covariation_matrix {bnmonitor} | R Documentation |

## Co-variation matrices

### Description

Construction of model-preserving co-variation matrices for objects of class `CI`

.

### Usage

```
total_covar_matrix(ci, entry, delta)
col_covar_matrix(ci, entry, delta)
partial_covar_matrix(ci, entry, delta)
row_covar_matrix(ci, entry, delta)
```

### Arguments

`ci` |
object of class |

`entry` |
a vector of length two specifying the entry of the covariance matrix to vary. |

`delta` |
multiplicative variation coefficient for the entry of the covariance matrix given in |

### Details

Functions to compute total, partial, row-based and column-based co-variation matrices to ensure the conditional independences of the original Bayesian network hold after a variation. If no co-variation is required for model-preservation the functions return a matrix filled with ones (no co-variation).

### Value

A co-variation matrix of the same size of the covariance matrix of `CI`

.

### References

C. GĂ¶rgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.

### See Also

### Examples

```
total_covar_matrix(synthetic_ci,c(1,1),0.3)
total_covar_matrix(synthetic_ci,c(1,2),0.3)
partial_covar_matrix(synthetic_ci,c(1,2),0.3)
row_covar_matrix(synthetic_ci,c(1,2),0.3)
col_covar_matrix(synthetic_ci,c(1,2),0.3)
```

*bnmonitor*version 0.1.4 Index]