Plot posterior probability statements for IRFs or CDMs

Usage

plot_hypothesis(
  object,
  type = c("irf", "cdm"),
  variable = NULL,
  shock = NULL,
  horizon = NULL,
  relation = c("<", "<=", ">", ">=", "=="),
  value = 0,
  compare_to = NULL,
  absolute = FALSE,
  probability = 0.9,
  models = NULL,
  scale_by = c("none", "shock_sd"),
  scale_var = NULL,
  ...
)

Arguments

object: A hypothesis table, a magnitude-audit table, or an object accepted by hypothesis_irf() / hypothesis_cdm().
type: One of "irf" or "cdm" when object is not already a tidy posterior-statement table.
variable: Optional left-hand-side response variable selection.
shock: Optional left-hand-side shock selection.
horizon: Optional left-hand-side horizon selection.
relation: Comparison operator.
value: Scalar comparison value for threshold statements.
compare_to: Optional right-hand-side response specification.
absolute: If TRUE, compare absolute responses.
probability: Equal-tailed interval probability used for gap summaries.
models: Optional model filter.
scale_by: Optional scaling mode for CDMs.
scale_var: Optional scaling variable specification.
...: Additional arguments passed to computation methods.

Value

A ggplot object.

Examples

data(us_fiscal_lsuw, package = "bsvars")
spec <- bsvars::specify_bsvar$new(us_fiscal_lsuw, p = 1)
#> The identification is set to the default option of lower-triangular structural matrix.
post <- bsvars::estimate(spec, S = 5, show_progress = FALSE)

h <- hypothesis_irf(post, variable = "gdp", shock = "gdp",
                    horizon = 0:2, relation = ">", value = 0)
#> Warning: In hypothesis_irf(): 'variable' is deprecated and will be removed in a future version.
#> Use 'variables' instead.
#> Warning: In hypothesis_irf(): 'shock' is deprecated and will be removed in a future version.
#> Use 'shocks' instead.
p <- plot_hypothesis(h)