scoreContribution {abn} | R Documentation |

## Compute the score's contribution in a network of each observation.

### Description

This function computes the score's contribution of each observation to the total network score.

### Usage

```
scoreContribution(object = NULL,
dag = NULL, data.df = NULL, data.dists = NULL,
verbose = FALSE)
```

### Arguments

`object` |
an object of class ' |

`dag` |
a matrix or a formula statement (see details) defining the network structure, a directed acyclic graph (DAG), see details for format. Note that colnames and rownames must be set. |

`data.df` |
a data frame containing the data used for learning the network, binary variables must be declared as factors and no missing values all allowed in any variable. |

`data.dists` |
a named list giving the distribution for each node in the network, see details. |

`verbose` |
if |

### Details

This function computes the score contribution of each observation
to the total network score.
This function is available only in the `mle`

settings.
To do so one uses the `glm`

and `predict`

functions.
This function is an attempt to perform diagnostic for an ABN analysis.

### Value

A named list that contains the scores contributions: maximum likelihood, aic, bic, mdl and diagonal values of the hat matrix.

### Examples

```
## Not run:
## Use a subset of a built-in simulated data set
mydat <- ex1.dag.data[,c("b1","g1","p1")]
## setup distribution list for each node
mydists <- list(b1="binomial", g1="gaussian", p1="poisson")
## now build cache
mycache <- buildScoreCache(data.df = mydat, data.dists = mydists, max.parents = 2, method = "mle")
## Find the globally best DAG
mp.dag <- mostProbable(score.cache=mycache, score="bic", verbose = FALSE)
out <- scoreContribution(object = mp.dag)
## Observations contribution per network node
boxplot(out$bic)
## End(Not run)
```

*abn*version 3.1.1 Index]