snowboot (version 0.5.0)

BparametersEst: Summary of the Bootstrap Degree Distribution

Description

This function provides summary statistics of a bootstrap degree distribution.

Usage

BparametersEst(outBootdeg)

Arguments

outBootdeg

A list that is the output of bootdeg

Value

• A list consisting of:
• mean
  An array of dimension length(outBootdeg$num.sam) x outBootdeg$n.boot x 3. The last dimension, of 3, is for the three different methods of obtaining the empirical degree distribution from outBootdeg$empd (see output empd from bootdeg for details). The (i,j,k)-th element in the array is an estimate of mean degree for the i-th LSMI sample, j-th bootstrap replication, and k-th empirical distribution from outBootdeg$empd.
• quartiles
  An array of dimension length(outBootdeg$num.sam) x 3 x outBootdeg$n.boot x 3. The last dimension, of 3, is for the three different methods of estimation from outBootdeg$empd (see output empd from bootdeg for details). The second dimension, of 3, corresponds to the quartiles (.25, .5, .75). The (i,j,k,l)-th element in the array is an estimate of j-th quartile for the i-th LSMI sample, k-th bootstrap replication, and l-th empirical distribution from outBootdeg$empd.
• rfreq
  An array of dimension length(outBootdeg$num.sam) x 5 x outBootdeg$n.boot x 3. The last dimension, of 3, is for the three different methods of estimation from outBootdeg$empd (see output empd from bootdeg for details.). The second dimension, of 5, corresponds to degree values: 0, 1, 2, 3, 4. The (i,j,k,l)-th element in the array is the proportion of nodes with degree j in the i-th LSMI sample, k-th bootstrap replication, and l-th empirical distribution from outBootdeg$empd.
• deciles
  An array of dimension length(outBootdeg$num.sam) x 9 x outBootdeg$n.boot x 3. The last dimension, of 3, is for the three different methods of estimation from outBootdeg$empd (see output empd from bootdeg for details.). The second dimension, of 9, corresponds to the deciles (.1, .2, ... , .9). The (i,j,k,l)-th element in the array is an estimate of j-th decile for the i-th LSMI sample, k-th bootstrap replication, and l-th empirical distribution from outBootdeg$empd.
• num.sam
  Numeric indices corresponding to LSMI samples used for bootstrap. See value num.sam from bootdeg
• seeds1
  A matrix of dimension length(num.sam) x n.seeds with the numeric seed ids. Each row corresponds to one LSMI. The rows are present in the same order as the ids in num.sam. See value seeds1 from bootdeg
• nodes_of_LSMI
  A list of length length(num.sam) where each element is vector containing the numeric ids of the nodes sampled using the respective LSMI. The elements are present in the same order as the ids in num.sam. Note: nodes_of_LSMI is unreported when n.neigh equals zero. See value nodes_of_LSMI from bootdeg

References

Efron, B. (1979). Bootstrap methods: another look at the jackknife. The annals of Statistics, 1-26.

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)
outBootdeg <- bootdeg(sam.out = sam.out, n.boot = 50)
a <- BparametersEst(outBootdeg)