plot_model: Plots a DAG in ggplot style using a causal model input

In CausalQueries: Make, Update, and Query Binary Causal Models

Plots a DAG in ggplot style using a causal model input

Description

If confounds are indicated (provided in attr(model$P, 'confounds')), then these are represented as bidirectional arcs. Builds on functionality from ggdag and dagitty.

Usage

plot_model(
  model = NULL,
  x_coord = NULL,
  y_coord = NULL,
  title = "",
  textcol = "white",
  textsize = 3.88,
  shape = 16,
  nodecol = "black",
  nodesize = 16
)

Arguments

model	A causal_model object generated from make_model
x_coord	A vector of x coordinates for DAG nodes. If left empty, coordinates are randomly generated
y_coord	A vector of y coordinates for DAG nodes. If left empty, coordinates are randomly generated
title	String specifying title of graph
textcol	String specifying color of text labels
textsize	Numeric, size of text labels
shape	Indicates shape of node. Defaults to circular node.
nodecol	String indicating color of node that is accepted by ggplot's default palette
nodesize	Size of node.

Value

A DAG plot in ggplot style.

Examples

## Not run: 
model <- make_model('X -> K -> Y; X <-> Y')

model |>
  CausalQueries:::plot_model()
model |>
  CausalQueries:::plot_model(
    x_coord = 1:3,
    y_coord = 1:3,
    title = "Mixed text and math: $\\alpha^2 + \\Gamma$")

## End(Not run)

CausalQueries documentation built on June 22, 2024, 6:50 p.m.