sensitivity {bnmonitor} | R Documentation |
sensitivity
returns the sensitivity function for a probabilistic query of interest with respect to a parameter change defined by the user.
sensitivity( bnfit, interest_node, interest_node_value, evidence_nodes = NULL, evidence_states = NULL, node, value_node, value_parents, new_value, covariation = "proportional" )
bnfit |
object of class |
interest_node |
character string. Node of the probability query of interest. |
interest_node_value |
character string. Level of |
evidence_nodes |
character string. Evidence nodes. If |
evidence_states |
character string. Levels of |
node |
character string. Node of which the conditional probability distribution is being changed. |
value_node |
character string. Level of |
value_parents |
character string. Levels of |
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. |
covariation |
character string. Co-variation scheme to be used for the updated Bayesian network. Can take values |
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
)
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.
CoupĂ©, 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.