Description Usage Arguments Details Value References See Also Examples
The function calculates bootstrap confidence intervals for the parameters of network degree distribution: probabilities of node degrees f(k) and mean degree μ, where k = 0, 1, … are the degrees.
1 
x 
a list with bootstrapped results – output of 
prob 
confidence level for the intervals. Default is 0.95 (i.e., 95% confidence). 
method 
method for calculating the bootstrap intervals. Default is

Currently, the bootstrap intervals can be calculated with two alternative
methods: "percentile"
or "basic"
. The "percentile"
intervals correspond to Efron's 100\cdotprob
% intervals
\insertCite@see @efron_1979, also Equation 5.18 by @davison_hinkley_1997 and Equation 3 by @gel_etal_2017, @chen_etal_2018_snowbootsnowboot:
(θ^*_{[Bα/2]}, θ^*_{[B(1α/2)]}),
where θ^*_{[Bα/2]} and θ^*_{[B(1α/2)]}
are empirical quantiles of the bootstrap distribution with B
bootstrap
replications for parameter θ
(θ can be the f(k) or μ),
and α = 1  prob
.
The "basic"
method produces intervals
\insertCite@see Equation 5.6 by @davison_hinkley_1997snowboot:
(2\hat{θ}  θ^*_{[B(1α/2)]}, 2\hat{θ}  θ^*_{[Bα/2]}),
where \hat{θ} is the sample estimate of the parameter. Note that this method can lead to negative confidence bounds, especially when \hat{θ} is close to 0.
A list object of class "snowboot
" with the following elements:
fk_ci 
A matrix of dimensions 2 \times 
mu_ci 
A numeric vector of length 2 with lower and upper confidence bounds for the network mean degree μ. 
prob 
Confidence level for the intervals. 
method 
Method that was used for calculating the bootstrap intervals. 
fk 
A vector with an estimate of the degree distribution, copied
from the input 
mu 
An estimate of the mean degree, copied from the input 
1 2 3 4 5 6 7 
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