dyadic {Blaunet} | R Documentation |
Computes six dyadic measures (computed for each edge in the edgelist). They are Euclidean Distance, Mahalanobis Distance, Co-nichers, Co-outsiders, Straddlers, Spanners.
dyadic(blauObj, dev.range, ecologies.off = FALSE, m.dist = TRUE)
blauObj |
An object of class blau initialized with the function |
dev.range |
When creating niches, indicates standard deviation around the mean in each dimension to include in niche. A larger value will make niches larger and therefore include more individuals. |
ecologies.off |
Defaults to |
m.dist |
Defaults to |
All measures are at the edge-level of analysis and characterize ties/dyads rather than individuals/nodes.
Euclidean Distance: is the euclidean distance between two nodes in the edge
Mahalanobis Distance: is euclidean distance standardized by the unit of measurement
Co-nichers: are ties that are located within the same niche. A 0 indicates that both nodes of the tie do not share any niche in common. A 1 indicates that both nodes of the tie belong to only one common niche. A number of 2 or more indicates that the nodes of the tie share more than 1 common niche.
Co-outsiders: is an indicator variable that denotes ties where both nodes do not belong to any niche.
Straddlers: is an indicator variable that denotes ties where one node belongs to at least one niche and the other node does not belong to any niche.
Spanners: is an indicator variable that denotes ties where one node belongs to one niche and the other node belongs to a different niche.
Returns a matrix stored in object$dyadic that contains eight columns. The first two contain the ego and alter for the edge, and the next six contain the dyadic measures.
data(BSANet)
square.data <- BSANet$square.data
el <- BSANet$el #edgelist
adj <- BSANet$adj #adjacency matrix
b <- blau(square.data, node.ids = 'person', ecology.ids = 'city', graph = el)
#implicitly a directed edgelist
#will automatically compute niches
b <- dyadic(b, dev.range = rep(1.5, 3)) # 3 is the number of dimensions
#adjacency matricies are also OK
b <- blau(square.data, node.ids = 'person', ecology.ids = 'city', graph = adj)
#will automatically compute niches
b <- dyadic(b, dev.range = rep(1.5, 3)) # 3 is the number of dimensions