#' +#'@name fevd.mvgam +#'@param object \code{list} object of class \code{mvgam} resulting from a call to [mvgam()] +#'that used a Vector Autoregressive latent process model (either as `VAR(cor = FALSE)` or +#'`VAR(cor = TRUE)`) +#'@param h Positive \code{integer} specifying the forecast horizon over which to calculate +#'the IRF +#'@param ... ignored +#'@details A forecast error variance decomposition is useful for quantifying the amount +#'of information each series that in a Vector Autoregression contributes to the forecast +#'distributions of the other series in the autoregression. This function calculates +#'the forecast error variance decomposition using the +#'orthogonalised impulse response coefficient matrices \eqn{\Psi_h}, which can be used to +#'quantify the contribution of series \eqn{j} to the +#'h-step forecast error variance of series \eqn{k}: +#'\deqn{ +#'\sigma_k^2(h) = \sum_{j=1}^K(\psi_{kj, 0}^2 + \ldots + \psi_{kj, +#' h-1}^2) \quad +#' } +#' If the orthogonalised impulse reponses \eqn{(\psi_{kj, 0}^2 + \ldots + \psi_{kj, h-1}^2)} +#'are divided by the variance of the forecast error \eqn{\sigma_k^2(h)}, +#'this yields an interpretable percentage representing how much of the +#'forecast error variance for \eqn{k} can be explained by an exogenous shock to \eqn{j}. +#'@return An object of class \code{mvgam_fevd} containing the posterior forecast error +#'variance decompositions. This +#'object can be used with the supplied S3 functions \code{plot} +#'@author Nicholas J Clark +#'@references Lütkepohl, H (2006). +#'New Introduction to Multiple Time Series Analysis. Springer, New York. +#'@seealso \code{\link{VAR}}, \code{\link{plot.mvgam_irf}}, \code{\link{stability}} +#' @examples +#' \donttest{ +#' # Simulate some time series that follow a latent VAR(1) process +#' simdat <- sim_mvgam(family = gaussian(), +#' n_series = 4, +#' trend_model = VAR(cor = TRUE), +#' prop_trend = 1) +#' plot_mvgam_series(data = simdat$data_train, series = 'all') +#' +#' # Fit a model that uses a latent VAR(1) +#' mod <- mvgam(y ~ -1, +#' trend_formula = ~ 1, +#' trend_model = VAR(cor = TRUE), +#' family = gaussian(), +#' data = simdat$data_train, +#' silent = 2) +#' +#' # Calulate forecast error variance decompositions for each series +#' fevds <- fevd(mod, h = 12) +#' +#' # Plot median contributions to forecast error variance +#' plot(fevds) +#' } +#'@export +fevd <- function(object, ...){ + UseMethod("fevd", object) +} + +#'@rdname fevd.mvgam +#'@method fevd mvgam +#'@export +fevd.mvgam <- function(object, + h = 1, + ...){ + validate_pos_integer(h) + trend_model <- attr(object$model_data, 'trend_model') + if(!trend_model %in% c('VAR', 'VARcor', 'VAR1', 'VAR1cor')){ + stop('Only VAR(1) models currently supported for calculating IRFs', + call. = FALSE) + } + beta_vars <- mcmc_chains(object$model_output, 'A') + sigmas <- mcmc_chains(object$model_output, 'Sigma') + n_series <- object$n_lv + + if(is.null(n_series)){ + n_series <- nlevels(object$obs_data$series) + } + + all_fevds <- lapply(seq_len(NROW(beta_vars)), function(draw){ + + # Get necessary VAR parameters into a simple list format + x <- list(K = n_series, + A = matrix(beta_vars[draw,], + nrow = n_series, + ncol = n_series, + byrow = TRUE), + Sigma = matrix(sigmas[draw,], + nrow = n_series, + ncol = n_series, + byrow = TRUE), + p = 1) + + # Calculate the IRF + gen_fevd(x, h = h) + }) + class(all_fevds) <- 'mvgam_fevd' + return(all_fevds) +} + +#### Functions to compute forecast error variance decompositions +# Much of this code is modified from R code generously provided in the vars +# package https://github.com/cran/vars #### +#' Forecast error variance decomposition +#' @noRd +gen_fevd <- function(x, h = 6, ...){ + + K <- x$K + ynames <- paste0('process_', 1:K) + msey <- var_fecov(x, h = h) + Phi <- var_phi(x, h = h) + mse <- matrix(NA, nrow = h, ncol = K) + Omega <- array(0, dim = c(h, K, K)) + for(i in 1 : h){ + mse[i, ] <- diag(msey[, , i]) + temp <- matrix(0, K, K) + for(j in 1 : i){ + temp <- temp + Phi[ , , j]^2 + } + temp <- temp / mse[i, ] + for(j in 1 : K){ + Omega[i, ,j] <- temp[j, ] + } + } + result <- list() + for(i in 1 : K){ + result[[i]] <- matrix(Omega[, , i], nrow = h, ncol = K) + colnames(result[[i]]) <- ynames + } + names(result) <- ynames + return(result) + } + +#' Forecast error covariance matrix +#' @noRd +var_fecov = function(x, h) { + sigma_yh <- array(NA, dim = c(x$K, x$K, h)) + Phi <- var_phi(x, h = h) + sigma_yh[, , 1] <- Phi[, , 1] %*% t(Phi[, , 1]) + if (h > 1) { + for (i in 2:h) { + temp <- matrix(0, nrow = x$K, ncol = x$K) + for (j in 2:i) { + temp <- temp + Phi[, , j] %*% t(Phi[, , j]) + } + sigma_yh[, , i] <- temp + sigma_yh[, , 1] + } + } + return(sigma_yh) +} diff --git a/R/irf.mvgam.R b/R/irf.mvgam.R index 477fd643..f01c6d3e 100644 --- a/R/irf.mvgam.R +++ b/R/irf.mvgam.R @@ -217,22 +217,3 @@ var_psi = function(x, h=10){ } return(Psi) } - -#' Forecast error decomposition -#' @noRd -var_fecov = function(x, h) { - sigma_u <- x$Sigma - sigma_yh <- array(NA, dim = c(x$K, x$K, h)) - sigma_yh[, , 1] <- sigma_u - Phi <- var_phi(x, h = h) - if (h > 1) { - for (i in 2:h) { - temp <- matrix(0, nrow = x$K, ncol = x$K) - for (j in 2:i) { - temp <- temp + Phi[, , j] %*% sigma_u %*% t(Phi[, , j]) - } - sigma_yh[, , i] <- temp + sigma_yh[, , 1] - } - } - return(sigma_yh) -} diff --git a/R/mvgam_fevd-class.R b/R/mvgam_fevd-class.R new file mode 100644 index 00000000..517b8148 --- /dev/null +++ b/R/mvgam_fevd-class.R @@ -0,0 +1,62 @@ +#' `mvgam_fevd` object description +#' +#' A \code{mvgam_fevd} object returned by function \code{\link{fevd}}. +#' Run `methods(class = "mvgam_fevd")` to see an overview of available methods. +#' @details A `mvgam_fevd` object contains a list of posterior forecast +#' error variance decompositions, each stored as +#' its own list +#' @seealso [mvgam], [VAR] +#' @author Nicholas J Clark +#' @name mvgam_fevd-class +NULL + +#'Plot forecast error variance decompositions from an mvgam_fevd object + +#'This function takes an \code{mvgam_fevd} object and produces +#'a plot of the posterior median contributions to forecast variance for each series +#'in the fitted Vector Autoregression +#' +#'@importFrom ggplot2 ggplot aes geom_bar facet_wrap labs +#'@param x \code{list} object of class \code{mvgam_fevd}. See [fevd()] +#'@param ... ignored +#'@return A \code{\link[ggplot2]{ggplot}} object, +#'which can be further customized using the \pkg{ggplot2} package +#'@author Nicholas J Clark +#'@export +plot.mvgam_fevd = function(x, ...){ + + ynames <- names(x[[1]]) + fevd_df = function(x){ + do.call(rbind, lapply(seq_len(length(x)), function(process){ + data.frame(horizon = 1:NROW(x[[process]]), + evd = as.vector(x[[process]]), + Series = sort(rep(colnames(x[[process]]), + NROW(x[[process]]))), + target = ynames[process]) + })) + } + + # Calculate posterior median error variance contributions + fevd_dfs <- do.call(rbind, lapply(seq_len(length(x)), + function(draw){ + fevd_df(x[[draw]]) + })) %>% + dplyr::group_by(horizon, target, Series) %>% + dplyr::summarise(mean_evd = median(evd)) %>% + dplyr::ungroup() %>% + dplyr::group_by(horizon, target) %>% + dplyr::mutate(total_evd = sum(mean_evd), + mean_evd = mean_evd / total_evd) %>% + dplyr::ungroup() -> mean_evds + + # Plot as a ggplot object + ggplot2::ggplot(mean_evds, + ggplot2::aes(fill = Series, + y = mean_evd, + x = horizon)) + + ggplot2::geom_bar(position = "stack", stat = "identity") + + ggplot2::facet_wrap(~target) + + ggplot2::labs(x = 'Forecast horizon', + y = 'Median contribution to forecast variance') + +} diff --git a/docs/news/index.html b/docs/news/index.html index 32707aee..6dcb4a88 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -61,6 +61,7 @@

