lsmi_union: Snowball Sampling with Multiple Inclusions around Several...
In Nezafati/snowboot: Bootstrap Methods for Network Inference

Description Usage Arguments Details Value References See Also Examples

Obtain one big LSMI – with max(n.seeds) seeds and n.wave waves around each – and subsample seeds to create smaller LSMIs (with less seeds and/or waves). The function is primarily used in cross-validation.

1	lsmi_union(net, n.seeds, n.wave, seeds = NULL)

`net`	a network object that is a list containing: `degree` the degree sequence of the network, which is an `integer` vector of length n; `edges` the edgelist, which is a two-column matrix, where each row is an edge of the network; `n` the network order (i.e., number of nodes in the network). The network object can be simulated by `random_network`, selected from the networks available in `artificial_networks`, converged from an `igraph` object with `igraph_to_network`, etc.
`n.seeds`	an integer vector of numbers of seeds for snowball sampling (cf. a single integer `n.seed` in `lsmi`). Only `n.seeds <= n` are retained. If `seeds` is specified, only values `n.seeds < length(unique(seeds))` are retained and automatically supplemented by `length(unique(seeds))`.
`n.wave`	an integer defining the number of waves (order of the neighborhood) to be recorded around the seed in the LSMI. For example, `n.wave = 1` corresponds to an LSMI with the seed and its first neighbors. Note that the algorithm allows for multiple inclusions.
`seeds`	a vector of numeric IDs of pre-specified seeds. If specified, LSMIs are constructed around each such seed.

Note that the produced LSMIs are slightly different from those described by \insertCitegel_etal_2017;textualsnowboot. The current R implementation produces smaller LSMIs by subsetting the seeds, not by new sampling of seeds from the network and growing completely new LSMIs, as it was done by \insertCitegel_etal_2017;textualsnowboot. See the details in Figure 3 by \insertCitechen_etal_2018_snowboot;textualsnowboot

A list with two elements:

`lsmi_big`	LSMI with `max(n.seeds)` seeds (see the argument definition above) and `n.wave` waves produced by the `lsmi` function.
`sequence_seeds`	A list of length equal to `length(n.seeds)`; each element of the list is a random subset of the seeds' IDs, starting from the largest (a set of size `max(n.seeds)`) to the smallest (a set of size `min(n.seeds)`).