Description Usage Arguments Details References Examples

Constructs a `dagitty`

graph object from a textual description.

1 |

`x` |
character, string describing a graphical model in dagitty syntax. |

`layout` |
logical, whether to automatically generate layout coordinates for each
variable (see |

The textual syntax for DAGitty graph is based on the dot language of the graphviz software (https://graphviz.gitlab.io/_pages/doc/info/lang.html). This is a fairly intuitive syntax – use the examples below and in the other functions to get you started. An important difference to graphviz is that the DAGitty language supports several types of graphs, which have different semantics. However, many users will mainly focus on DAGs.

A DAGitty graph description has the following form:

`[graph type] '{' [statements] '}'`

where `[graph type]`

is one of 'dag', 'mag', 'pdag', or 'pag' and `[statements]`

is a list of variables statements and edge statements, which may (optionally) be
separated by semicolons. Whitespace, including newlines, has no semantic role.

Variable statments look like

`[variable id] '[' [properties] ']'`

For example, the statement

`x [exposure,pos="1,0"]`

declares a variable with ID x that is an exposure variable and has a layout position of 1,0.

The edge statement

`x -> y`

declares a directed edge from variable x to variable y. Explicit variable statements are not required for the variables involved in edge statements, unless attributes such as position or exposure/outcome status need to be set.

DAGs (directed acyclic graphs) can contain the following edges: `->`

, `<->`

.
Bidirected edges in DAGs are simply shorthands for substructures `<- U ->`

,
where U is an unobserved variable.

MAGs (maximal ancestral graphs) can contain the following edges: `->`

,
`<->`

, `--`

.
The bidirected and directed edges of MAGs can represent latent confounders, and
the undirected edges represent latent selection variables.
For details, see Richardson and Spirtes (2002).

PDAGs (partially directed acyclic graphs) can contain the following edges: `->`

,
`<->`

, `--`

.
The bidirected edges mean the same thing as in DAGs. The undirected edges represent
edges whose direction is not known. Thus, PDAGs are used to represent equivalence
classes of DAGs (see also the function `equivalenceClass`

).

PAGs (partial ancestral graphs) are to MAGs what PDAGs are to DAGs: they represent
equivalence classes of MAGs. MAGs can contain the following edges: `@-@`

,
`->`

, `@->`

, `--`

, `@--`

(the @ symbols are written as circle marks in most of the literature). For
details on PAGs, see Zhang et al (2008). For now, only a few DAGitty functions
support PAGs (for instance, `adjustmentSets`

.

The DAGitty parser does not perform semantic validation. That is, it will not check whether a DAG is actually acyclic, or whether all chain components in a PAG are actually chordal. This is not done because it can be computationally rather expensive.

Richardson, Thomas; Spirtes, Peter (2002), Ancestral graph Markov models.
*The Annals of Statistics* 30(4): 962-1030.

J. Zhang (2008), Causal Reasoning with Ancestral Graphs.
*Journal of Machine Learning Research* 9: 1437-1474.

B. van der Zander and M. Liskiewicz (2016),
Separators and Adjustment Sets in Markov Equivalent DAGs.
In *Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16)*,
Phoenix, Arizona, USA.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
# Specify a simple DAG containing one path
g <- dagitty("dag{
a -> b ;
b -> c ;
d -> c
}")
# Newlines and semicolons are optional
g <- dagitty("dag{
a -> b b -> c c -> d
}")
# Paths can be specified in one go; the semicolon below is
# optional
g <- dagitty("dag{
a -> b ->c ; c -> d
}")
# Edges can be written in reverse notation
g <- dagitty("dag{
a -> b -> c <- d
}")
# Spaces are optional as well
g <- dagitty("dag{a->b->c<-d}")
# Variable attributes can be set in square brackets
# Example: DAG with one exposure, one outcome, and one unobserved variable
g <- dagitty("dag{
x -> y ; x <- z -> y
x [exposure]
y [outcome]
z [unobserved]
}")
# The same graph as above
g <- dagitty("dag{x[e]y[o]z[u]x<-z->y<-x}")
# A two-factor latent variable model
g <- dagitty("dag {
X <-> Y
X -> a X -> b X -> c X -> d
Y -> a Y -> b Y -> c Y -> d
}")
# Curly braces can be used to "group" variables and
# specify edges to whole groups of variables
# The same two-factor model
g <- dagitty("dag{ {X<->Y} -> {a b c d} }")
# A MAG
g <- dagitty("mag{ a -- x -> y <-> z }")
# A PDAG
g <- dagitty("pdag{ x -- y -- z }")
# A PAG
g <- dagitty("pag{ x @-@ y @-@ z }")
``` |

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