make_model function

Make a model

make_model uses causal statements encoded as strings to specify the 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

make_model(statement = "X -> Y", add_causal_types = TRUE, nodal_types = NULL)

Arguments

• statement: character string. Statement describing causal relations between nodes. 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

Returns

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 parentand 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

Examples

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

# Example where cyclicaly dag attempted
## Not run:

 modelXKX <- make_model("X -> K -> X")
## End(Not run)

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

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

# Unconnected nodes not allowed
## Not run:

 model <- make_model("X <-> Y")
## End(Not run)

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) |>
 inspect("parameters_df")

nodal_types = list(Y = c("01", "10"), Z = c("0", "1"))
make_model("Z -> Y", nodal_types = nodal_types) |>
 inspect("parameters_df")

See Also

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