Draws the estimated causal structure as a ggplot2-based directed graph. Node
positions are computed with igraph's hierarchical layout (sugiyama), so the
causal flow is generally arranged from top to bottom. Because the output is a
static image, it is stable in RMarkdown / Quarto. If you need an interactive
HTML figure, use plot_adjacency() (DiagrammeR-based).
Usage
# S3 method for class 'LingamResult'
autoplot(
object,
threshold = 0,
node_size = 16,
node_color = "lightblue",
label_edges = TRUE,
label_pos = 0.35,
...
)Arguments
- object
Return value of
lingam_direct()(aLingamResultobject)- threshold
Coefficients with an absolute value at or below this are not treated as edges (default: 0)
- node_size
Node size (default: 16)
- node_color
Node fill color (default: "lightblue")
- label_edges
Whether to display coefficient labels on edges (default: TRUE)
- label_pos
Position of each coefficient label along its edge, as a fraction from the source (0) to the target (1). The default 0.35 places labels off-center (toward the source) so labels on crossing edges do not overlap near the midpoint.
- ...
Unused
Details
autoplot() is a ggplot2 generic, so you must load ggplot2 with
library(ggplot2) before using it. Plotting requires ggplot2 and igraph.
Examples
# \donttest{
if (requireNamespace("ggplot2", quietly = TRUE) &&
requireNamespace("igraph", quietly = TRUE)) {
library(ggplot2)
dat <- generate_lingam_sample_6()
model <- lingam_direct(dat$data, reg_method = "ols")
autoplot(model)
}
# }