impact.boot is DEPRECATED. The function will be removed in the next update. Use impact.NCT instead.
impact.boot(
input,
boots,
gamma,
nodes = c("all"),
binary.data = FALSE,
weighted = TRUE,
split = c("median", "mean", "forceEqual", "cutEqual", "quartiles"),
progressbar = TRUE
)a matrix or data frame of observations (not a network/edgelist).
See included example datasets depression and social.
the number of times to bootstrap the impact function
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
Logical. Should the pbar be plotted in order to see the progress of the estimation procedure? Defaults to TRUE.
impact.boot returns a list of class "impact.boot"
This function wraps the function impact and bootstraps to
provide confidence intervals of node impacts.
This method is computationally intensive. It is recommended that users test a subset of nodes
at a time using the nodes argument, rather than testing all nodes simultaneously.
impact.boot returns an object of class impact.boot, which includes confidence intervals.