This function is DEPRECATED and will be replaced by impact().
global.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
global.impact() returns a list of class "global.impact" which contains:
a named vector containing the global strength impact for each node tested
a named vector containing the global strength estimate for the lower half
a named vector containing the global strength estimate for the upper half
Generates the global strength impact of each specified node. Global strength impact can be interpreted as the degree to which the level of a node impacts the overall connectivity of the network
For an explanation of impact functions in general, see impact.
Global strength is defined as the sum of the absolute value of all edges in the network, and is closely related to the concept of density (density is the sum of edges not accounting for absolute values). Global strength impact measures to what degree the global strength varies as a function of each node.