New functionalities

Bug fixes

diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png index 17a35806..fa3dfa5c 100644 Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index 9023c14e..2f97086d 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/fevd.mvgam-1.png b/docs/reference/fevd.mvgam-1.png new file mode 100644 index 00000000..0f799a65 Binary files /dev/null and b/docs/reference/fevd.mvgam-1.png differ diff --git a/docs/reference/fevd.mvgam-2.png b/docs/reference/fevd.mvgam-2.png new file mode 100644 index 00000000..570c2f5c Binary files /dev/null and b/docs/reference/fevd.mvgam-2.png differ diff --git a/docs/reference/fevd.mvgam.html b/docs/reference/fevd.mvgam.html new file mode 100644 index 00000000..cb3575e0 --- /dev/null +++ b/docs/reference/fevd.mvgam.html @@ -0,0 +1,194 @@ + +Calculate latent VAR forecast error variance decompositions — fevd.mvgam • mvgam + Skip to contents + + +
+
+
+

Calculate latent VAR forecast error variance decompositions

+ Source: R/fevd.mvgam.R +
fevd.mvgam.Rd
+
+ +
+

Compute forecast error variance decompositions from +mvgam models with Vector Autoregressive dynamics

+
+ +
+

Usage

+ 
fevd(object, ...)
+
+# S3 method for mvgam
+fevd(object, h = 1, ...)
+
+ +
+

Arguments

+
object
+

list object of class mvgam resulting from a call to mvgam() +that used a Vector Autoregressive latent process model (either as VAR(cor = FALSE) or +VAR(cor = TRUE))

+ + +
...
+

ignored

+ + +
h
+

Positive integer specifying the forecast horizon over which to calculate +the IRF

+ +
+
+

Value

+ + +

An object of class mvgam_fevd containing the posterior forecast error +variance decompositions. This +object can be used with the supplied S3 functions plot

+ + +
+
+

Details

+

A forecast error variance decomposition is useful for quantifying the amount +of information each series that in a Vector Autoregression contributes to the forecast +distributions of the other series in the autoregression. This function calculates +the forecast error variance decomposition using the +orthogonalised impulse response coefficient matrices \(\Psi_h\), which can be used to +quantify the contribution of series \(j\) to the +h-step forecast error variance of series \(k\): +$$ +\sigma_k^2(h) = \sum_{j=1}^K(\psi_{kj, 0}^2 + \ldots + \psi_{kj, +h-1}^2) \quad +$$ +If the orthogonalised impulse reponses \((\psi_{kj, 0}^2 + \ldots + \psi_{kj, h-1}^2)\) +are divided by the variance of the forecast error \(\sigma_k^2(h)\), +this yields an interpretable percentage representing how much of the +forecast error variance for \(k\) can be explained by an exogenous shock to \(j\).

