net_by_accuracy() measures how well the nodes' reports of a cognitive
social structure agree with an ideal network, on average.
It is the mean of node_by_accuracy() over the nodes,
from 0 to 1 where every report is the ideal.
net_by_accuracy(.data, ideal = NULL, variant = c("hamming", "jaccard"))A network object of class stocnet, igraph, tbl_graph, network, or similar.
Internally any of these will be coerced to an efficient implementation.
For more information on possible coercions, see e.g. manynet::as_stocnet().
The ideal network to compare each report against, with the same nodes as the network. By default, the locally aggregated structure of the network itself.
Character string naming which variant of the measure to compute, where more than one definition of the same quantity is in use. The variant chosen is reported when the result is printed.
A network_measure numeric score.
The object also carries the measure it computed, the range its values
can fall within, and whether and how those values were normalized.
These are shown as a one-line header when the object is printed.
Where a measure offers a choice between several ways of counting the
same thing, it also carries the variant it used.
All can be retrieved with attr().
Other features:
measure_accuracy_node,
measure_features,
measure_fit,
motif_accuracy_node
Other measures:
measure_accuracy_node,
measure_assort_net,
measure_assort_node,
measure_breadth,
measure_broker_node,
measure_broker_tie,
measure_brokerage,
measure_central_between,
measure_central_close,
measure_central_degree,
measure_central_eigen,
measure_central_tie_between,
measure_central_tie_close,
measure_central_tie_degree,
measure_central_tie_eigen,
measure_closure,
measure_closure_node,
measure_cohesion,
measure_core,
measure_diffusion_infection,
measure_diffusion_net,
measure_diffusion_node,
measure_diverse_net,
measure_diverse_node,
measure_diverse_tie,
measure_features,
measure_fit,
measure_fragmentation,
measure_hierarchy,
measure_periods
net_by_accuracy(ison_hightech)
#> # Mean Hamming accuracy, normalized [0, 1]
#> [1] 0.803