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 cnSearch functions.
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.
complexityan integer vector, the network complexity.
loglika 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.
signature(object="catNetworkEvaluate", complexity="integer"):
Finds a network in the list nets with 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.
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.
catNetwork-class, catNetworkDistance-class, cnCompare, cnPlot