+
+
+

References

+

Lütkepohl, H (2006). +New Introduction to Multiple Time Series Analysis. Springer, New York.

+
+
+

See also

+

VAR, plot.mvgam_irf, stability

+
+
+

Author

+

Nicholas J Clark

+
+ +
+

Examples

+ 
# \donttest{
+# Simulate some time series that follow a latent VAR(1) process
+simdat <- sim_mvgam(family = gaussian(),
+                    n_series = 4,
+                    trend_model = VAR(cor = TRUE),
+                    prop_trend = 1)
+plot_mvgam_series(data = simdat$data_train, series = 'all')
+
+
+# Fit a model that uses a latent VAR(1)
+mod <- mvgam(y ~ -1,
+             trend_formula = ~ 1,
+             trend_model = VAR(cor = TRUE),
+             family = gaussian(),
+             data = simdat$data_train,
+             silent = 2)
+#> In file included from stan/lib/stan_math/stan/math/prim/prob/von_mises_lccdf.hpp:5,
+#>                  from stan/lib/stan_math/stan/math/prim/prob/von_mises_ccdf_log.hpp:4,
+#>                  from stan/lib/stan_math/stan/math/prim/prob.hpp:359,
+#>                  from stan/lib/stan_math/stan/math/prim.hpp:16,
+#>                  from stan/lib/stan_math/stan/math/rev.hpp:16,
+#>                  from stan/lib/stan_math/stan/math.hpp:19,
+#>                  from stan/src/stan/model/model_header.hpp:4,
+#>                  from C:/Users/uqnclar2/AppData/Local/Temp/Rtmpm6CzOV/model-6f604d042ef7.hpp:2:
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp: In function 'stan::return_type_t<T_x, T_sigma, T_l> stan::math::von_mises_cdf(const T_x&, const T_mu&, const T_k&)':
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: '-Wmisleading-indentation' is disabled from this point onwards, since column-tracking was disabled due to the size of the code/headers
+#>   194 |       if (cdf_n < 0.0)
+#>       | 
+#> stan/lib/stan_math/stan/math/prim/prob/von_mises_cdf.hpp:194: note: adding '-flarge-source-files' will allow for more column-tracking support, at the expense of compilation time and memory
+
+# Calulate forecast error variance decompositions for each series
+fevds <- fevd(mod, h = 12)
+
+# Plot them
+plot(fevds)
+
+# }
+
+
+
+ + +
+ + + + + + + diff --git a/docs/reference/index.html b/docs/reference/index.html index 14e614da..7c137436 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -103,6 +103,11 @@

All functionsfevd() + +
Calculate latent VAR forecast error variance decompositions
+
+ fitted(<mvgam>)
Expected Values of the Posterior Predictive Distribution
@@ -208,6 +213,11 @@

All functionsmvgam_fevd-class + +
mvgam_fevd object description
+

+ mvgam_forecast-class
mvgam_forecast object description
@@ -248,6 +258,14 @@

All functionsplot(<mvgam_fevd>) + +
Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an mvgam_fevd object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression
+

+ plot(<mvgam_irf>)
Plot impulse responses from an mvgam_irf object diff --git a/docs/reference/mvgam_fevd-class.html b/docs/reference/mvgam_fevd-class.html new file mode 100644 index 00000000..b956ec59 --- /dev/null +++ b/docs/reference/mvgam_fevd-class.html @@ -0,0 +1,102 @@ + +mvgam_fevd object description — mvgam_fevd-class • mvgam + Skip to contents + + +
+
+
+

mvgam_fevd object description

+ Source: R/mvgam_fevd-class.R +
mvgam_fevd-class.Rd
+
+ +
+

A mvgam_fevd object returned by function fevd. +Run methods(class = "mvgam_fevd") to see an overview of available methods.

