bswar: Calculate Bootstrap Estimates and Confidence Intervals for...

In degreenet: Models for Skewed Count Distributions Relevant to Networks

Calculate Bootstrap Estimates and Confidence Intervals for the Waring Distribution

Description

Uses the parametric bootstrap to estimate the bias and confidence interval of the MLE of the Waring Distribution.

Usage

bswar(x, cutoff=1, m=200, np=2, alpha=0.95, v=NULL,
                   hellinger=FALSE)
bootstrapwar(x,cutoff=1,cutabove=1000,
             m=200,alpha=0.95,guess=c(3.31, 0.1),file="none",
             conc = FALSE)

Arguments

x	A vector of counts (one per observation).
cutoff	Calculate estimates conditional on exceeding this value.
m	Number of bootstrap samples to draw.
np	Number of parameters in the model (1 by default).
alpha	Type I error for the confidence interval.
v	Parameter value to use for the bootstrap distribution. By default it is the MLE of the data.
hellinger	Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood.
cutabove	Calculate estimates conditional on not exceeding this value.
guess	Guess at the parameter value.
file	Name of the file to store the results. By default do not save the results.
conc	Calculate the concentration index of the distribution?

Value

dist	matrix of sample CDFs, one per row.
obsmle	The Waring MLE of the PDF exponent.
bsmles	Vector of bootstrap MLE.
quantiles	Quantiles of the bootstrap MLEs.
pvalue	p-value of the Anderson-Darling statistics relative to the bootstrap MLEs.
obsmands	Observed Anderson-Darling Statistic.
meanmles	Mean of the bootstrap MLEs.
guess	Initial estimate at the MLE.
mle.meth	Method to use to compute the MLE.

Note

See the papers on https://handcock.github.io/?q=Holland for details

References

Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism for human sexual network formation," Proceedings of the Royal Society, B, 2003, 270, 1123-1128.

See Also

anbmle, simwar, llwar

Examples

# Now, simulate a Waring distribution over 100
# observations with expected count 1 and probability of another
# of 0.2

set.seed(1)
s4 <- simwar(n=100, v=c(5,0.2))
table(s4)

#
# Calculate the MLE and an asymptotic confidence
# interval for the parameter.
#

s4est <- awarmle(s4)
s4est

#
# Use the bootstrap to compute a confidence interval rather than using the 
# asymptotic confidence interval for the parameter.
#

bswar(s4, m=20)

degreenet documentation built on Sept. 26, 2024, 1:08 a.m.