CausalQueries (version 1.1.0)

make_model: Make a model

Description

make_model uses dagitty syntax and functionality to specify nodes and edges of a graph. Implied causal types are calculated and default priors are provided under the assumption of no confounding. Models can be updated with specification of a parameter matrix, P, by providing restrictions on causal types, and/or by providing informative priors on parameters. The default setting for a causal model have flat (uniform) priors and parameters putting equal weight on each parameter within each parameter set. These can be adjust with set_priors and set_parameters

Usage

make_model(statement, add_causal_types = TRUE, nodal_types = NULL)

Value

An object of class causal_model.

An object of class "causal_model" is a list containing at least the following components:

statement

A character vector of the statement that defines the model

dag

A data.frame with columns `parent`and `children` indicating how nodes relate to each other.

nodes

A named list with the nodes in the model

parents_df

A data.frame listing nodes, whether they are root nodes or not, and the number of parents they have

nodal_types

Optional: A named list with the nodal types in the model. List should be ordered according to the causal ordering of nodes. If NULL nodal types are generated. If FALSE, a parameters data frame is not generated.

parameters_df

A data.frame with descriptive information of the parameters in the model

causal_types

A data.frame listing causal types and the nodal types that produce them

Arguments

statement

A character. Statement describing causal relations using dagitty syntax. Only directed relations are permitted. For instance "X -> Y" or "X1 -> Y <- X2; X1 -> X2".

add_causal_types

Logical. Whether to create and attach causal types to model. Defaults to `TRUE`.

nodal_types

List of nodal types associated with model nodes

See Also

summary.causal_model provides summary method for output objects of class causal_model

Examples

make_model(statement = "X -> Y")
modelXKY <- make_model("X -> K -> Y; X -> Y")

# Example where cyclicaly dag attempted
if (FALSE) {
 modelXKX <- make_model("X -> K -> X")
}

# Examples with confounding
model <- make_model("X->Y; X <-> Y")
model$P
model <- make_model("Y2 <- X -> Y1; X <-> Y1; X <-> Y2")
dim(model$P)
model$P
model <- make_model("X1 -> Y <- X2; X1 <-> Y; X2 <-> Y")
dim(model$P)
model$parameters_df

# A single node graph is also possible
model <- make_model("X")

# Unconnected nodes not allowed
if (FALSE) {
 model <- make_model("X <-> Y")
}

nodal_types <-
  list(
    A = c("0","1"),
    B = c("0","1"),
    C = c("0","1"),
    D = c("0","1"),
    E = c("0","1"),
    Y = c(
      "00000000000000000000000000000000",
      "01010101010101010101010101010101",
      "00110011001100110011001100110011",
      "00001111000011110000111100001111",
      "00000000111111110000000011111111",
      "00000000000000001111111111111111",
      "11111111111111111111111111111111" ))

make_model("A -> Y; B ->Y; C->Y; D->Y; E->Y",
          nodal_types = nodal_types)$parameters_df

nodal_types = list(Y = c("01", "10"), Z = c("0", "1"))
make_model("Z -> Y", nodal_types = nodal_types)$parameters_df
make_model("Z -> Y", nodal_types = FALSE)$parents_df