This function is DEPRECATED and will be replaced by impact().
edge.impact(input, gamma, nodes = c("all"), binary.data = FALSE,
weighted = TRUE, split = c("median", "mean", "forceEqual",
"cutEqual", "quartiles"))a matrix or data frame of observations (not a network/edgelist).
See included example datasets depression and social.
the sparsity parameter used in generating networks. Defaults to 0.5 for interval data and 0.25 for binary data
indicates which nodes should be tested. Can be given as a character string of desired nodes (e.g., c("node1","node2")) or as a numeric vector of column numbers (e.g., c(1,2)).
logical. Indicates whether the input data is binary
logical. Indicates whether resultant networks preserve edge weights or binarize edges.
method by which to split network given non-binary data. "median": median split (excluding the median), "mean": mean split, "forceEqual": creates equally sized groups by partitioning random median observations to the smaller group, "cutEqual": creates equally sized groups by deleting random values from the bigger group,"quartile": uses the top and bottom quartile as groups
edge.impact() returns a list of class "edge.impact" which contains:
a list of matrices. Each symmetric matrix contains the edge impacts for the given node
a list of matrices. Each symmetric matrix contains the edge estimates for the given node's lower half
a list of matrices. Each symmetric matrix contains the edge estimates for the given node's upper half
a list of dataframes. Each dataframe contains an edgelist of edge impacts
Generates a matrix of edge impacts for each specified node. Each scalar in a given matrix represents the degree to which the level of a node impacts the strength of a specified edge in the network
For an explanation of impact functions in general, see impact.
Edge impact is the change in an edge's value as a function of a given node. A separate edge impact value is calculated for each edge in the network.
It is highly useful to plot the edge impacts as if they were a network. Positive edges in the resultant graph can be interpreted as edges that were made more positive by the given node, and negative edges can be interpreted as edges that were made more negative by the given node.
The $hi and $lo output of edge.impact can also be used to quickly visualize
the difference in network structure depending on node level (see examples).