snowboot (version 0.5.1)

Oempdegreedistrib: Obtaining an Empirical Network Degree Distribution from Labeled Snowball Sampling with Multiple Inclusion (LSMI).

Description

Oempdegreedistrib is used to obtain the empirical network degree distribution from labeled snowball sampling with multiple inclusion (LSMI).

Usage

Oempdegreedistrib(net, n.seeds, n.neigh, num.sam = 1, seeds = NULL)

Arguments

net

A network object that is list containing:

edges

The edgelist of the network. A two column matrix where each row is an edge.

degree

The degree sequence of the network, which is an integer vector of length n.

n

The network order.

The object can be created by local.network.MR.new5 or it can be imported.

n.seeds

A number of seeds in the snowball sample. It must be a positive integer.

n.neigh

A number of waves to be sampled around each seed in LSMI. For example, n.neigh = 0 corresponds to seeds only, and n.neigh = 1 corresponds to sampling seeds and their first neighbors). Note that the algorithm allows for multiple inclusions.

num.sam

A number for the LSMI repetitions. Default value is one.

seeds

A matrix of dimension num.sam x n.seeds containing the numeric ids of the seeds to initiate sampling. Each row of the matrix corresponds to one LSMI sample. Note that this is an optional parameter. WARNING: As of now, this feature is only supported when parameter n.neigh is greater than zero.

Value

A list consisting of

samples

A list of length num.sam where each element is a list containing three tables: the frequency of degrees sampled from seeds, non-seeds including duplicated nodes, and non-seeds without duplications.

values

A list of length num.sam where each element is a vector containing the unique degree values sampled in each LSMI.

Oemp

A list of length num.sam where each element is a list containing two tables based on different methods to estimating the empirical distribution from the network sample (One list per LSMI).

num.sam

A number for the LSMI repetitions.

val.seeds

A list of length num.sam where each element is a vector of unique degree values sampled solely from seeds (One vector per LSMI).

val.nonseed

a list of length num.sam where each element is a vector of unique degree values sampled solely from non-seeds (One vector per LSMI). Note: This item is unreported when n.neigh equals zero.

n.seeds

the number of seeds in the snowball sample.

n.neigh

the number of waves carried out by the snowball sample. See input argument for details.

p0.real

proportion of nodes from the network with degree zero. Note: p0.real is unreported when n.neigh equals zero.

p0.seeds

a list of length num.sam where each element is the proportion of seeds with degree zero. (One element per LSMI).

ekseed

a list of length num.sam where each element is the sample mean of the seeds. Note that This is unreported when n.neigh equals zero.

seeds1

a matrix of dimension num.sam x n.seeds with the numeric seed ids. Each row corresponds to one LSMI.

nodes_of_LSMI

a list of length num.sam where each element is vector containing the numeric ids of the nodes sampled using LSMI (One element per LSMI). Note: nodes_of_LSMI is unreported when n.neigh equals zero.

References

Thompson, M. E., Ramirez Ramirez, L. L., Lyubchich, V. and Gel, Y. R. (2015), Using the bootstrap for statistical inference on random graphs. Can J Statistics. doi: 10.1002/cjs.11271

Examples

net <- artificial_networks[[1]]
sam.out <- Oempdegreedistrib(net = net, n.seeds = 40, n.neigh = 1, num.sam = 1)