Package 'rjd3report'

Plot a simple seasonal adjustment dashboard

Description

Function to plot a simple dashboard of a seasonal adjustment model.

Usage

## S3 method for class 'simple_dashboard'
plot(
  x,
  main = "Simple Dashboard with outliers",
  subtitle = NULL,
  color_series = c(y = "#F0B400", t = "#1E6C0B", sa = "#155692"),
  reference_date = TRUE,
  ...
)

Arguments

x	A simple_dashboard object.
main	Main title.
subtitle	Subtitle.
color_series	Color of the raw time series, the trend and the seasonally adjusted component.
reference_date	Boolean indicating if the reference date should be printed.
...	Other unused parameters.

See Also

simple_dashboard.

Examples

data <- window(rjd3toolkit::ABS$X0.2.09.10.M, start = 2003)
sa_model <- rjd3x13::x13(data)
dashboard_data <- simple_dashboard(sa_model)
plot(dashboard_data, main = "Simple dashboard")
dashboard_data2 <- simple_dashboard2(sa_model)
plot(dashboard_data2, main = "Simple dashboard with outliers")

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Get SARIMA Orders

Description

sarima_orders() returns the SARIMA orders as a list while sarima_orders_ch() returns a string.

Usage

sarima_orders(x, ...)

sarima_orders_ch(x, ...)

Arguments

x	The model.
...	Other unused parameters.

Examples

y <- rjd3toolkit::ABS$X0.2.09.10.M
mod <- rjd3toolkit::sarima_estimate(y, order = c(0,1,1), seasonal = c(0,1,1))
sarima_orders(mod)
sarima_orders_ch(mod)
mod_x13 <- rjd3x13::x13(y)
sarima_orders_ch(mod_x13)

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Compute data for a simple seasonal adjustment

Description

Functions to compute the data to produce a simple seasonal adjustment dashboard. simple_dashboard2() is a slightly variation of simple_dashboard() with smaller description text to include a table with last outliers.

Usage

simple_dashboard(
  x,
  context = NULL,
  digits = 2,
  scale_var_decomp = FALSE,
  remove_others_contrib = FALSE,
  add_obs_to_forecast = TRUE,
  td_effect = NULL
)

simple_dashboard2(
  x,
  context = NULL,
  digits = 2,
  scale_var_decomp = FALSE,
  remove_others_contrib = FALSE,
  digits_outliers = digits,
  columns_outliers = c("Estimate", "T-stat"),
  n_last_outliers = 4,
  order_outliers = c("AO", "LS", "TC", "SO"),
  add_obs_to_forecast = TRUE,
  td_effect = NULL
)

Arguments

x	A seasonal adjustment model.
context	Context used to estimate the model.
digits	Number of digits used in the tables.
scale_var_decomp	boolean indicating if the variance decomposition table should be rescaled to 100.
remove_others_contrib	boolean indication if the "Others" contribution (i.e.: the pre-adjustment contribution) should be removed from the variance decomposition table.
add_obs_to_forecast	Boolean indicating if the last observed values should be added to the forecast table (for the plot).
td_effect	Boolean indicating if the residual trading days effect test should be printed. By default (td_effect = NULL) the test is only printed for monthly series.
digits_outliers	number of digits used in the table of outliers.
columns_outliers	informations about outliers that should be printed in the summary table. Can be either a vector of characters among c("Estimate", "Std. Error", "T-stat", "Pr(>|t|)"); or an vector of integer: 1 corresponding to the estimate coefficient ("Estimate"), 2 corresponding to the standard deviation error ("Std. Error"), 3 corresponding to the t-statistic ("T-stat") or 4 corresponding to the p-value ("Pr(>|t|)"). By default only the estimate coefficients and the t-statistics are printed (columns_outliers = c("Estimate", "T-stat")).
n_last_outliers	number of last outliers to be printed (by default n_last_outliers = 4).
order_outliers	order of the outliers in case of several outliers at the same date.

Examples

data <- window(rjd3toolkit::ABS$X0.2.09.10.M, start = 2003)
sa_model <- rjd3x13::x13(data)
dashboard_data <- simple_dashboard(sa_model)
plot(dashboard_data, main = "Simple dashboard")
dashboard_data2 <- simple_dashboard2(sa_model)
plot(dashboard_data2, main = "Simple dashboard with outliers")

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