BparametersEst(outBootdeg)bootdegc(length(outBootdeg$num.sam),outBootdeg$n.boot,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.c(length(outBootdeg$num.sam), 3, outBootdeg$n.boot, 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.c(length(outBootdeg$num.sam), 5, outBootdeg$n.boot, 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.c(length(outBootdeg$num.sam), 9,
outBootdeg$n.boot, 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 from bootdeg.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.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.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
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)
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