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Compute Network-Level Impulse Response Functions

Source: R/irf.R
compute_irf.Rd

Computes IRFs for network-level statistics given a shock.

Important: ggplot2 exports its own stat_density function, which masks dbn::stat_density when both packages are loaded. If you use ggplot2, pass the statistic explicitly: stat_fun = dbn::stat_density.

Usage

compute_irf(
  fit,
  shock,
  H = 20,
  t0 = 1,
  stat_fun = stat_density,
  n_draws = NULL,
  shock_pars = list(),
  ...
)

Arguments

fit

A dbn model fit object

shock

Shock matrix or shock type (see build_shock)

H

Number of horizons to compute

t0

Shock time for dynamic models

stat_fun

Network statistic function (default: stat_density). Must accept a matrix and return a scalar. When ggplot2 is loaded, use dbn::stat_density explicitly to avoid name masking.

n_draws

Number of posterior draws to use

shock_pars

List of parameters for build_shock if shock is a character string

...

Additional arguments

Value

Data frame with IRF results including posterior summaries

See also

build_shock, plot.dbn_irf, print.dbn_irf, debug_irf, stat_density

Examples

# \donttest{
sim <- simulate_dynamic_dbn(n = 6, time = 10, seed = 1)
fit <- dbn(sim$Y, model = "dynamic", nscan = 200, burn = 100, verbose = FALSE)
S <- build_shock(m = 6, type = "unit_edge", i = 1, j = 2)
irf <- compute_irf(fit, shock = S, H = 5, stat_fun = dbn::stat_density)
print(irf)
#> Network Impulse Response Function
#> Model: dynamic 
#> Statistic: custom_function 
#> Shock time: 1 
#> 
#> Summary:
#>   horizon   mean median    sd   q025  q975    q10   q90
#> 1       0  0.033  0.033 0.000  0.033 0.033  0.033 0.033
#> 2       1  0.002  0.001 0.036 -0.065 0.096 -0.042 0.043
#> 3       2 -0.003  0.000 0.056 -0.096 0.096 -0.055 0.041
#> 4       3 -0.005 -0.003 0.057 -0.106 0.088 -0.057 0.051
#> 5       4 -0.014 -0.001 0.061 -0.192 0.075 -0.086 0.038
#> 6       5  0.011  0.000 0.097 -0.128 0.237 -0.050 0.064
# }