prior_setting: Setting priors

In CausalQueries: Make, Update, and Query Binary Causal Models

Setting priors

Description

Functionality for altering priors:

make_priors Generates priors for a model.

set_priors Adds priors to a model.

Extracts priors as a named vector

Usage

make_priors(
  model,
  alphas = NA,
  distribution = NA,
  alter_at = NA,
  node = NA,
  nodal_type = NA,
  label = NA,
  param_set = NA,
  given = NA,
  statement = NA,
  join_by = "|",
  param_names = NA
)

set_priors(
  model,
  alphas = NA,
  distribution = NA,
  alter_at = NA,
  node = NA,
  nodal_type = NA,
  label = NA,
  param_set = NA,
  given = NA,
  statement = NA,
  join_by = "|",
  param_names = NA
)

get_priors(model)

Arguments

model	A model object generated by make_model().
alphas	Real positive numbers giving hyperparameters of the Dirichlet distribution
distribution	string indicating a common prior distribution (uniform, jeffreys or certainty)
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
nodal_type	string. Label for nodal type indicating nodal types for which values are to be altered
label	string. Label for nodal type indicating nodal types for which values are to be altered. Equivalent to nodal_type.
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	string. The name of specific parameter in the form of, for example, 'X.1', 'Y.01'

Details

Seven arguments govern which parameters should be altered. The default is 'all' but this can be reduced by specifying

* alter_at String specifying filtering operations to be applied to parameters_df, yielding a logical vector indicating parameters for which values should be altered. "node == 'X' & nodal_type

* node, which restricts for example to parameters associated with node 'X'

* label or nodal_type The label of a particular nodal type, written either in the form Y0000 or Y.Y0000

* param_set The param_set of a parameter.

* given Given parameter set of a parameter.

* statement, which restricts for example to nodal types that satisfy the statement 'Y[X=1] > Y[X=0]'

* param_set, given, which are useful when setting confound statements that produces several sets of parameters

Two arguments govern what values to apply:

* alphas is one or more non negative numbers and

* distribution indicates one of a common class: uniform, jeffreys, or 'certain'

Forbidden statements include:

• Setting distribution and values at the same time.

• Setting a distribution other than uniform, jeffreys or certainty.

• Setting negative values.

• specifying alter_at with any of node, nodal_type,param_set,given,statement or param_names

• specifying param_names with any of node,nodal_type,param_set,given,statement or alter_at

• specifying statement with any of node or nodal_type

Value

A vector indicating the hyperparameters of the prior distribution of the nodal types.

An object of class causal_model. It essentially returns a list containing the elements comprising a model (e.g. 'statement', 'nodal_types' and 'DAG') with the 'priors' attached to it.

A vector indicating the hyperparameters of the prior distribution of the nodal types.

Examples

# make_priors examples:

# Pass all nodal types
model <- make_model("Y <- X")
make_priors(model, alphas = .4)
make_priors(model, distribution = "jeffreys")

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

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

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

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

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

# set_priors examples:

# Pass all nodal types
model <- make_model("Y <- X")
set_priors(model, alphas = .4)
set_priors(model, distribution = "jeffreys")

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

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

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

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

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

# get_priors examples:

get_priors(make_model('X -> Y'))

CausalQueries documentation built on Oct. 20, 2023, 1:06 a.m.