bsdp {degreenet} | R Documentation |
Uses the parametric bootstrap to estimate the bias and confidence interval of the MLE of the Discrete Pareto Distribution.
bsdp(x, cutoff=1, m=200, np=1, alpha=0.95) bootstrapdp(x,cutoff=1,cutabove=1000, m=200,alpha=0.95,guess=3.31,hellinger=FALSE, mle.meth="adpmle")
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. |
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 |
Initial estimate at the MLE. |
mle.meth |
Method to use to compute the MLE. |
dist |
matrix of sample CDFs, one per row. |
obsmle |
The Discrete Pareto 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. |
See the working papers on http://www.csss.washington.edu/Papers for details
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.
anbmle, simdp, lldp
## Not run: # Now, simulate a Discrete Pareto distribution over 100 # observations with expected count 1 and probability of another # of 0.2 set.seed(1) s4 <- simdp(n=100, v=3.31) table(s4) # # Calculate the MLE and an asymptotic confidence # interval for the parameter. # s4est <- adpmle(s4) s4est # # Use the bootstrap to compute a confidence interval rather than using the # asymptotic confidence interval for the parameter. # bsdp(s4, m=20) ## End(Not run)