Classes "catNetworkEvaluate" and "dagEvaluate"
This class contains a list of
catNetworks together with some diagnostic metrics and information.
catNetworkEvaluate objects are created automatically as result of calling
cnEvaluate or one of the
catNetworkEvaluate is used to output the result of two functions:
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
cnCompare fills all the slots from
by comparing these networks with a given network.
See in the manual of
cnCompare function for description of different distance criteria.
cnPlot upon a
catNetworkEvaluate object, some relevant comparison information can be plotted.
catNetworkEvaluate is created by calling
loglik contains the information not about the networks in the
but about the optimal networks found during the stochastic search process.
Also, the slots from
markov.fn are not used.
integer, the number of nodes in the network.
integer, the sample size used for evaluation.
listof resultant networks.
integervector, the network complexity.
numericalvector, the likelihood of the sample being evaluated.
integervector, the hamming distance between the parent matrices of the found networks and the original network.
integervector, the hamming distance between the exponents of the parent matrices.
integervector, the number of true positives directed edges.
integervector, the number of false positives directed edges.
integervector, the number of false negatives directed edges.
numericvector, the F-score.
integervector, the number of true positives undirected edges.
integervector, the number of false positives undirected edges.
integervector, the number of false negatives undirected edges.
integervector, the number of false positive order relations.
integervector, the number of false negative order relations.
integervector, the number of false positive Markov pairs.
integervector, the number of false negative Markov pairs.
numericalvector, the KL distance, currently inactive.
numerical, the processing time in seconds.
signature(object="catNetworkEvaluate", complexity="integer"): Finds a network in the list
netswith specific complexity.
signature(object="catNetworkEvaluate"): Finds the optimal network according to AIC criterion.
signature(object="catNetworkEvaluate"): Finds the optimal network according to BIC criterion.
signature(object="catNetworkEvaluate"): Draw distance plots.
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