sensitivity {bnmonitor} | R Documentation |

## Sensitivity function

### Description

`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 |

`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 |

### 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`

)

### 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.

### References

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.

### See Also

*bnmonitor*version 0.1.4 Index]