rjdqa (version 0.1.5)

simple_dashboard: 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,
  digits = 2,
  scale_var_decomp = FALSE,
  remove_others_contrib = FALSE
)
simple_dashboard2(
  x,
  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")
)

Arguments

x

a seasonal adjustment model made by 'RJDemetra' (object of class "SA").

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.

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.

See Also

plot.sc_dashboard.

Examples

data <- window(RJDemetra::ipi_c_eu[, "FR"], start = 2003)
sa_model <- RJDemetra::jx13(data, "RSA5c")
dashboard_data <- simple_dashboard(sa_model)
plot(dashboard_data, main = "Simple dashboard IPI - FR")
dashboard_data2 <- simple_dashboard2(sa_model)
plot(dashboard_data2, main = "Simple dashboard with outliers IPI - FR")