+
+ + +
+

Details

+

A mvgam_fevd object contains a list of posterior forecast +error variance decompositions, each stored as +its own list

+
+
+

See also

+

mvgam, VAR

+
+
+

Author

+

Nicholas J Clark

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/plot.mvgam_fevd.html b/docs/reference/plot.mvgam_fevd.html new file mode 100644 index 00000000..b5549e85 --- /dev/null +++ b/docs/reference/plot.mvgam_fevd.html @@ -0,0 +1,129 @@ + +Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an mvgam_fevd object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression — plot.mvgam_fevd • mvgam + Skip to contents + + +
+
+
+

Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an mvgam_fevd object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression

+ Source: R/mvgam_fevd-class.R +
plot.mvgam_fevd.Rd
+
+ +
+

Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an mvgam_fevd object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression

+
+ +
+

Usage

+ 
# S3 method for mvgam_fevd
+plot(x, ..)
+
+ +
+

Arguments

+
x
+

list object of class mvgam_fevd. See fevd()

+ + +
...
+

ignored

+ +
+
+

Value

+ + +

A ggplot object, +which can be further customized using the ggplot2 package

+
+
+

Author

+

Nicholas J Clark

+
+ +
+ + +
+ + + + + + + diff --git a/man/fevd.mvgam.Rd b/man/fevd.mvgam.Rd new file mode 100644 index 00000000..f39e47df --- /dev/null +++ b/man/fevd.mvgam.Rd @@ -0,0 +1,81 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/fevd.mvgam.R +\name{fevd.mvgam} +\alias{fevd.mvgam} +\alias{fevd} +\title{Calculate latent VAR forecast error variance decompositions} +\usage{ +fevd(object, ...) + +\method{fevd}{mvgam}(object, h = 1, ...) +} +\arguments{ +\item{object}{\code{list} object of class \code{mvgam} resulting from a call to \code{\link[=mvgam]{mvgam()}} +that used a Vector Autoregressive latent process model (either as \code{VAR(cor = FALSE)} or +\code{VAR(cor = TRUE)})} + +\item{...}{ignored} + +\item{h}{Positive \code{integer} specifying the forecast horizon over which to calculate +the IRF} +} +\value{ +An object of class \code{mvgam_fevd} containing the posterior forecast error +variance decompositions. This +object can be used with the supplied S3 functions \code{plot} +} +\description{ +Compute forecast error variance decompositions from +\code{mvgam} models with Vector Autoregressive dynamics +} +\details{ +A forecast error variance decomposition is useful for quantifying the amount +of information each series that in a Vector Autoregression contributes to the forecast +distributions of the other series in the autoregression. This function calculates +the forecast error variance decomposition using the +orthogonalised impulse response coefficient matrices \eqn{\Psi_h}, which can be used to +quantify the contribution of series \eqn{j} to the +h-step forecast error variance of series \eqn{k}: +\deqn{ +\sigma_k^2(h) = \sum_{j=1}^K(\psi_{kj, 0}^2 + \ldots + \psi_{kj, +h-1}^2) \quad +} +If the orthogonalised impulse reponses \eqn{(\psi_{kj, 0}^2 + \ldots + \psi_{kj, h-1}^2)} +are divided by the variance of the forecast error \eqn{\sigma_k^2(h)}, +this yields an interpretable percentage representing how much of the +forecast error variance for \eqn{k} can be explained by an exogenous shock to \eqn{j}. +} +\examples{ +\donttest{ +# Simulate some time series that follow a latent VAR(1) process +simdat <- sim_mvgam(family = gaussian(), + n_series = 4, + trend_model = VAR(cor = TRUE), + prop_trend = 1) +plot_mvgam_series(data = simdat$data_train, series = 'all') + +# Fit a model that uses a latent VAR(1) +mod <- mvgam(y ~ -1, + trend_formula = ~ 1, + trend_model = VAR(cor = TRUE), + family = gaussian(), + data = simdat$data_train, + silent = 2) + +# Calulate forecast error variance decompositions for each series +fevds <- fevd(mod, h = 12) + +# Plot them +plot(fevds) +} +} +\references{ +Lütkepohl, H (2006). +New Introduction to Multiple Time Series Analysis. Springer, New York. +} +\seealso{ +\code{\link{VAR}}, \code{\link{plot.mvgam_irf}}, \code{\link{stability}} +} +\author{ +Nicholas J Clark +} diff --git a/man/mvgam_fevd-class.Rd b/man/mvgam_fevd-class.Rd new file mode 100644 index 00000000..c16cd329 --- /dev/null +++ b/man/mvgam_fevd-class.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mvgam_fevd-class.R +\name{mvgam_fevd-class} +\alias{mvgam_fevd-class} +\title{\code{mvgam_fevd} object description} +\description{ +A \code{mvgam_fevd} object returned by function \code{\link{fevd}}. +Run \code{methods(class = "mvgam_fevd")} to see an overview of available methods. +} +\details{ +A \code{mvgam_fevd} object contains a list of posterior forecast +error variance decompositions, each stored as +its own list +} +\seealso{ +\link{mvgam}, \link{VAR} +} +\author{ +Nicholas J Clark +} diff --git a/man/plot.mvgam_fevd.Rd b/man/plot.mvgam_fevd.Rd new file mode 100644 index 00000000..69ffd5fd --- /dev/null +++ b/man/plot.mvgam_fevd.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mvgam_fevd-class.R +\name{plot.mvgam_fevd} +\alias{plot.mvgam_fevd} +\title{Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an \code{mvgam_fevd} object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression} +\usage{ +\method{plot}{mvgam_fevd}(x, ..) +} +\arguments{ +\item{x}{\code{list} object of class \code{mvgam_fevd}. See \code{\link[=fevd]{fevd()}}} + +\item{...}{ignored} +} +\value{ +A \code{\link[ggplot2]{ggplot}} object, +which can be further customized using the \pkg{ggplot2} package +} +\description{ +Plot forecast error variance decompositions from an mvgam_fevd object +This function takes an \code{mvgam_fevd} object and produces +a plot of the posterior median contributions to forecast variance for each series +in the fitted Vector Autoregression +} +\author{ +Nicholas J Clark +} diff --git a/misc/mvgam_cheatsheet.png b/misc/mvgam_cheatsheet.png index f3c4f92a..8ed6ec21 100644 Binary files a/misc/mvgam_cheatsheet.png and b/misc/mvgam_cheatsheet.png differ diff --git a/src/RcppExports.o b/src/RcppExports.o index 03b01119..e8dd19f0 100644 Binary files a/src/RcppExports.o and b/src/RcppExports.o differ diff --git a/src/mvgam.dll b/src/mvgam.dll index 357430f9..4be42be1 100644 Binary files a/src/mvgam.dll and b/src/mvgam.dll differ diff --git a/src/trend_funs.o b/src/trend_funs.o index 1c0d5a8f..917270cf 100644 Binary files a/src/trend_funs.o and b/src/trend_funs.o differ diff --git a/tests/testthat/test-example_processing.R b/tests/testthat/test-example_processing.R index 39167c7b..88143e78 100644 --- a/tests/testthat/test-example_processing.R +++ b/tests/testthat/test-example_processing.R @@ -57,6 +57,13 @@ test_that("irf() gives correct outputs", { expect_no_error(plot(irfs)) }) +test_that("fevd() gives correct outputs", { + fevds <- fevd(mvgam:::mvgam_example3, h = 12) + expect_true(length(fevds) == 30) + expect_true(NROW(fevds[[1]]$process_1) == 12) + expect_no_error(plot(fevds)) +}) + test_that("variable extraction works correctly", { expect_true(inherits(as.matrix(mvgam:::mvgam_example4, 'A', regex = TRUE),