adjustment_sets: Covariate Adjustment Sets

In ggdag: Analyze and Create Elegant Directed Acyclic Graphs

Covariate Adjustment Sets

Description

See dagitty::adjustmentSets() for details.

Usage

dag_adjustment_sets(.tdy_dag, exposure = NULL, outcome = NULL, ...)

ggdag_adjustment_set(
  .tdy_dag,
  exposure = NULL,
  outcome = NULL,
  ...,
  shadow = FALSE,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  expand_x = expansion(c(0.25, 0.25)),
  expand_y = expansion(c(0.2, 0.2))
)

Arguments

.tdy_dag	input graph, an object of class tidy_dagitty or dagitty
exposure	a character vector, the exposure variable. Default is NULL, in which case it will be determined from the DAG.
outcome	a character vector, the outcome variable. Default is NULL, in which case it will be determined from the DAG.
...	additional arguments to adjustmentSets
shadow	logical. Show paths blocked by adjustment?
node_size	size of DAG node
text_size	size of DAG text
label_size	size of label text
text_col	color of DAG text
label_col	color of label text
node	logical. Should nodes be included in the DAG?
stylized	logical. Should DAG nodes be stylized? If so, use geom_dag_nodes and if not use geom_dag_point
text	logical. Should text be included in the DAG?
use_labels	a string. Variable to use for geom_dag_repel_label(). Default is NULL.
expand_x, expand_y	Vector of range expansion constants used to add some padding around the data, to ensure that they are placed some distance away from the axes. Use the convenience function ggplot2::expansion() to generate the values for the expand argument.

Value

a tidy_dagitty with an adjusted column and set column, indicating adjustment status and DAG ID, respectively, for the adjustment sets or a ggplot

Examples

dag <- dagify(y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2,
  exposure = "x",
  outcome = "y"
)

tidy_dagitty(dag) %>% dag_adjustment_sets()

ggdag_adjustment_set(dag)

ggdag_adjustment_set(dagitty::randomDAG(10, .5),
  exposure = "x3",
  outcome = "x5"
)

ggdag documentation built on May 31, 2023, 7:48 p.m.