| from_tna | R Documentation |
Extracts the transition matrix, labels, and initial state probabilities
from a tna object and plots with cograph. Initial probabilities
are mapped to donut fills.
from_tna(
tna_object,
engine = c("splot", "soplot"),
plot = TRUE,
weight_digits = 2,
show_zero_edges = FALSE,
...
)
tna_object |
A |
engine |
Which cograph renderer to use: |
plot |
Logical. If TRUE (default), immediately plot using the chosen engine. |
weight_digits |
Number of decimal places to round edge weights to. Default 2.
Edges that round to zero are removed unless |
show_zero_edges |
Logical. If TRUE, keep edges even if their weight rounds to zero. Default: FALSE. |
... |
Additional parameters passed to the plotting engine (e.g., |
The tna object's transition matrix becomes edge weights, labels become
node labels, and initial state probabilities (inits) are mapped to
donut_fill values to visualize starting state distributions.
TNA networks are always treated as directed because transition matrices represent directional state changes.
The default donut_inner_ratio of 0.8 creates thin rings that
effectively visualize probability values without obscuring node labels.
The following tna properties are automatically extracted:
weights: Transition matrix -> edge weights
labels: State labels -> node labels
inits: Initial probabilities -> donut_fill (0-1 scale)
The following visual defaults are applied for TNA plots (all can be overridden via ...):
layout = "oval": Oval/elliptical node arrangement
node_fill: Colors from TNA palette (Accent/Set3 based on state count)
node_size = 7: Larger nodes for readability
arrow_size = 0.61: Prominent directional arrows
edge_color = "#003355": Dark blue edges
edge_labels = TRUE: Show transition weights on edges
edge_label_size = 0.6: Readable edge labels
edge_label_position = 0.7: Labels positioned toward target
edge_start_style = "dotted": Dotted line at edge source
edge_start_length = 0.2: 20% of edge is dotted
Invisibly, a named list of cograph parameters that can be passed to
splot() or soplot().
cograph for creating networks from scratch,
splot and soplot for plotting engines,
from_qgraph for qgraph object conversion
# Convert and plot a tna object
model <- tna::tna(tna::group_regulation)
from_tna(model) # Plots with donut rings showing initial probabilities
# Use soplot engine instead
from_tna(model, engine = "soplot")
# Customize the visualization
from_tna(model, layout = "circle", donut_color = c("steelblue", "gray90"))
# Extract parameters without plotting
params <- from_tna(model, plot = FALSE)
# Modify and plot manually
params$node_fill <- "coral"
do.call(splot, params)
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