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Sensitivity function

Source: R/sensitivity.R
sensitivity.Rd

sensitivity returns the sensitivity function for a probabilistic query of interest with respect to a parameter change defined by the user.

Usage

sensitivity(
  bnfit,
  interest_node,
  interest_node_value,
  evidence_nodes = NULL,
  evidence_states = NULL,
  node,
  value_node,
  value_parents,
  new_value,
  covariation = "proportional"
)

Arguments

bnfit

object of class bn.fit.

interest_node

character string. Node of the probability query of interest.

interest_node_value

character string. Level of interest_node.

evidence_nodes

character string. Evidence nodes. If NULL no evidence is considered. Set by default to NULL.

evidence_states

character string. Levels of evidence_nodes. If NULL no evidence is considered. If evidence_nodes="NULL", evidence_states should be set to NULL. Set by default to NULL.

node

character string. Node of which the conditional probability distribution is being changed.

value_node

character string. Level of node.

value_parents

character string. Levels of node's parents. The levels should be defined according to the order of the parents in bnfit[[node]][["parents"]]. If node has no parents, then should be set to NULL.

new_value

numeric vector with elements between 0 and 1. Values to which the parameter should be updated. It can take a specific value or more than one. For more than one value, these should be defined through a vector with an increasing order of the elements. new_value can also take as value the character string all: in this case a sequence of possible parameter changes ranging from 0.05 to 0.95 is considered.

covariation

character string. Co-variation scheme to be used for the updated Bayesian network. Can take values uniform, proportional, orderp, all. If equal to all, uniform, proportional and order-preserving co-variation schemes are considered. Set by default to proportional.

Value

A dataframe with the varied parameter values and the output probabilities for the co-variation schemes selected. If plot = TRUE the function also returns a plot of the sensitivity function.

Details

The Bayesian network on which parameter variation is being conducted should be expressed as a bn.fit object. The name of the node to be varied, its level and its parent's level should be specified. The parameter variation specified by the function is:

P ( node = value_node | parents = value_parents ) = new_value

and the probabilistic query of interest is:

P ( interest_node = interest_node_value | evidence_nodes = evidence_states )

References

Coupé, V. M., & Van Der Gaag, L. C. (2002). Properties of sensitivity analysis of Bayesian belief networks. Annals of Mathematics and Artificial Intelligence, 36(4), 323-356.

Leonelli, M., Goergen, C., & Smith, J. Q. (2017). Sensitivity analysis in multilinear probabilistic models. Information Sciences, 411, 84-97.

See also

covariation, sensquery