Bootstrapping convenience function for impact statistics
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)input |
a matrix or data frame of observations (not a network/edgelist).
See included example datasets |
boots |
the number of times to bootstrap the impact function |
gamma |
the sparsity parameter used in generating networks. Defaults to 0.5 for interval data and 0.25 for binary data |
nodes |
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)). |
binary.data |
logical. Indicates whether the input data is binary |
weighted |
logical. Indicates whether resultant networks preserve edge weights or binarize edges. |
split |
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 |
progressbar |
Logical. Should the pbar be plotted in order to see the progress of the estimation procedure? Defaults to TRUE. |
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.
impact.boot returns a list of class "impact.boot"
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