This class contains a list of `catNetwork`

s together with some diagnostic metrics and information. `catNetworkEvaluate`

objects are created automatically as result of calling `cnEvaluate`

or one of the `cnSearch`

functions.

The class `catNetworkEvaluate`

is used to output the result of two functions: `cnEvaluate`

and `cnSearchSA`

.
The usage of it in the first case is explained next.
The complexity and log-likelihood of the networks listed in `nets`

slots are stored in
`complexity`

and `loglik`

slots.
Function `cnEvaluate`

and `cnCompare`

fills all the slots from `hamm`

to `markov.fn`

by comparing these networks with a given network.
See in the manual of `cnCompare`

function for description of different distance criteria.
By calling `cnPlot`

upon a `catNetworkEvaluate`

object, some relevant comparison information can be plotted.

When `catNetworkEvaluate`

is created by calling `cnSearchSA`

or `cnSearchSAcluster`

functions,
`complexity`

and `loglik`

contains the information not about the networks in the `nets`

list,
but about the optimal networks found during the stochastic search process.
Also, the slots from `hamm`

to `markov.fn`

are not used.

`numnodes`

:an

`integer`

, the number of nodes in the network.`numsamples`

:an

`integer`

, the sample size used for evaluation.`nets`

:a

`list`

of resultant networks.`complexity`

an

`integer`

vector, the network complexity.`loglik`

a

`numerical`

vector, the likelihood of the sample being evaluated.`hamm`

:an

`integer`

vector, the hamming distance between the parent matrices of the found networks and the original network.`hammexp`

:an

`integer`

vector, the hamming distance between the exponents of the parent matrices.`tp`

:an

`integer`

vector, the number of true positives directed edges.`fp`

:an

`integer`

vector, the number of false positives directed edges.`fn`

:an

`integer`

vector, the number of false negatives directed edges.`pr`

:a

`numeric`

vector, precision.`sp`

:a

`numeric`

vector, specificity.`sn`

:a

`numeric`

vector, sensitivity(recall).`fscore`

:a

`numeric`

vector, the F-score.`skel.tp`

:an

`integer`

vector, the number of true positives undirected edges.`skel.fp`

:an

`integer`

vector, the number of false positives undirected edges.`skel.fn`

:an

`integer`

vector, the number of false negatives undirected edges.`order.fp`

:an

`integer`

vector, the number of false positive order relations.`order.fn`

:an

`integer`

vector, the number of false negative order relations.`markov.fp`

:an

`integer`

vector, the number of false positive Markov pairs.`markov.fn`

:an

`integer`

vector, the number of false negative Markov pairs.`KLdist`

:a

`numerical`

vector, the KL distance, currently inactive.`time`

:a

`numerical`

, the processing time in seconds.

- cnFind
`signature(object="catNetworkEvaluate", complexity="integer")`

: Finds a network in the list`nets`

with specific complexity.- cnFindAIC
`signature(object="catNetworkEvaluate")`

: Finds the optimal network according to AIC criterion.- cnFindBIC
`signature(object="catNetworkEvaluate")`

: Finds the optimal network according to BIC criterion.- cnPlot
`signature(object="catNetworkEvaluate")`

: Draw distance plots.

N. Balov

`catNetwork-class`

, `catNetworkDistance-class`

, `cnCompare`

, `cnPlot`

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