plot_influence_diagram function

Plot influence diagram from a GrangerTest object

Arrows show causality (influencing) direction.

plot_influence_diagram(obj, splicing_df, two_arrows = TRUE, lev_sig = 0.05)

Arguments

• obj: GrangerTest object
• splicing_df: Splicing data.frame object
• two_arrows: plot influence arrows both ways? (Default is TRUE).
• lev_sig: significance level

Returns

ggplot object

Details

By default two_arrows is TRUE and an influencing arrow is drawn for each significant p-value. If two_arrows is FALSE and one of the p-values is signficant then -log10(p_value) difference is plotted i.e

Examples

r1 <- get_sample_recording()
fv_list <- get_filtered_views(r1, data_points = "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
splicing_df <- splice_time(jv_sub, win_size = 3, step_size = 0.5)
sv <- get_spliced_view(jv_sub, splicing_df)
g <- granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", lag = 3/25)

plot_influence_diagram(g, splicing_df)
plot_influence_diagram(g, splicing_df, two_arrows = TRUE)

d1 <- get_duration_annotation_data(r1)
plot_influence_diagram(g, splicing_df) +
autolayer(d1, expr = (Tier == "Influence S>T" | Tier == "Influence T>S") & Out <= 60,
          fill_col = "Tier")

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction()