CausalQueries (version 1.3.2)

make_par_values: make_par_values

Description

This is the one step function for make_priors and make_parameters. See make_priors for more help.

Usage

make_par_values(
  model,
  alter = "priors",
  x = NA,
  alter_at = NA,
  node = NA,
  label = NA,
  nodal_type = NA,
  param_set = NA,
  given = NA,
  statement = NA,
  join_by = "|",
  param_names = NA,
  distribution = NA,
  normalize = FALSE
)

Arguments

model

model created with make_model

alter

character vector with one of "priors" or "param_value" specifying what to alter

x

vector of real non negative values to be substituted into "priors" or "param_value"

alter_at

string specifying filtering operations to be applied to parameters_df, yielding a logical vector indicating parameters for which values should be altered. (see examples)

node

string indicating nodes which are to be altered

label

string. Label for nodal type indicating nodal types for which values are to be altered. Equivalent to nodal_type.

nodal_type

string. Label for nodal type indicating nodal types for which values are to be altered

param_set

string indicating the name of the set of parameters to be altered

given

string indicates the node on which the parameter to be altered depends

statement

causal query that determines nodal types for which values are to be altered

join_by

string specifying the logical operator joining expanded types when statement contains wildcards. Can take values '&' (logical AND) or '|' (logical OR).

param_names

vector of strings. The name of specific parameter in the form of, for example, 'X.1', 'Y.01'

distribution

string indicating a common prior distribution (uniform, jeffreys or certainty)

normalize

logical. If TRUE normalizes such that param set probabilities sum to 1.

Examples

# the below methods can be applied to either priors or
# param_values by specifying the desired option in \code{alter}

model <- CausalQueries::make_model("X -> M -> Y; X <-> Y")

#altering values using \code{alter_at}
CausalQueries:::make_par_values(model = model,
                                x = c(0.5,0.25),
                                alter_at = paste(
                                  "node == 'Y' &",
                                  "nodal_type %in% c('00','01') &",
                                  "given == 'X.0'"))

#altering values using \code{param_names}
CausalQueries:::make_par_values(model = model,
                                x = c(0.5,0.25),
                                param_names = c("Y.10_X.0","Y.10_X.1"))

#altering values using \code{statement}
CausalQueries:::make_par_values(model = model,
                                x = c(0.5,0.25),
                                statement = "Y[M=1] > Y[M=0]")

#altering values using a combination of other arguments
CausalQueries:::make_par_values(model = model,
x = c(0.5,0.25), node = "Y", nodal_type = c("00","01"), given = "X.0")